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Journal articles on the topic 'Coevolutionary domains'
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Krepel, Dana, Ryan R. Cheng, Michele Di Pierro, and José N. Onuchic. "Deciphering the structure of the condensin protein complex." Proceedings of the National Academy of Sciences 115, no. 47 (November 1, 2018): 11911–16. http://dx.doi.org/10.1073/pnas.1812770115.
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Protein assemblies consisting of structural maintenance of chromosomes (SMC) and kleisin subunits are essential for the process of chromosome segregation across all domains of life. Prokaryotic condensin belonging to this class of protein complexes is composed of a homodimer of SMC that associates with a kleisin protein subunit called ScpA. While limited structural data exist for the proteins that comprise the (SMC)–kleisin complex, the complete structure of the entire complex remains unknown. Using an integrative approach combining both crystallographic data and coevolutionary information, we predict an atomic-scale structure of the whole condensin complex, which our results indicate being composed of a single ring. Coupling coevolutionary information with molecular-dynamics simulations, we study the interaction surfaces between the subunits and examine the plausibility of alternative stoichiometries of the complex. Our analysis also reveals several additional configurational states of the condensin hinge domain and the SMC–kleisin interaction domains, which are likely involved with the functional opening and closing of the condensin ring. This study provides the foundation for future investigations of the structure–function relationship of the various SMC–kleisin protein complexes at atomic resolution.
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Croce, Giancarlo, Thomas Gueudré, Maria Virginia Ruiz Cuevas, Victoria Keidel, Matteo Figliuzzi, Hendrik Szurmant, and Martin Weigt. "A multi-scale coevolutionary approach to predict interactions between protein domains." PLOS Computational Biology 15, no. 10 (October 21, 2019): e1006891. http://dx.doi.org/10.1371/journal.pcbi.1006891.
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Cartlidge, John, and Seth Bullock. "Combating Coevolutionary Disengagement by Reducing Parasite Virulence." Evolutionary Computation 12, no. 2 (June 2004): 193–222. http://dx.doi.org/10.1162/106365604773955148.
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While standard evolutionary algorithms employ a static, absolute fitness metric, co-evolutionary algorithms assess individuals by their performance relative to populations of opponents that are themselves evolving. Although this arrangement offers the possibility of avoiding long-standing difficulties such as premature convergence, it suffers from its own unique problems, cycling, over-focusing and disengagement. Here, we introduce a novel technique for dealing with the third and least explored of these problems. Inspired by studies of natural host-parasite systems, we show that disengagement can be avoided by selecting for individuals that exhibit reduced levels of “virulence”, rather than maximum ability to defeat coevolutionary adversaries. Experiments in both simple and complex domains are used to explain how this counterintuitive approach may be used to improve the success of coevolutionary algorithms.
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Zheng, Wei, Xiaogen Zhou, Qiqige Wuyun, Robin Pearce, Yang Li, and Yang Zhang. "FUpred: detecting protein domains through deep-learning-based contact map prediction." Bioinformatics 36, no. 12 (March 30, 2020): 3749–57. http://dx.doi.org/10.1093/bioinformatics/btaa217.
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Abstract Motivation Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence. Results We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew’s correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins. Availability and implementation https://zhanglab.ccmb.med.umich.edu/FUpred. Supplementary information Supplementary data are available at Bioinformatics online.
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Xue, Xingsi, Jie Chen, Junfeng Chen, and Dongxu Chen. "Using Compact Coevolutionary Algorithm for Matching Biomedical Ontologies." Computational Intelligence and Neuroscience 2018 (October 8, 2018): 1–8. http://dx.doi.org/10.1155/2018/2309587.
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Over the recent years, ontologies are widely used in various domains such as medical records annotation, medical knowledge representation and sharing, clinical guideline management, and medical decision-making. To implement the cooperation between intelligent applications based on biomedical ontologies, it is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Although Evolutionary algorithms (EAs) are one of the state-of-the-art methodologies to match the heterogeneous ontologies, huge memory consumption, long runtime, and the bias improvement of the solutions hamper them from efficiently matching biomedical ontologies. To overcome these shortcomings, we propose a compact CoEvolutionary Algorithm to efficiently match the biomedical ontologies. Particularly, a compact EA with local search strategy is able to save the memory consumption and runtime, and three subswarms with different optimal objectives can help one another to avoid the solution’s bias improvement. In the experiment, two famous testing cases provided by Ontology Alignment Evaluation Initiative (OAEI 2017), i.e. anatomy track and large biomed track, are utilized to test our approach’s performance. The experimental results show the effectiveness of our proposal.
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Solan, Ron, Joana Pereira, Andrei N. Lupas, Rachel Kolodny, and Nir Ben-Tal. "Gram-negative outer-membrane proteins with multiple β-barrel domains." Proceedings of the National Academy of Sciences 118, no. 31 (July 30, 2021): e2104059118. http://dx.doi.org/10.1073/pnas.2104059118.
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Outer-membrane beta barrels (OMBBs) are found in the outer membrane of gram-negative bacteria and eukaryotic organelles. OMBBs fold as antiparallel β-sheets that close onto themselves, forming pores that traverse the membrane. Currently known structures include only one barrel, of 8 to 36 strands, per chain. The lack of multi-OMBB chains is surprising, as most OMBBs form oligomers, and some function only in this state. Using a combination of sensitive sequence comparison methods and coevolutionary analysis tools, we identify many proteins combining multiple beta barrels within a single chain; combinations that include eight-stranded barrels prevail. These multibarrels seem to be the result of independent, lineage-specific fusion and amplification events. The absence of multibarrels that are universally conserved in bacteria with an outer membrane, coupled with their frequent de novo genesis, suggests that their functions are not essential but rather beneficial in specific environments. Adjacent barrels of complementary function within the same chain may allow for functions beyond those of the individual barrels.
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Donges, Jonathan F., Wolfgang Lucht, Finn Müller-Hansen, and Will Steffen. "The technosphere in Earth System analysis: A coevolutionary perspective." Anthropocene Review 4, no. 1 (February 1, 2017): 23–33. http://dx.doi.org/10.1177/2053019616676608.
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Earth System analysis is the study of the joint dynamics of biogeophysical, social and technological processes on our planet. To advance our understanding of possible future development pathways and identify management options for navigating to safe operating spaces while avoiding undesirable domains, computer models of the Earth System are developed and applied. These models hardly represent dynamical properties of technological processes despite their great planetary-scale influence on the biogeophysical components of the Earth System and the associated risks for human societies posed, e.g. by climatic change or novel entities. In this contribution, we reflect on the technosphere from the perspective of Earth System analysis with a threefold focus on agency, networks and complex coevolutionary dynamics. First, we argue that Haff’s conception of the technosphere takes an extreme position in implying a strongly constrained human agency in the Earth System. Assuming that the technosphere develops according to dynamics largely independently of human intentions, Haff’s perspective appears incompatible with a humanistic view that underlies the sustainability discourse at large and, more specifically, current frameworks such as UN sustainable development goals and the safe and just operating space for humanity. Second, as an alternative to Haff’s static three-stratum picture, we propose complex adaptive networks as a concept for describing the interplay of social agents and technospheric entities and their emergent dynamics for Earth System analysis. Third, we argue that following a coevolutionary approach in conceptualising and modelling technospheric dynamics, also including the socio-cultural and biophysical spheres of the Earth System, could resolve the apparent conflict between the discourses on sustainability and the technosphere. Hence, this coevolutionary approach may point the way forward in modelling technological influences in the Earth System and may lead to a considerably deeper understanding of pathways to sustainable development in the future.
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Granata, Daniele, Luca Ponzoni, Cristian Micheletti, and Vincenzo Carnevale. "Patterns of coevolving amino acids unveil structural and dynamical domains." Proceedings of the National Academy of Sciences 114, no. 50 (November 28, 2017): E10612—E10621. http://dx.doi.org/10.1073/pnas.1712021114.
