Academic literature on the topic 'Coevolutionary domains'

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.

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.

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.

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.

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.

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.

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).

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.

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.

The characterization of proteins and enzymes is traditionally organised according to the sequence-structure-function paradigm. The investigation of the inter-relationships between these three properties has motivated the development of several experimental and computational techniques, that have made available an unprecedented amount of sequence and structural data. The interest in developing comparative methods for rationalizing such copious information has, of course, grown in parallel. Regarding the structure-function relationship, for instance, the availability of experimentally resolved protein structures and of computer simulations have improved our understanding of the role of proteins' internal dynamics in assisting their functional rearrangements and activity. Several approaches are currently available for elucidating and comparing proteins' internal dynamics. These can capture the relevant collective degrees of freedom that recapitulate the main conformational changes. These collective coordinates have the potential to unveil remote evolutionary relationships between proteins, that are otherwise not easily accessible from purely sequence- or structure-based investigations. Starting from this premise, in the first chapter of this thesis I will present a novel and general computational method that can detect large-scale dynamical correlations in proteins by comparing different representative conformers. This is accomplished by applying dimensionality-reduction techniques to inter-amino acid distance fluctuation matrices. As a result, an optimal quasi-rigid domain decomposition of the protein or macromolecular assembly of interest is identified, and this facilitates the functionally-oriented interpretation of their internal dynamics. Building on this approach, in the second chapter I will discuss its systematic application to a class of membrane proteins of paramount biochemical interest, namely the class A G protein-coupled receptors. The comparative analysis of their internal dynamics, as encoded by the quasi-rigid domains, allowed us to identify recurrent patterns in the large-scale dynamics of these receptors. This, in turn, allowed us to single out a number of key functional sites. These were, for the most part, previously known -- a fact that at the same time validates the method, and gives confidence for the viability of the other, novel sites. Finally, for the last part of the thesis, I focussed on the sequence-structure relationship. In particular, I considered the problem of inferring structural properties of proteins from the analysis of large multiple sequence alignments of homologous sequences. For this purpose, I recasted the strategies developed for the dynamical features extraction in order to identify compact groups of coevolving residues, based only on the knowledge of amino acid variability in aligned primary sequences. Throughout the thesis, many methodological techniques have been taken into considerations, mainly based on concepts from graph theory and statistical data analysis (clustering). All these topics are explained in the methodological sections of each chapter.

Abstract Gene–culture coevolution in the cognitive domain is expected to occur whenever cultural phenomena select for genes that affect cognition. Such cultural selection would occur, for example, if the culture of making certain tools leads to selection that favours genetic variants that somehow make one better at learning to make these tools. While these coevolutionary processes seem probable and important, they are difficult to study because the genetic underpinnings of cognitive traits are often poorly understood. Indeed, most evidence for cognitive gene–culture coevolution are circumstantial or indirect, and the role of such processes is often a topic of debate. This chapter suggests, however, that a strong case for cognitive gene–culture coevolution can be made based on theoretical considerations, which can also guide future work and put current evidence in context. Using a process-based (mechanistic) approach to cognitive evolution, the authors distinguish between culturally selected genetic changes in innate knowledge, culturally selected genetic changes in attentional and learning mechanisms, and culturally selected genetic changes in the neuroanatomical substrate. In this light, the authors suggest a limited role to hypothesized culturally selected modules requiring complex innate knowledge. On the other hand, culturally selected modifications of attentional and learning parameters may be significant because they can jointly handle the computational challenges involved in the construction of complex cognitive representations. In turn, the development of such representations (in the form of associative networks), sets the demand for culturally selected changes in size and structure of neuroanatomical substrate, for which some evidence is accumulating.