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Journal articles on the topic 'Protein Structure Models'

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Author: Grafiati

Published: 6 September 2023

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1

Mucsi, Z., Z. Gaspari, G. Orosz, and A. Perczel. "Structure-oriented rational design of chymotrypsin inhibitor models." Protein Engineering Design and Selection 16, no. 9 (September 1, 2003): 673–81. http://dx.doi.org/10.1093/protein/gzg090.

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2

Kim, HyungRae. "Residue Environment Score for Selecting Protein Structure Models and Protein-Protein Docking Models." Biophysical Journal 110, no. 3 (February 2016): 346a. http://dx.doi.org/10.1016/j.bpj.2015.11.1862.

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3

Koliński, A., and J. Skolnick. "High coordination lattice models of protein structure, dynamics and thermodynamics." Acta Biochimica Polonica 44, no. 3 (September 30, 1997): 389–422. http://dx.doi.org/10.18388/abp.1997_4393.

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A high coordination lattice discretization of protein conformational space is described. The model allows discrete representation of polypeptide chains of globular proteins and small macromolecular assemblies with an accuracy comparable to the accuracy of crystallographic structures. Knowledge based force field, that consists of sequence specific short range interactions, cooperative model of hydrogen bond network and tertiary one body, two body and multibody interactions, is outlined and discussed. A model of stochastic dynamics for these protein models is also described. The proposed method enables moderate resolution tertiary structure prediction of simple and small globular proteins. Its applicability in structure prediction increases significantly when evolutionary information is exploited or/and when sparse experimental data are available. The model responds correctly to sequence mutations and could be used at early stages of a computer aided protein design and protein redesign. Computational speed, associated with the discrete structure of the model, enables studies of the long time dynamics of polypeptides and proteins and quite detailed theoretical studies of thermodynamics of nontrivial protein models.

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4

Yang, Yifeng David, Preston Spratt, Hao Chen, Changsoon Park, and Daisuke Kihara. "Sub-AQUA: real-value quality assessment of protein structure models." Protein Engineering, Design and Selection 23, no. 8 (June 4, 2010): 617–32. http://dx.doi.org/10.1093/protein/gzq030.

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5

Kihara, Daisuke, Hao Chen, and Yifeng Yang. "Quality Assessment of Protein Structure Models." Current Protein & Peptide Science 10, no. 3 (June 1, 2009): 216–28. http://dx.doi.org/10.2174/138920309788452173.

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6

Hirsch, M., and M. Habeck. "Mixture models for protein structure ensembles." Bioinformatics 24, no. 19 (July 28, 2008): 2184–92. http://dx.doi.org/10.1093/bioinformatics/btn396.

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7

Ghosh, Soma, and Saraswathi Vishveshwara. "Ranking the quality of protein structure models using sidechain based network properties." F1000Research 3 (January 21, 2014): 17. http://dx.doi.org/10.12688/f1000research.3-17.v1.

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Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server(http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.

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8

Domingues, F. S., J. Rahnenfuhrer, and T. Lengauer. "Automated clustering of ensembles of alternative models in protein structure databases." Protein Engineering Design and Selection 17, no. 6 (August 3, 2004): 537–43. http://dx.doi.org/10.1093/protein/gzh063.

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9

Shatabda, Swakkhar, M. A. Hakim Newton, Mahmood A. Rashid, Duc Nghia Pham, and Abdul Sattar. "How Good Are Simplified Models for Protein Structure Prediction?" Advances in Bioinformatics 2014 (April 29, 2014): 1–9. http://dx.doi.org/10.1155/2014/867179.

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Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP.

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10

Bienkowska, J., Hongxian He, and T. F. Smith. "Automatic pattern embedding in protein structure models." IEEE Intelligent Systems 16, no. 6 (November 2001): 21–25. http://dx.doi.org/10.1109/5254.972074.

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11

Jamroz, Michal, Andrzej Kolinski, and Daisuke Kihara. "Ensemble-based evaluation for protein structure models." Bioinformatics 32, no. 12 (June 15, 2016): i314—i321. http://dx.doi.org/10.1093/bioinformatics/btw262.

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12

Karplus, Kevin, Kimmen Sjölander, Christian Barrett, Melissa Cline, David Haussler, Richard Hughey, Liisa Holm, and Chris Sander. "Predicting protein structure using hidden Markov models." Proteins: Structure, Function, and Genetics 29, S1 (1997): 134–39. http://dx.doi.org/10.1002/(sici)1097-0134(1997)1+<134::aid-prot18>3.0.co;2-p.

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13

Bastolla, Ugo. "Computing protein dynamics from protein structure with elastic network models." Wiley Interdisciplinary Reviews: Computational Molecular Science 4, no. 5 (April 18, 2014): 488–503. http://dx.doi.org/10.1002/wcms.1186.

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14

Zhou, Xiaogen, Jun Hu, Chengxin Zhang, Guijun Zhang, and Yang Zhang. "Assembling multidomain protein structures through analogous global structural alignments." Proceedings of the National Academy of Sciences 116, no. 32 (July 24, 2019): 15930–38. http://dx.doi.org/10.1073/pnas.1905068116.