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Patterns of interacting amino acids are so preserved within protein families that the sole analysis of evolutionary comutations can identify pairs of contacting residues. It is also known that evolution conserves functional dynamics, i.e., the concerted motion or displacement of large protein regions or domains. Is it, therefore, possible to use a pure sequence-based analysis to identify these dynamical domains? To address this question, we introduce here a general coevolutionary coupling analysis strategy and apply it to a curated sequence database of hundreds of protein families. For most families, the sequence-based method partitions amino acids into a few clusters. When viewed in the context of the native structure, these clusters have the signature characteristics of viable protein domains: They are spatially separated but individually compact. They have a direct functional bearing too, as shown for various reference cases. We conclude that even large-scale structural and functionally related properties can be recovered from inference methods applied to evolutionary-related sequences. The method introduced here is available as a software package and web server (spectrus.sissa.it/spectrus-evo_webserver).
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García, Enol, José R. Villar, Qing Tan, Javier Sedano, and Camelia Chira. "An efficient multi-robot path planning solution using A* and coevolutionary algorithms." Integrated Computer-Aided Engineering 30, no. 1 (November 24, 2022): 41–52. http://dx.doi.org/10.3233/ica-220695.
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Multi-robot path planning has evolved from research to real applications in warehouses and other domains; the knowledge on this topic is reflected in the large amount of related research published in recent years on international journals. The main focus of existing research relates to the generation of efficient routes, relying the collision detection to the local sensory system and creating a solution based on local search methods. This approach implies the robots having a good sensory system and also the computation capabilities to take decisions on the fly. In some controlled environments, such as virtual labs or industrial plants, these restrictions overtake the actual needs as simpler robots are sufficient. Therefore, the multi-robot path planning must solve the collisions beforehand. This study focuses on the generation of efficient collision-free multi-robot path planning solutions for such controlled environments, extending our previous research. The proposal combines the optimization capabilities of the A* algorithm with the search capabilities of co-evolutionary algorithms. The outcome is a set of routes, either from A* or from the co-evolutionary process, that are collision-free; this set is generated in real-time and makes its implementation on edge-computing devices feasible. Although further research is needed to reduce the computational time, the computational experiments performed in this study confirm a good performance of the proposed approach in solving complex cases where well-known alternatives, such as M* or WHCA, fail in finding suitable solutions.
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Dharna, Aaron, Julian Togelius, and L. B. Soros. "Co-Generation of Game Levels and Game-Playing Agents." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (October 1, 2020): 203–9. http://dx.doi.org/10.1609/aiide.v16i1.7431.
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Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.
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Gopnik, Alison, Shaun O’Grady, Christopher G. Lucas, Thomas L. Griffiths, Adrienne Wente, Sophie Bridgers, Rosie Aboody, Hoki Fung, and Ronald E. Dahl. "Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood." Proceedings of the National Academy of Sciences 114, no. 30 (July 24, 2017): 7892–99. http://dx.doi.org/10.1073/pnas.1700811114.
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How was the evolution of our unique biological life history related to distinctive human developments in cognition and culture? We suggest that the extended human childhood and adolescence allows a balance between exploration and exploitation, between wider and narrower hypothesis search, and between innovation and imitation in cultural learning. In particular, different developmental periods may be associated with different learning strategies. This relation between biology and culture was probably coevolutionary and bidirectional: life-history changes allowed changes in learning, which in turn both allowed and rewarded extended life histories. In two studies, we test how easily people learn an unusual physical or social causal relation from a pattern of evidence. We track the development of this ability from early childhood through adolescence and adulthood. In the physical domain, preschoolers, counterintuitively, perform better than school-aged children, who in turn perform better than adolescents and adults. As they grow older learners are less flexible: they are less likely to adopt an initially unfamiliar hypothesis that is consistent with new evidence. Instead, learners prefer a familiar hypothesis that is less consistent with the evidence. In the social domain, both preschoolers and adolescents are actually the most flexible learners, adopting an unusual hypothesis more easily than either 6-y-olds or adults. There may be important developmental transitions in flexibility at the entry into middle childhood and in adolescence, which differ across domains.
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Jaśkowski, Wojciech, Paweł Liskowski, Marcin Szubert, and Krzysztof Krawiec. "The performance profile: A multi–criteria performance evaluation method for test–based problems." International Journal of Applied Mathematics and Computer Science 26, no. 1 (March 1, 2016): 215–29. http://dx.doi.org/10.1515/amcs-2016-0015.
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Abstract In test-based problems, solutions produced by search algorithms are typically assessed using average outcomes of interactions with multiple tests. This aggregation leads to information loss, which can render different solutions apparently indifferent and hinder comparison of search algorithms. In this paper we introduce the performance profile, a generic, domain-independent, multi-criteria performance evaluation method that mitigates this problem by characterizing the performance of a solution by a vector of outcomes of interactions with tests of various difficulty. To demonstrate the usefulness of this gauge, we employ it to analyze the behavior of Othello and Iterated Prisoner’s Dilemma players produced by five (co)evolutionary algorithms as well as players known from previous publications. Performance profiles reveal interesting differences between the players, which escape the attention of the scalar performance measure of the expected utility. In particular, they allow us to observe that evolution with random sampling produces players coping well against the mediocre opponents, while the coevolutionary and temporal difference learning strategies play better against the high-grade opponents. We postulate that performance profiles improve our understanding of characteristics of search algorithms applied to arbitrary test-based problems, and can prospectively help design better methods for interactive domains.
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Verkhivker, Gennady. "Coevolution, Dynamics and Allostery Conspire in Shaping Cooperative Binding and Signal Transmission of the SARS-CoV-2 Spike Protein with Human Angiotensin-Converting Enzyme 2." International Journal of Molecular Sciences 21, no. 21 (November 4, 2020): 8268. http://dx.doi.org/10.3390/ijms21218268.
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Binding to the host receptor is a critical initial step for the coronavirus SARS-CoV-2 spike protein to enter into target cells and trigger virus transmission. A detailed dynamic and energetic view of the binding mechanisms underlying virus entry is not fully understood and the consensus around the molecular origins behind binding preferences of SARS-CoV-2 for binding with the angiotensin-converting enzyme 2 (ACE2) host receptor is yet to be established. In this work, we performed a comprehensive computational investigation in which sequence analysis and modeling of coevolutionary networks are combined with atomistic molecular simulations and comparative binding free energy analysis of the SARS-CoV and SARS-CoV-2 spike protein receptor binding domains with the ACE2 host receptor. Different from other computational studies, we systematically examine the molecular and energetic determinants of the binding mechanisms between SARS-CoV-2 and ACE2 proteins through the lens of coevolution, conformational dynamics, and allosteric interactions that conspire to drive binding interactions and signal transmission. Conformational dynamics analysis revealed the important differences in mobility of the binding interfaces for the SARS-CoV-2 spike protein that are not confined to several binding hotspots, but instead are broadly distributed across many interface residues. Through coevolutionary network analysis and dynamics-based alanine scanning, we established linkages between the binding energy hotspots and potential regulators and carriers of signal communication in the virus–host receptor complexes. The results of this study detailed a binding mechanism in which the energetics of the SARS-CoV-2 association with ACE2 may be determined by cumulative changes of a number of residues distributed across the entire binding interface. The central findings of this study are consistent with structural and biochemical data and highlight drug discovery challenges of inhibiting large and adaptive protein–protein interfaces responsible for virus entry and infection transmission.
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Henrich, Joseph, and Michael Muthukrishna. "The Origins and Psychology of Human Cooperation." Annual Review of Psychology 72, no. 1 (January 4, 2021): 207–40. http://dx.doi.org/10.1146/annurev-psych-081920-042106.
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Humans are an ultrasocial species. This sociality, however, cannot be fully explained by the canonical approaches found in evolutionary biology, psychology, or economics. Understanding our unique social psychology requires accounting not only for the breadth and intensity of human cooperation but also for the variation found across societies, over history, and among behavioral domains. Here, we introduce an expanded evolutionary approach that considers how genetic and cultural evolution, and their interaction, may have shaped both the reliably developing features of our minds and the well-documented differences in cultural psychologies around the globe. We review the major evolutionary mechanisms that have been proposed to explain human cooperation, including kinship, reciprocity, reputation, signaling, and punishment; we discuss key culture–gene coevolutionary hypotheses, such as those surrounding self-domestication and norm psychology; and we consider the role of religions and marriage systems. Empirically, we synthesize experimental and observational evidence from studies of children and adults from diverse societies with research among nonhuman primates.
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Xiao, Qingjie, Mengxue Xu, Weiwei Wang, Tingting Wu, Weizhe Zhang, Wenming Qin, and Bo Sun. "Utilization of AlphaFold2 to Predict MFS Protein Conformations after Selective Mutation." International Journal of Molecular Sciences 23, no. 13 (June 29, 2022): 7235. http://dx.doi.org/10.3390/ijms23137235.
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The major facilitator superfamily (MFS) is the largest secondary transporter family and is responsible for transporting a broad range of substrates across the biomembrane. These proteins are involved in a series of conformational changes during substrate transport. To decipher the transport mechanism, it is necessary to obtain structures of these different conformations. At present, great progress has been made in predicting protein structure based on coevolutionary information. In this study, AlphaFold2 was used to predict different conformational structures for 69 MFS transporters of E. coli after the selective mutation of residues at the interface between the N- and C-terminal domains. The predicted structures for these mutants had small RMSD values when compared to structures obtained using X-ray crystallography, which indicates that AlphaFold2 predicts the structure of MSF transporters with high accuracy. In addition, different conformations of other transporter family proteins have been successfully predicted based on mutation methods. This study provides a structural basis to study the transporting mechanism of the MFS transporters and a method to probe dynamic conformation changes of transporter family proteins when performing their function.
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Meng, Lingyan, Xiaomeng Li, Yue Hou, Yaxuan Li, and Yingkao Hu. "Functional conservation and divergence in plant-specific GRF gene family revealed by sequences and expression analysis." Open Life Sciences 17, no. 1 (January 1, 2022): 155–71. http://dx.doi.org/10.1515/biol-2022-0018.
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Abstract Unique to plants, growth regulatory factors (GRFs) play important roles in plant growth and reproduction. This study investigated the evolutionary and functional characteristics associated with plant growth. Using genome-wide analysis of 15 plant species, 173 members of the GRF family were identified and phylogenetically categorized into six groups. All members contained WRC and QLQ conserved domains, and the family’s expansion largely depended on segmental duplication. The promoter region of the GRF gene family mainly contained four types of cis-acting elements (light-responsive elements, development-related elements, hormone-responsive elements, and environmental stress-related elements) that are mainly related to gene expression levels. Functional divergence analysis revealed that changes in amino acid site evolution rate played a major role in the differentiation of the GRF gene family, with ten significant sites identified. Six significant sites were identified for positive selection. Moreover, the four groups of coevolutionary sites identified may play a key role in regulating the transcriptional activation of the GRF protein. Expression profiles revealed that GRF genes were generally highly expressed in young plant tissues and had tissue or organ expression specificity, demonstrating their functional conservation with distinct divergence. The results of these sequence and expression analyses are expected to provide molecular evolutionary and functional references for the plant GRF gene family.
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Krepel, Dana, Aram Davtyan, Nicholas P. Schafer, Peter G. Wolynes, and José N. Onuchic. "Braiding topology and the energy landscape of chromosome organization proteins." Proceedings of the National Academy of Sciences 117, no. 3 (December 30, 2019): 1468–77. http://dx.doi.org/10.1073/pnas.1917750117.
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Assemblies of structural maintenance of chromosomes (SMC) proteins and kleisin subunits are essential to chromosome organization and segregation across all kingdoms of life. While structural data exist for parts of the SMC−kleisin complexes, complete structures of the entire complexes have yet to be determined, making mechanistic studies difficult. Using an integrative approach that combines crystallographic structural information about the globular subdomains, along with coevolutionary information and an energy landscape optimized force field (AWSEM), we predict atomic-scale structures for several tripartite SMC−kleisin complexes, including prokaryotic condensin, eukaryotic cohesin, and eukaryotic condensin. The molecular dynamics simulations of the SMC−kleisin protein complexes suggest that these complexes exist as a broad conformational ensemble that is made up of different topological isomers. The simulations suggest a critical role for the SMC coiled-coil regions, where the coils intertwine with various linking numbers. The twist and writhe of these braided coils are coupled with the motion of the SMC head domains, suggesting that the complexes may function as topological motors. Opening, closing, and translation along the DNA of the SMC−kleisin protein complexes would allow these motors to couple to the topology of DNA when DNA is entwined with the braided coils.
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Sutto, Ludovico, Simone Marsili, Alfonso Valencia, and Francesco Luigi Gervasio. "From residue coevolution to protein conformational ensembles and functional dynamics." Proceedings of the National Academy of Sciences 112, no. 44 (October 20, 2015): 13567–72. http://dx.doi.org/10.1073/pnas.1508584112.
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The analysis of evolutionary amino acid correlations has recently attracted a surge of renewed interest, also due to their successful use in de novo protein native structure prediction. However, many aspects of protein function, such as substrate binding and product release in enzymatic activity, can be fully understood only in terms of an equilibrium ensemble of alternative structures, rather than a single static structure. In this paper we combine coevolutionary data and molecular dynamics simulations to study protein conformational heterogeneity. To that end, we adapt the Boltzmann-learning algorithm to the analysis of homologous protein sequences and develop a coarse-grained protein model specifically tailored to convert the resulting contact predictions to a protein structural ensemble. By means of exhaustive sampling simulations, we analyze the set of conformations that are consistent with the observed residue correlations for a set of representative protein domains, showing that (i) the most representative structure is consistent with the experimental fold and (ii) the various regions of the sequence display different stability, related to multiple biologically relevant conformations and to the cooperativity of the coevolving pairs. Moreover, we show that the proposed protocol is able to reproduce the essential features of a protein folding mechanism as well as to account for regions involved in conformational transitions through the correct sampling of the involved conformers.
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Liu, Guangshuai, Huanxin Zhang, Chao Zhao, and Honghai Zhang. "Evolutionary History of the Toll-Like Receptor Gene Family across Vertebrates." Genome Biology and Evolution 12, no. 1 (December 4, 2019): 3615–34. http://dx.doi.org/10.1093/gbe/evz266.
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Abstract Adaptation to a wide range of pathogenic environments is a major aspect of the ecological adaptations of vertebrates during evolution. Toll-like receptors (TLRs) are ancient membrane-bound sensors in animals and are best known for their roles in detecting and defense against invading pathogenic microorganisms. To understand the evolutionary history of the vertebrate TLR gene family, we first traced the origin of single-cysteine cluster TLRs that share the same protein architecture with vertebrate TLRs in early-branching animals and then analyzed all members of the TLR family in over 200 species covering all major vertebrate clades. Our results indicate that although the emergence of single-cysteine cluster TLRs predates the separation of bilaterians and cnidarians, most vertebrate TLR members originated shortly after vertebrate emergence. Phylogenetic analyses divided 1,726 vertebrate TLRs into 8 subfamilies, and TLR3 may represent the most ancient subfamily that emerged before the branching of deuterostomes. Our analysis reveals that purifying selection predominated in the evolution of all vertebrate TLRs, with mean dN/dS (ω) values ranging from 0.082 for TLR21 in birds to 0.434 for TLR11 in mammals. However, we did observe patterns of positive selection acting on specific codons (527 of 60,294 codons across all vertebrate TLRs, 8.7‰), which are significantly concentrated in ligand-binding extracellular domains and suggest host–pathogen coevolutionary interactions. Additionally, we found stronger positive selection acting on nonviral compared with viral TLRs, indicating the more essential nonredundant function of viral TLRs in host immunity. Taken together, our findings provide comprehensive insight into the complex evolutionary processes of the vertebrate TLR gene family, involving gene duplication, pseudogenization, purification, and positive selection.
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Hong, Seung Hwan, Keehyoung Joo, and Jooyoung Lee. "ConDo: protein domain boundary prediction using coevolutionary information." Bioinformatics 35, no. 14 (November 30, 2018): 2411–17. http://dx.doi.org/10.1093/bioinformatics/bty973.
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AbstractMotivationDomain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based methods often perform poorly due to their use of only local (i.e. short-range) features. These conventional features such as sequence profiles, secondary structures and solvent accessibilities are typically restricted to be within 20 residues of the domain boundary candidate.ResultsTo address the performance of ML-based methods, we developed a new protein domain boundary prediction method (ConDo) that utilizes novel long-range features such as coevolutionary information in addition to the aforementioned local window features as inputs for ML. Toward this purpose, two types of coevolutionary information were extracted from multiple sequence alignment using direct coupling analysis: (i) partially aligned sequences, and (ii) correlated mutation information. Both the partially aligned sequence information and the modularity of residue–residue couplings possess long-range correlation information.Availability and implementationhttps://github.com/gicsaw/ConDo.gitSupplementary informationSupplementary data are available at Bioinformatics online.