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Most proteins exist with multiple domains in cells for cooperative functionality. However, structural biology and protein folding methods are often optimized for single-domain structures, resulting in a rapidly growing gap between the improved capability for tertiary structure determination and high demand for multidomain structure models. We have developed a pipeline, termed DEMO, for constructing multidomain protein structures by docking-based domain assembly simulations, with interdomain orientations determined by the distance profiles from analogous templates as detected through domain-level structure alignments. The pipeline was tested on a comprehensive benchmark set of 356 proteins consisting of 2–7 continuous and discontinuous domains, for which DEMO generated models with correct global fold (TM-score > 0.5) for 86% of cases with continuous domains and for 100% of cases with discontinuous domain structures, starting from randomly oriented target-domain structures. DEMO was also applied to reassemble multidomain targets in the CASP12 and CASP13 experiments using domain structures excised from the top server predictions, where the full-length DEMO models showed a significantly improved quality over the original server models. Finally, sparse restraints of mass spectrometry-generated cross-linking data and cryo-EM density maps are incorporated into DEMO, resulting in improvements in the average TM-score by 6.3% and 12.5%, respectively. The results demonstrate an efficient approach to assembling multidomain structures, which can be easily used for automated, genome-scale multidomain protein structure assembly.

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15

Flechsig, Holger, and Yuichi Togashi. "Designed Elastic Networks: Models of Complex Protein Machinery." International Journal of Molecular Sciences 19, no. 10 (October 13, 2018): 3152. http://dx.doi.org/10.3390/ijms19103152.

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Recently, the design of mechanical networks with protein-inspired responses has become increasingly popular. Here, we review contributions which were motivated by studies of protein dynamics employing coarse-grained elastic network models. First, the concept of evolutionary optimization that we developed to design network structures which execute prescribed tasks is explained. We then review what presumably marks the origin of the idea to design complex functional networks which encode protein-inspired behavior, namely the design of an elastic network structure which emulates the cycles of ATP-powered conformational motion in protein machines. Two recent applications are reviewed. First, the construction of a model molecular motor, whose operation incorporates both the tight coupling power stroke as well as the loose coupling Brownian ratchet mechanism, is discussed. Second, the evolutionary design of network structures which encode optimal long-range communication between remote sites and represent mechanical models of allosteric proteins is presented. We discuss the prospects of designed protein-mimicking elastic networks as model systems to elucidate the design principles and functional signatures underlying the operation of complex protein machinery.

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16

Robic, Srebrenka. "Mathematics, Thermodynamics, and Modeling to Address Ten Common Misconceptions about Protein Structure, Folding, and Stability." CBE—Life Sciences Education 9, no. 3 (September 2010): 189–95. http://dx.doi.org/10.1187/cbe.10-03-0018.

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To fully understand the roles proteins play in cellular processes, students need to grasp complex ideas about protein structure, folding, and stability. Our current understanding of these topics is based on mathematical models and experimental data. However, protein structure, folding, and stability are often introduced as descriptive, qualitative phenomena in undergraduate classes. In the process of learning about these topics, students often form incorrect ideas. For example, by learning about protein folding in the context of protein synthesis, students may come to an incorrect conclusion that once synthesized on the ribosome, a protein spends its entire cellular life time in its fully folded native confirmation. This is clearly not true; proteins are dynamic structures that undergo both local fluctuations and global unfolding events. To prevent and address such misconceptions, basic concepts of protein science can be introduced in the context of simple mathematical models and hands-on explorations of publicly available data sets. Ten common misconceptions about proteins are presented, along with suggestions for using equations, models, sequence, structure, and thermodynamic data to help students gain a deeper understanding of basic concepts relating to protein structure, folding, and stability.

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17

Perez, Alberto, Zheng Yang, Ivet Bahar, Ken A. Dill, and Justin L. MacCallum. "FlexE: Using Elastic Network Models to Compare Models of Protein Structure." Journal of Chemical Theory and Computation 8, no. 10 (April 26, 2012): 3985–91. http://dx.doi.org/10.1021/ct300148f.

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18

van Beusekom, Bart, Natasja Wezel, Maarten L. Hekkelman, Anastassis Perrakis, Paul Emsley, and Robbie P. Joosten. "Building and rebuilding N-glycans in protein structure models." Acta Crystallographica Section D Structural Biology 75, no. 4 (April 1, 2019): 416–25. http://dx.doi.org/10.1107/s2059798319003875.

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N-Glycosylation is one of the most common post-translational modifications and is implicated in, for example, protein folding and interaction with ligands and receptors. N-Glycosylation trees are complex structures of linked carbohydrate residues attached to asparagine residues. While carbohydrates are typically modeled in protein structures, they are often incomplete or have the wrong chemistry. Here, new tools are presented to automatically rebuild existing glycosylation trees, to extend them where possible, and to add new glycosylation trees if they are missing from the model. The method has been incorporated in the PDB-REDO pipeline and has been applied to build or rebuild 16 452 carbohydrate residues in 11 651 glycosylation trees in 4498 structure models, and is also available from the PDB-REDO web server. With better modeling of N-glycosylation, the biological function of this important modification can be better and more easily understood.