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Panait, Liviu. "Theoretical Convergence Guarantees for Cooperative Coevolutionary Algorithms." Evolutionary Computation 18, no. 4 (December 2010): 581–615. http://dx.doi.org/10.1162/evco_a_00004.
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Cooperative coevolutionary algorithms have the potential to significantly speed up the search process by dividing the space into parts that can each be conquered separately. However, recent research presented theoretical and empirical arguments that these algorithms tend to converge to suboptimal solutions in the search space, and are thus not fit for optimization tasks. This paper details an extended formal model for cooperative coevolutionary algorithms, and uses it to explore possible reasons these algorithms converge to optimal or suboptimal solutions. We demonstrate that, under specific conditions, this theoretical model will converge to the globally optimal solution. The proofs provide the underlying theoretical foundation for a better application of cooperative coevolutionary algorithms. We demonstrate the practical advantages of applying ideas from this theoretical work to a simple problem domain.
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Tian, Jin, Minqiang Li, and Fuzan Chen. "A hybrid classification algorithm based on coevolutionary EBFNN and domain covering method." Neural Computing and Applications 18, no. 3 (April 4, 2008): 293–308. http://dx.doi.org/10.1007/s00521-008-0182-6.
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Hofmeyer, Hèrm, and Juan Manuel Davila Delgado. "Coevolutionary and genetic algorithm based building spatial and structural design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, no. 4 (October 7, 2015): 351–70. http://dx.doi.org/10.1017/s0890060415000384.
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AbstractIn this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.
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ROSENMAN, MIKE, and ROB SAUNDERS. "Self-regulatory hierarchical coevolution." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 17, no. 4 (November 2003): 273–85. http://dx.doi.org/10.1017/s089006040317401x.
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An evolutionary model for nonroutine design is presented, which is called hierarchical coevolution. The requirements for an evolutionary model of nonroutine design are provided, and some of the problems with existing approaches are discussed. Some of the ways in which these problems have been addressed are examined in terms of the design knowledge required by evolutionary processes. Then, a synthesis of these approaches as a hierarchical coevolutionary model of nonroutine design is presented and the manner in which this model addresses the requirements of an evolutionary design model is discussed. An implementation in the domain of space planning provides an example of a hierarchical design problem.
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Rodriguez-Rivas, Juan, Simone Marsili, David Juan, and Alfonso Valencia. "Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone." Proceedings of the National Academy of Sciences 113, no. 52 (December 13, 2016): 15018–23. http://dx.doi.org/10.1073/pnas.1611861114.
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Protein–protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein–protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein–protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein–protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.
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Stanley, K. O., and R. Miikkulainen. "Competitive Coevolution through Evolutionary Complexification." Journal of Artificial Intelligence Research 21 (February 1, 2004): 63–100. http://dx.doi.org/10.1613/jair.1338.
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Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.
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Xue, Xingsi, and Wenbin Tan. "Matching Cybersecurity Ontologies on Internet of Everything through Coevolutionary Multiobjective Evolutionary Algorithm." Security and Communication Networks 2022 (August 10, 2022): 1–13. http://dx.doi.org/10.1155/2022/3572404.
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Since Internet of Everything (IoE) makes all the connections that come online more relevant and valuable, they are subject to numerous security and privacy concerns. Cybersecurity ontology is a shared knowledge model for tackling the security information heterogeneity issue on IoE, which has been widely used in the IoE domain. However, the existing CSOs are developed and maintained independently, yielding the CSO heterogeneity problem. To address this issue, we need to use the similarity measure (SM) to calculate two entities’ similarity value in two CSOs and, on this basis, determine the entity correspondences, i.e., CSO alignment. Usually, it is necessary to integrate various SMs to enhance the result’s correctness, but how to combine and tune these SMs to improve the alignment’s quality is still a challenge. To face this challenge, this work first models CSO matching problem as a Constrained Multiobjective Optimization Problem (CMOOP) and then proposes a Coevolutionary Multiobjective Evolutionary Algorithm (CE-MOEA) to effectively address it. In particular, CE-MOEA uses the multiobjective evolutionary paradigm to avoid the solutions’ bias improvement and introduces the coevolutionary mechanism to trade off Pareto Front’s (PF’s) diversity and convergence. The experiment uses Ontology Alignment Evaluation Initiative’s (OAEI’s) bibliographic track and conference track and five real CSO matching tasks to test CE-MOEA’s performance. Comparisons between OAEI’s participants and EA- and MOEA-based matching techniques show that CE-MOEA is able to effectively address various heterogeneous ontology matching problems and determine high-quality CSO alignments.
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Campbell, Matthew A., Shannon Loncar, Robert M. Kotin, and Robert J. Gifford. "Comparative analysis reveals the long-term coevolutionary history of parvoviruses and vertebrates." PLOS Biology 20, no. 11 (November 29, 2022): e3001867. http://dx.doi.org/10.1371/journal.pbio.3001867.
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Parvoviruses (family Parvoviridae) are small DNA viruses that cause numerous diseases of medical, veterinary, and agricultural significance and have important applications in gene and anticancer therapy. DNA sequences derived from ancient parvoviruses are common in animal genomes and analysis of these endogenous parvoviral elements (EPVs) has demonstrated that the family, which includes twelve vertebrate-specific genera, arose in the distant evolutionary past. So far, however, such “paleovirological” analysis has only provided glimpses into the biology of ancient parvoviruses and their long-term evolutionary interactions with hosts. Here, we comprehensively map EPV diversity in 752 published vertebrate genomes, revealing defining aspects of ecology and evolution within individual parvovirus genera. We identify 364 distinct EPV sequences and show these represent approximately 200 unique germline incorporation events, involving at least five distinct parvovirus genera, which took place at points throughout the Cenozoic Era. We use the spatiotemporal and host range calibrations provided by these sequences to infer defining aspects of long-term evolution within individual parvovirus genera, including mammalian vicariance for genus Protoparvovirus, and interclass transmission for genus Dependoparvovirus. Moreover, our findings support a model of virus evolution in which the long-term cocirculation of multiple parvovirus genera in vertebrates reflects the adaptation of each viral genus to fill a distinct ecological niche. Our findings show that efforts to develop parvoviruses as therapeutic tools can be approached from a rational foundation based on comparative evolutionary analysis. To support this, we published our data in the form of an open, extensible, and cross-platform database designed to facilitate the wider utilisation of evolution-related domain knowledge in parvovirus research.
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Wever, Marcel, Lorijn van Rooijen, and Heiko Hamann. "Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets." Evolutionary Computation 28, no. 2 (June 2020): 165–93. http://dx.doi.org/10.1162/evco_a_00266.
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In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ``user oracle'' represents input received from the user and the “knowledge oracle” represents available, formalized domain knowledge. We call our two-oracle approach the “first apply knowledge then query” (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.
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Puppala, Narendra, and Sandip Sen. "Evolving Cooperative Groups Using Shared Memory." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 457–61. http://dx.doi.org/10.20965/jaciii.1999.p0457.
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We present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. We coevolve agent behavior with genetic algorithms (GAs), where one GA population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and the best strategy pairs are stored together in shared memory. To evaluate a strategy from one population, it is paired sequentially with strategies from the other population stored in shared memory. The maximum evaluation from all such pairings is used to evaluate the strategy. A pair is stored in shared memory if we encounter at least one pair previously stored there with a lower fitness value. We evaluate our approach using an asymmetric room painting domain with two agents - a whitewasher and a painter. Shared memory proved superior to random pairing in consistently generating optimal behavior patterns.
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Chen, Zhen, Weiqian Meyer, Sugima Rappert, Jibin Sun, and An-Ping Zeng. "Coevolutionary Analysis Enabled Rational Deregulation of Allosteric Enzyme Inhibition in Corynebacterium glutamicum for Lysine Production." Applied and Environmental Microbiology 77, no. 13 (April 29, 2011): 4352–60. http://dx.doi.org/10.1128/aem.02912-10.
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ABSTRACTProduct feedback inhibition of allosteric enzymes is an essential issue for the development of highly efficient microbial strains for bioproduction. Here we used aspartokinase fromCorynebacterium glutamicum(CgAK), a key enzyme controlling the biosynthesis of industrially important aspartate family amino acids, as a model to demonstrate a fast and efficient approach to the deregulation of allostery. In the last 50 years many researchers and companies have made considerable efforts to deregulate this enzyme from allosteric inhibition by lysine and threonine. However, only a limited number of positive mutants have been identified so far, almost exclusively by random mutation and selection. In this study, we used statistical coupling analysis of protein sequences, a method based on coevolutionary analysis, to systematically clarify the interaction network within the regulatory domain of CgAK that is essential for allosteric inhibition. A cluster of interconnected residues linking different inhibitors' binding sites as well as other regions of the protein have been identified, including most of the previously reported positions of successful mutations. Beyond these mutation positions, we have created another 14 mutants that can partially or completely desensitize CgAK from allosteric inhibition, as shown by enzyme activity assays. The introduction of only one of the inhibition-insensitive CgAK mutations (here Q298G) into a wild-typeC. glutamicumstrain by homologous recombination resulted in an accumulation of 58 g/literl-lysine within 30 h of fed-batch fermentation in a bioreactor.
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Deliyska, Boryana, Vladislav Todorov, and Adelina Ivanova. "Common Ontology of Sustainable Development." International Journal of Information Systems and Social Change 11, no. 4 (October 2020): 55–69. http://dx.doi.org/10.4018/ijissc.2020100104.
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Sustainable development in its various aspects is a multifaceted and contradictory concept, interpreted by multiple sources. This study exposes the need to explore and develop a productive coevolutionary approach to build modular ontological metamodel of sustainable development including a set of interconnected ontologies and instance databases. Based on a review and on an analysis of existing ontologies in this area, a methodology for building common ontology of sustainable development is proposed as a top level of the metamodel. Using terminology from well-established and authoritative sources in the area, as well as the methods, the resources, and the standards of Semantic Web, the methodology contains stages: exploring the SD concepts; controlled vocabulary and thesaurus creating; ontology formalization, verification, querying, and publication. Relationships with the respective domain and other related to sustainable development ontologies are established. In conclusion, the results and the future development of the ontological metamodel are discussed.
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MORFFE, JANS, NAYLA GARCÍA, LAURA VÉLIZ, KOICHI HASEGAWA, and RAMON A. CARRENO. "Morphological and molecular characterization of two species of nematodes (Oxyuridomorpha: Thelastomatoidea: Protrelloididae, Thelastomatidae) parasitic in the cockroach Blaberus discoidalis Serville (Blattaria: Blaberidae) from Cuba." Zootaxa 5194, no. 1 (October 4, 2022): 92–108. http://dx.doi.org/10.11646/zootaxa.5194.1.5.
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Protrelleta floridana Chitwood, 1932 and Cranifera cranifera (Chitwood, 1932) (Oxyuridomorpha: Thelastomatoidea: Protrelloididae, Thelastomatidae) are recorded for the first time in Cuba. These nematodes were found to parasitize the cockroach Blaberus discoidalis Serville (Blattaria: Blaberidae), constituting a new host record for them. Both species are redescribed with the aid of scanning electron microscopy and the arrangement of the copulatory papillae of the males of P. floridana is amended. The present specimens coincide in their morphology and most of the measurements with the type populations from USA and the records from Costa Rica, with only minor differences. The molecular phylogeny was inferred by mean of the D2-D3 domain of the 28S rDNA and the Cuban P. floridana and C. cranifera form monophyletic clades with sequences of both taxa from Costa Rica as well as a sequence of C. cranifera from Russia. In the case of C. cranifera its phylogeny and that of its blaberid hosts reveal coevolutionary relationships.
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Cavalier-Smith, Thomas, and Ema E.-Yung Chao. "Multidomain ribosomal protein trees and the planctobacterial origin of neomura (eukaryotes, archaebacteria)." Protoplasma 257, no. 3 (January 3, 2020): 621–753. http://dx.doi.org/10.1007/s00709-019-01442-7.
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AbstractPalaeontologically, eubacteria are > 3× older than neomura (eukaryotes, archaebacteria). Cell biology contrasts ancestral eubacterial murein peptidoglycan walls and derived neomuran N-linked glycoprotein coats/walls. Misinterpreting long stems connecting clade neomura to eubacteria on ribosomal sequence trees (plus misinterpreted protein paralogue trees) obscured this historical pattern. Universal multiprotein ribosomal protein (RP) trees, more accurate than rRNA trees, are taxonomically undersampled. To reduce contradictions with genically richer eukaryote trees and improve eubacterial phylogeny, we constructed site-heterogeneous and maximum-likelihood universal three-domain, two-domain, and single-domain trees for 143 eukaryotes (branching now congruent with 187-protein trees), 60 archaebacteria, and 151 taxonomically representative eubacteria, using 51 and 26 RPs. Site-heterogeneous trees greatly improve eubacterial phylogeny and higher classification, e.g. showing gracilicute monophyly, that many ‘rDNA-phyla’ belong in Proteobacteria, and reveal robust new phyla Synthermota and Aquithermota. Monoderm Posibacteria and Mollicutes (two separate wall losses) are both polyphyletic: multiple outer membrane losses in Endobacteria occurred separately from Actinobacteria; neither phylum is related to Chloroflexi, the most divergent prokaryotes, which originated photosynthesis (new model proposed). RP trees support an eozoan root for eukaryotes and are consistent with archaebacteria being their sisters and rooted between Filarchaeota (=Proteoarchaeota, including ‘Asgardia’) and Euryarchaeota sensu-lato (including ultrasimplified ‘DPANN’ whose long branches often distort trees). Two-domain trees group eukaryotes within Planctobacteria, and archaebacteria with Planctobacteria/Sphingobacteria. Integrated molecular/palaeontological evidence favours negibacterial ancestors for neomura and all life. Unique presence of key pre-neomuran characters favours Planctobacteria only as ancestral to neomura, which apparently arose by coevolutionary repercussions (explained here in detail, including RP replacement) of simultaneous outer membrane and murein loss. Planctobacterial C-1 methanotrophic enzymes are likely ancestral to archaebacterial methanogenesis and β-propeller-α-solenoid proteins to eukaryotic vesicle coats, nuclear-pore-complexes, and intraciliary transport. Planctobacterial chaperone-independent 4/5-protofilament microtubules and MamK actin-ancestors prepared for eukaryote intracellular motility, mitosis, cytokinesis, and phagocytosis. We refute numerous wrong ideas about the universal tree.
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Zhang, Xuan, Xueting Wang, Kai Xu, Zhihao Jiang, Kai Dong, Xialin Xie, He Zhang, et al. "The serine/threonine/tyrosine kinase STY46 defends against hordeivirus infection by phosphorylating γb protein." Plant Physiology 186, no. 1 (February 12, 2021): 715–30. http://dx.doi.org/10.1093/plphys/kiab056.
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Abstract Protein phosphorylation is a common post-translational modification that frequently occurs during plant–virus interaction. Host protein kinases often regulate virus infectivity and pathogenicity by phosphorylating viral proteins. The Barley stripe mosaic virus (BSMV) γb protein plays versatile roles in virus infection and the coevolutionary arms race between plant defense and viral counter-defense. Here, we identified that the autophosphorylated cytosolic serine/threonine/tyrosine (STY) protein kinase 46 of Nicotiana benthamiana (NbSTY46) phosphorylates and directly interacts with the basic motif domain (aa 19–47) of γb in vitro and in vivo. Overexpression of wild-type NbSTY46, either transiently or transgenically, suppresses BSMV replication and ameliorates viral symptoms, whereas silencing of NbSTY46 leads to increased viral replication and exacerbated symptom. Moreover, the antiviral role of NbSTY46 requires its kinase activity, as the NbSTY46T436A mutant, lacking kinase activity, not only loses the ability to phosphorylate and interact with γb but also fails to impair BSMV infection when expressed in plants. NbSTY46 could also inhibit the replication of Lychnis ringspot virus, another chloroplast-replicating hordeivirus. In summary, we report a function of the cytosolic kinase STY46 in defending against plant viral infection by phosphorylating a viral protein in addition to its basal function in plant growth, development, and abiotic stress responses.
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McIntyre, Andrew R., and Malcolm I. Heywood. "Classification as Clustering: A Pareto Cooperative-Competitive GP Approach." Evolutionary Computation 19, no. 1 (March 2011): 137–66. http://dx.doi.org/10.1162/evco_a_00016.
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Intuitively population based algorithms such as genetic programming provide a natural environment for supporting solutions that learn to decompose the overall task between multiple individuals, or a team. This work presents a framework for evolving teams without recourse to prespecifying the number of cooperating individuals. To do so, each individual evolves a mapping to a distribution of outcomes that, following clustering, establishes the parameterization of a (Gaussian) local membership function. This gives individuals the opportunity to represent subsets of tasks, where the overall task is that of classification under the supervised learning domain. Thus, rather than each team member representing an entire class, individuals are free to identify unique subsets of the overall classification task. The framework is supported by techniques from evolutionary multiobjective optimization (EMO) and Pareto competitive coevolution. EMO establishes the basis for encouraging individuals to provide accurate yet nonoverlaping behaviors; whereas competitive coevolution provides the mechanism for scaling to potentially large unbalanced datasets. Benchmarking is performed against recent examples of nonlinear SVM classifiers over 12 UCI datasets with between 150 and 200,000 training instances. Solutions from the proposed coevolutionary multiobjective GP framework appear to provide a good balance between classification performance and model complexity, especially as the dataset instance count increases.
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Ji, Meijun, Kangtai Sun, Hui Fang, Zhimin Zhuang, Haodong Chen, Qi Chen, Ziyi Cao, et al. "Genome-wide identification and characterization of the CLASP_N gene family in upland cotton (Gossypium hirsutum L.)." PeerJ 10 (January 3, 2022): e12733. http://dx.doi.org/10.7717/peerj.12733.
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Background Cytoplasmic linker–associated proteins (CLASPs) are tubule proteins that can bind to microtubules and participate in regulating the structure and function of microtubules, which significantly affects the development and growth of plants. These proteins have been identified in Arabidopsis; however, little research has been performed in upland cotton. Methods In this study, the whole genome of the CLASP_N family was analyzed to provide theoretical support for the function of this gene family in the development of upland cotton fiber. Bioinformatics was used to analyze the family characteristics of CLASP_N in upland cotton, such as member identification, sequence characteristics, conserved domain structure and coevolutionary relationships. Real-time fluorescent quantitative PCR (qRT-PCR) was used to clarify the expression pattern of the upland cotton CLASP_N gene family in cotton fiber. Results At the genome-wide level, we identified 16 upland cotton CLASP_N genes. A chromosomal localization analysis revealed that these 16 genes were located on 13 chromosomes. The motif results showed that all CLASP_N proteins have the CLASP_N domain. Gene structure analysis showed that the structure and length of exons and introns were consistent in the subgroups. In the evolutionary analysis with other species, the gene family clearly diverged from the other species in the evolutionary process. A promoter sequence analysis showed that this gene family contains a large number of cis-acting elements related to a variety of plant hormones. qRT-PCR was used to clarify the expression pattern of the upland cotton CLASP_N gene family in cotton fiber and leaves, and Gh210800 was found to be highly expressed in the later stages of fiber development. The results of this study provide a foundation for further research on the molecular role of the CLASP_N genes in cotton fiber development.
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Goubard, Armelle, François Clavel, Fabrizio Mammano, and Béatrice Labrosse. "In vivo selection by enfuvirtide of HIV type-1 env quasispecies with optimal potential for phenotypic expression of HR1 mutations." Antiviral Therapy 14, no. 4 (May 1, 2008): 597–602. http://dx.doi.org/10.1177/135965350901400409.
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Background HIV type-1 (HIV-1) resistance to enfuvirtide (ENF) is mediated by mutations in the HR1 domain of gp41. We have previously shown that some of these mutations are selected in the context of env backgrounds that are not dominant before exposure to ENF, suggesting that particular env environments could facilitate phenotypic expression of HR1-mediated ENF resistance. Methods Envelope clones, representing the viral quasispecies present in the longitudinal follow-up of a patient who failed ENF-based salvage therapy, were tested for ENF susceptibility and Env-related replicative capacity. ENF resistance mutations in HR1 were introduced or back-mutated in representative clones to evaluate their phenotypic effect in different genetic contexts. Results The ENF resistance levels produced by the introduction of mutation V38A in pretherapeutic env sequences were significantly lower than those of env clones harvested after viral escape, and in which V38A was naturally selected. Back-mutation of V38A from these clones resulted in a strong loss in ENF resistance, but these clones retained significant residual resistance, again strongly suggesting the role of determinants outside of HR1 in HIV-1 resistance to ENF. By contrast with changes in resistance, addition or removal of HR1 mutations in env clones had little effect on viral replicative capacity. Conclusions The development of ENF resistance in vivo is a concerted coevolutionary process whereby HR1 mutations are selected within env variants that permit their optimal phenotypic expression.
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Balbuena, Juan Antonio, Óscar Alejandro Pérez-Escobar, Cristina Llopis-Belenguer, and Isabel Blasco-Costa. "Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot." Systematic Biology 69, no. 6 (April 16, 2020): 1212–30. http://dx.doi.org/10.1093/sysbio/syaa033.
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Abstract Symbiosis is a key driver of evolutionary novelty and ecological diversity, but our understanding of how macroevolutionary processes originate extant symbiotic associations is still very incomplete. Cophylogenetic tools are used to assess the congruence between the phylogenies of two groups of organisms related by extant associations. If phylogenetic congruence is higher than expected by chance, we conclude that there is cophylogenetic signal in the system under study. However, how to quantify cophylogenetic signal is still an open issue. We present a novel approach, Random Tanglegram Partitions (Random TaPas) that applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic congruence. By means of simulations, we show that the output value produced is inversely proportional to the number and proportion of cospeciation events employed to build simulated tanglegrams. In addition, with time-calibrated trees, Random TaPas can also distinguish cospeciation from pseudocospeciation. Random TaPas can handle large tanglegrams in affordable computational time and incorporates phylogenetic uncertainty in the analyses. We demonstrate its application with two real examples: passerine birds and their feather mites, and orchids and bee pollinators. In both systems, Random TaPas revealed low cophylogenetic signal, but mapping its variation onto the tanglegram pointed to two different coevolutionary processes. We suggest that the recursive partitioning of the tanglegram buffers the effect of phylogenetic nonindependence occurring in current global-fit methods and therefore Random TaPas is more reliable than regular global-fit methods to identify host–symbiont associations that contribute most to cophylogenetic signal. Random TaPas can be implemented in the public-domain statistical software R with scripts provided herein. A User’s Guide is also available at GitHub.[Codiversification; coevolution; cophylogenetic signal; Symbiosis.]
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Chengqi Zhang*, Ling Guan**, and Zheru Chi. "Introduction to the Special Issue on Learning in Intelligent Algorithms and Systems Design." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (December 20, 1999): 439–40. http://dx.doi.org/10.20965/jaciii.1999.p0439.
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Learning has long been and will continue to be a key issue in intelligent algorithms and systems design. Emulating the behavior and mechanisms of human learning by machines at such high levels as symbolic processing and such low levels as neuronal processing has long been a dominant interest among researchers worldwide. Neural networks, fuzzy logic, and evolutionary algorithms represent the three most active research areas. With advanced theoretical studies and computer technology, many promising algorithms and systems using these techniques have been designed and implemented for a wide range of applications. This Special Issue presents seven papers on learning in intelligent algorithms and systems design from researchers in Japan, China, Australia, and the U.S. <B>Neural Networks:</B> Emulating low-level human intelligent processing, or neuronal processing, gave birth of artificial neural networks more than five decades ago. It was hoped that devices based on biological neural networks would possess characteristics of the human brain. Neural networks have reattracted researchers' attention since the late 1980s when back-propagation algorithms were used to train multilayer feed-forward neural networks. In the last decades, we have seen promising progress in this research field yield many new models, learning algorithms, and real-world applications, evidenced by the publication of new journals in this field. <B>Fuzzy Logic:</B> Since L. A. Zadeh introduced fuzzy set theory in 1965, fuzzy logic has increasingly become the focus of many researchers and engineers opening up new research and problem solving. Fuzzy set theory has been favorably applied to control system design. In the last few years, fuzzy model applications have bloomed in image processing and pattern recognition. <B>Evolutionary Algorithms:</B> Evolutionary optimization algorithms have been studied over three decades, emulating natural evolutionary search and selection so powerful in global optimization. The study of evolutionary algorithms includes evolutionary programming (EP), evolutionary strategies (ESs), genetic algorithms (GAs), and genetic programming (GP). In the last few years, we have also seen multiple computational algorithms combined to maximize system performance, such as neurofuzzy networks, fuzzy neural networks, fuzzy logic and genetic optimization, neural networks, and evolutionary algorithms. This Special Issue also includes papers that introduce combined techniques. <B>Wang</B> et al present an improved fuzzy algorithm for enhanced eyeground images. Examination of the eyeground image is effective in diagnosing glaucoma and diabetes. Conventional eyeground image quality is usually too poor for doctors to obtain useful information, so enhancement is required to eliminate this. Due to details and uncertainties in eyeground images, conventional enhancement such as histogram equalization, edge enhancement, and high-pass filters fail to achieve good results. Fuzzy enhancement enhances images in three steps: (1) transferring an image from the spatial domain to the fuzzy domain; (2) conducting enhancement in the fuzzy domain; and (3) returning the image from the fuzzy domain to the spatial domain. The paper detailing this proposes improved mapping and fast implementation. <B>Mohammadian</B> presents a method for designing self-learning hierarchical fuzzy logic control systems based on the integration of evolutionary algorithms and fuzzy logic. The purpose of such an approach is to provide an integrated knowledge base for intelligent control and collision avoidance in a multirobot system. Evolutionary algorithms are used as in adaptation for learning fuzzy knowledge bases of control systems and learning, mapping, and interaction between fuzzy knowledge bases of different fuzzy logic systems. Fuzzy integral has been found useful in data fusion. <B>Pham and Wagner</B> present an approach based on the fuzzy integral and GAs to combine likelihood values of cohort speakers. The fuzzy integral nonlinearly fuses similarity measures of an utterance assigned to cohort speakers. In their approach, Gas find optimal fuzzy densities required for fuzzy fusion. Experiments using commercial speech corpus T146 show their approach achieves more favorable performance than conventional normalization. Evolution reflects the behavior of a society. <B>Puppala and Sen</B> present a coevolutionary approach to generating behavioral strategies for cooperating agent groups. Agent behavior evolves via GAs, where one genetic algorithm population is evolved per individual in the cooperative group. Groups are evaluated by pairing strategies from each population and best strategy pairs are stored together in shared memory. The approach is evaluated using asymmetric room painting and results demonstrate the superiority of shared memory over random pairing in consistently generating optimal behavior patterns. Object representation and template optimization are two main factors affecting object recognition performance. <B>Lu</B> et al present an evolutionary algorithm for optimizing handwritten numeral templates represented by rational B-spline surfaces of character foreground-background-distance distribution maps. Initial templates are extracted from training a feed-forward neural network instead of using arbitrarily chosen patterns to reduce iterations required in evolutionary optimization. To further reduce computational complexity, a fast search is used in selection. Using 1,000 optimized numeral templates, the classifier achieves a classification rate of 96.4% while rejecting 90.7% of nonnumeral patterns when tested on NIST Special Database 3. Determining an appropriate number of clusters is difficult yet important. <B>Li</B> et al based their approach based on rival penalized competitive learning (RPCL), addressing problems of overlapped clusters and dependent components of input vectors by incorporating full covariance matrices into the original RPCL algorithm. The resulting learning algorithm progressively eliminates units whose clusters contain only a small amount of training data. The algorithm is applied to determine the number of clusters in a Gaussian mixture distribution and to optimize the architecture of elliptical function networks for speaker verification and for vowel classification. Another important issue on learning is <B>Kurihara and Sugawara's</B> adaptive reinforcement learning algorithm integrating exploitation- and exploration-oriented learning. This algorithm is more robust in dynamically changing, large-scale environments, providing better performance than either exploitation- learning or exploration-oriented learning, making it is well suited for autonomous systems. In closing we would like to thank the authors who have submitted papers to this Special Issue and express our appreciation to the referees for their excellent work in reading papers under a tight schedule.
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Shcherbakova, Olena, Volker Gast, Damián E. Blasi, Hedvig Skirgård, Russell D. Gray, and Simon J. Greenhill. "A quantitative global test of the complexity trade-off hypothesis: the case of nominal and verbal grammatical marking." Linguistics Vanguard, December 6, 2022. http://dx.doi.org/10.1515/lingvan-2021-0011.
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Abstract Nouns and verbs are known to differ in the types of grammatical information they encode. What is less well known is the relationship between verbal and nominal coding within and across languages. The equi-complexity hypothesis holds that all languages are equally complex overall, which entails trade-offs between coding in different domains. From a diachronic point of view, this hypothesis implies that the loss and gain of coding in different domains can be expected to balance each other out. In this study, we test to what extent such inverse coevolution can be observed in a sample of 244 languages, using data from a comprehensive cross-linguistic database (Grambank) and applying computational phylogenetic modelling to control for genealogical relatedness. We find evidence for coevolutionary relationships between specific features within nominal and verbal domains on a global scale, but not for overall degrees of grammatical coding between languages. Instead, these amounts of nominal and verbal coding are positively correlated in Sino-Tibetan languages and inversely correlated in Indo-European languages. Our findings indicate that accretion and loss of grammatical information in nominal words and verbs are lineage-specific.
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Wu, Tianqi, Jie Hou, Badri Adhikari, and Jianlin Cheng. "Analysis of several key factors influencing deep learning-based inter-residue contact prediction." Bioinformatics, August 30, 2019. http://dx.doi.org/10.1093/bioinformatics/btz679.
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Abstract Motivation Deep learning has become the dominant technology for protein contact prediction. However, the factors that affect the performance of deep learning in contact prediction have not been systematically investigated. Results We analyzed the results of our three deep learning-based contact prediction methods (MULTICOM-CLUSTER, MULTICOM-CONSTRUCT and MULTICOM-NOVEL) in the CASP13 experiment and identified several key factors [i.e. deep learning technique, multiple sequence alignment (MSA), distance distribution prediction and domain-based contact integration] that influenced the contact prediction accuracy. We compared our convolutional neural network (CNN)-based contact prediction methods with three coevolution-based methods on 75 CASP13 targets consisting of 108 domains. We demonstrated that the CNN-based multi-distance approach was able to leverage global coevolutionary coupling patterns comprised of multiple correlated contacts for more accurate contact prediction than the local coevolution-based methods, leading to a substantial increase of precision by 19.2 percentage points. We also tested different alignment methods and domain-based contact prediction with the deep learning contact predictors. The comparison of the three methods showed deeper sequence alignments and the integration of domain-based contact prediction with the full-length contact prediction improved the performance of contact prediction. Moreover, we demonstrated that the domain-based contact prediction based on a novel ab initio approach of parsing domains from MSAs alone without using known protein structures was a simple, fast approach to improve contact prediction. Finally, we showed that predicting the distribution of inter-residue distances in multiple distance intervals could capture more structural information and improve binary contact prediction. Availability and implementation https://github.com/multicom-toolbox/DNCON2/. Supplementary information Supplementary data are available at Bioinformatics online.
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Chonofsky, Mark, Saulo H. P. de Oliveira, Konrad Krawczyk, and Charlotte M. Deane. "The evolution of contact prediction: Evidence that contact selection in statistical contact prediction is changing." Bioinformatics, November 6, 2019. http://dx.doi.org/10.1093/bioinformatics/btz816.
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Abstract Motivation Over the last few years, the field of protein structure prediction has been transformed by increasingly-accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments. However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV, and DNCON2, as examples of direct coupling analysis, meta-prediction, and deep learning. Results We considered correctly-predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from multiple sequence alignments. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. Availability We use publicly-available databases. Our code is available for download at http://opig.stats.ox.ac.uk/. Supplementary information Supplementary information is available at Bioinformatics online.
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Ritter, Seth C., and Benjamin J. Hackel. "Validation and Stabilization of a Prophage Lysin of Clostridium perfringens by Using Yeast Surface Display and Coevolutionary Models." Applied and Environmental Microbiology 85, no. 10 (March 8, 2019). http://dx.doi.org/10.1128/aem.00054-19.
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ABSTRACT Bacteriophage lysins are compelling antimicrobial proteins whose biotechnological utility and evolvability would be aided by elevated stability. Lysin catalytic domains, which evolved as modular entities distinct from cell wall binding domains, can be classified into one of several families with highly conserved structure and function, many of which contain thousands of annotated homologous sequences. Motivated by the quality of these evolutionary data, the performance of generative protein models incorporating coevolutionary information was analyzed to predict the stability of variants in a collection of 9,749 multimutants across 10 libraries diversified at different regions of a putative lysin from a prophage region of a Clostridium perfringens genome. Protein stability was assessed via a yeast surface display assay with accompanying high-throughput sequencing. Statistical fitness of mutant sequences, derived from second-order Potts models inferred with different levels of sequence homolog information, was predictive of experimental stability with areas under the curve (AUCs) ranging from 0.78 to 0.85. To extract an experimentally derived model of stability, a logistic model with site-wise score contributions was regressed on the collection of multimutants. This achieved a cross-validated classification performance of 0.95. Using this experimentally derived model, 5 designs incorporating 5 or 6 mutations from multiple libraries were constructed. All designs retained enzymatic activity, with 4 of 5 increasing the melting temperature and with the highest-performing design achieving an improvement of +4°C. IMPORTANCE Bacteriophage lysins exhibit high specificity and activity toward host bacteria with which the phage coevolved. These properties of lysins make them attractive for use as antimicrobials. Although there has been significant effort to develop platforms for rapid lysin engineering, there have been numerous shortcomings when pursuing the ultrahigh throughput necessary for the discovery of rare combinations of mutations to improve performance. In addition to validation of a putative lysin and stabilization thereof, the experimental and computational methods presented here offer a new avenue for improving protein stability and are easily scalable to analysis of tens of millions of mutations in single experiments.
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Acharya, Debarun, and Tapan K. Dutta. "Elucidating the network features and evolutionary attributes of intra- and interspecific protein–protein interactions between human and pathogenic bacteria." Scientific Reports 11, no. 1 (January 8, 2021). http://dx.doi.org/10.1038/s41598-020-80549-x.
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AbstractHost–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for interspecific domain-domain interactions. Moreover, these interactions do not always occur with high-affinity, as we observed that bacteria-interacting human proteins are rich in protein-disorder content, which correlates positively with the number of interacting pathogen proteins, facilitating low-affinity interspecific interactions. Furthermore, functional analyses of pathogen-interacting human proteins revealed an enrichment in regulation of processes like metabolism, immune system, cellular localization and transport apart from divulging functional competence to bind enzyme/protein, nucleic acids and cell adhesion molecules, necessary for host-microbial cross-talk.
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Acharya, Debarun, and Tapan K. Dutta. "Elucidating the network features and evolutionary attributes of intra- and interspecific protein–protein interactions between human and pathogenic bacteria." Scientific Reports 11, no. 1 (January 8, 2021). http://dx.doi.org/10.1038/s41598-020-80549-x.
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AbstractHost–pathogen interaction is one of the most powerful determinants involved in coevolutionary processes covering a broad range of biological phenomena at molecular, cellular, organismal and/or population level. The present study explored host–pathogen interaction from the perspective of human–bacteria protein–protein interaction based on large-scale interspecific and intraspecific interactome data for human and three pathogenic bacterial species, Bacillus anthracis, Francisella tularensis and Yersinia pestis. The network features revealed a preferential enrichment of intraspecific hubs and bottlenecks for both human and bacterial pathogens in the interspecific human–bacteria interaction. Analyses unveiled that these bacterial pathogens interact mostly with human party-hubs that may enable them to affect desired functional modules, leading to pathogenesis. Structural features of pathogen-interacting human proteins indicated an abundance of protein domains, providing opportunities for interspecific domain-domain interactions. Moreover, these interactions do not always occur with high-affinity, as we observed that bacteria-interacting human proteins are rich in protein-disorder content, which correlates positively with the number of interacting pathogen proteins, facilitating low-affinity interspecific interactions. Furthermore, functional analyses of pathogen-interacting human proteins revealed an enrichment in regulation of processes like metabolism, immune system, cellular localization and transport apart from divulging functional competence to bind enzyme/protein, nucleic acids and cell adhesion molecules, necessary for host-microbial cross-talk.
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Kennedy, Emily N., Clay A. Foster, Sarah A. Barr, and Robert B. Bourret. "General strategies for using amino acid sequence data to guide biochemical investigation of protein function." Biochemical Society Transactions, November 23, 2022. http://dx.doi.org/10.1042/bst20220849.
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The rapid increase of ‘-omics' data warrants the reconsideration of experimental strategies to investigate general protein function. Studying individual members of a protein family is likely insufficient to provide a complete mechanistic understanding of family functions, especially for diverse families with thousands of known members. Strategies that exploit large amounts of available amino acid sequence data can inspire and guide biochemical experiments, generating broadly applicable insights into a given family. Here we review several methods that utilize abundant sequence data to focus experimental efforts and identify features truly representative of a protein family or domain. First, coevolutionary relationships between residues within primary sequences can be successfully exploited to identify structurally and/or functionally important positions for experimental investigation. Second, functionally important variable residue positions typically occupy a limited sequence space, a property useful for guiding biochemical characterization of the effects of the most physiologically and evolutionarily relevant amino acids. Third, amino acid sequence variation within domains shared between different protein families can be used to sort a particular domain into multiple subtypes, inspiring further experimental designs. Although generally applicable to any kind of protein domain because they depend solely on amino acid sequences, the second and third approaches are reviewed in detail because they appear to have been used infrequently and offer immediate opportunities for new advances. Finally, we speculate that future technologies capable of analyzing and manipulating conserved and variable aspects of the three-dimensional structures of a protein family could lead to broad insights not attainable by current methods.
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Auer, S., J. Heitzig, U. Kornek, E. Schöll, and J. Kurths. "The Dynamics of Coalition Formation on Complex Networks." Scientific Reports 5, no. 1 (August 25, 2015). http://dx.doi.org/10.1038/srep13386.
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Abstract Complex networks describe the structure of many socio-economic systems. However, in studies of decision-making processes the evolution of the underlying social relations are disregarded. In this report, we aim to understand the formation of self-organizing domains of cooperation (“coalitions”) on an acquaintance network. We include both the network’s influence on the formation of coalitions and vice versa how the network adapts to the current coalition structure, thus forming a social feedback loop. We increase complexity from simple opinion adaptation processes studied in earlier research to more complex decision-making determined by costs and benefits and from bilateral to multilateral cooperation. We show how phase transitions emerge from such coevolutionary dynamics, which can be interpreted as processes of great transformations. If the network adaptation rate is high, the social dynamics prevent the formation of a grand coalition and therefore full cooperation. We find some empirical support for our main results: Our model develops a bimodal coalition size distribution over time similar to those found in social structures. Our detection and distinguishing of phase transitions may be exemplary for other models of socio-economic systems with low agent numbers and therefore strong finite-size effects.
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Lee, Jaekwon, Seung Yeob Shin, Shiva Nejati, and Lionel C. Briand. "Optimal priority assignment for real-time systems: a coevolution-based approach." Empirical Software Engineering 27, no. 6 (August 6, 2022). http://dx.doi.org/10.1007/s10664-022-10170-1.
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AbstractIn real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.
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Malinverni, Duccio, Alfredo Jost Lopez, Paolo De Los Rios, Gerhard Hummer, and Alessandro Barducci. "Modeling Hsp70/Hsp40 interaction by multi-scale molecular simulations and coevolutionary sequence analysis." eLife 6 (May 12, 2017). http://dx.doi.org/10.7554/elife.23471.
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The interaction between the Heat Shock Proteins 70 and 40 is at the core of the ATPase regulation of the chaperone machinery that maintains protein homeostasis. However, the structural details of the interaction remain elusive and contrasting models have been proposed for the transient Hsp70/Hsp40 complexes. Here we combine molecular simulations based on both coarse-grained and atomistic models with coevolutionary sequence analysis to shed light on this problem by focusing on the bacterial DnaK/DnaJ system. The integration of these complementary approaches resulted in a novel structural model that rationalizes previous experimental observations. We identify an evolutionarily conserved interaction surface formed by helix II of the DnaJ J-domain and a structurally contiguous region of DnaK, involving lobe IIA of the nucleotide binding domain, the inter-domain linker, and the β-basket of the substrate binding domain.