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19

Azulay, Hay, Aviv Lutaty, and Nir Qvit. "How Similar Are Proteins and Origami?" Biomolecules 12, no. 5 (April 21, 2022): 622. http://dx.doi.org/10.3390/biom12050622.

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Protein folding and structural biology are highly active disciplines that combine basic research in various fields, including biology, chemistry, physics, and computer science, with practical applications in biomedicine and nanotechnology. However, there are still gaps in the understanding of the detailed mechanisms of protein folding, and protein structure-function relations. In an effort to bridge these gaps, this paper studies the equivalence of proteins and origami. Research on proteins and origami provides strong evidence to support the use of origami folding principles and mechanical models to explain aspects of proteins formation and function. Although not identical, the equivalence of origami and proteins emerges in: (i) the folding processes, (ii) the shape and structure of proteins and origami models, and (iii) the intrinsic mechanical properties of the folded structures/models, which allows them to synchronically fold/unfold and effectively distribute forces to the whole structure. As a result, origami can contribute to the understanding of various key protein-related mechanisms and support the design of de novo proteins and nanomaterials.

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20

Gáspári, Zoltán, and László Nyitray. "Coiled coils as possible models of protein structure evolution." BioMolecular Concepts 2, no. 3 (June 1, 2011): 199–210. http://dx.doi.org/10.1515/bmc.2011.015.

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AbstractCoiled coils are formed by two or more α-helices wrapped around one another. This structural motif often guides di-, tri- or multimerization of proteins involved in diverse biological processes such as membrane fusion, signal transduction and the organization of the cytoskeleton. Although coiled coil motifs seem conceptually simple and their existence was proposed in the early 1950s, the high variability of the motif makes coiled coil prediction from sequence a difficult task. They might be confused with intrinsically disordered sequences and even more with a recently described structural motif, the charged single α-helix. By contrast, the versatility of coiled coil structures renders them an ideal candidate for protein (re)design and many novel variants have been successfully created to date. In this paper, we review coiled coils in the light of protein evolution by putting our present understanding of the motif and its variants in the context of structural interconversions. We argue that coiled coils are ideal subjects for studies of subtle and large-scale structural changes because of their well-characterized and versatile nature.

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21

Mascarenhas, Nahren Manuel, and Shachi Gosavi. "Understanding protein domain-swapping using structure-based models of protein folding." Progress in Biophysics and Molecular Biology 128 (September 2017): 113–20. http://dx.doi.org/10.1016/j.pbiomolbio.2016.09.013.

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22

Wang, Wenkai, Zhenling Peng, and Jianyi Yang. "Single-sequence protein structure prediction using supervised transformer protein language models." Nature Computational Science 2, no. 12 (December 19, 2022): 804–14. http://dx.doi.org/10.1038/s43588-022-00373-3.

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23

Sasaki, J., T. Terada, S. Nakamura, and K. shimizu. "Evaluation of protein structure prediction models by computers." Seibutsu Butsuri 43, supplement (2003): S90. http://dx.doi.org/10.2142/biophys.43.s90_1.

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24

Sanchez, R. "ModBase: A database of comparative protein structure models." Bioinformatics 15, no. 12 (December 1, 1999): 1060–61. http://dx.doi.org/10.1093/bioinformatics/15.12.1060.

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25

Neelamraju, Sridhar, David J. Wales, and Shachi Gosavi. "Protein energy landscape exploration with structure-based models." Current Opinion in Structural Biology 64 (October 2020): 145–51. http://dx.doi.org/10.1016/j.sbi.2020.07.003.

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26

Monroe, Lyman, Genki Terashi, and Daisuke Kihara. "Variability of Protein Structure Models from Electron Microscopy." Structure 25, no. 4 (April 2017): 592–602. http://dx.doi.org/10.1016/j.str.2017.02.004.

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27

Taylor, William R. "Decoy Models for Protein Structure Comparison Score Normalisation." Journal of Molecular Biology 357, no. 2 (March 2006): 676–99. http://dx.doi.org/10.1016/j.jmb.2005.12.084.

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28

Kota, Pradeep, Feng Ding, Srinivas Ramachandran, and Nikolay V. Dokholyan. "Gaia: automated quality assessment of protein structure models." Bioinformatics 27, no. 16 (June 23, 2011): 2209–15. http://dx.doi.org/10.1093/bioinformatics/btr374.

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29

Yuan, Xin, Yu Shao, and Christopher Bystroff. "Ab Initio Protein Structure Prediction Using Pathway Models." Comparative and Functional Genomics 4, no. 4 (2003): 397–401. http://dx.doi.org/10.1002/cfg.305.

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30

Varadi, M., M. Deshpande, S. Nair, S. Anyango, D. Bertoni, and S. Velankar. "High-accuracy protein structure models in AlphaFold DB." Acta Crystallographica Section A Foundations and Advances 78, a2 (August 23, 2022): a436. http://dx.doi.org/10.1107/s2053273322093044.

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31

Eyre, T. A., L. Partridge, and J. M. Thornton. "Computational analysis of -helical membrane protein structure: implications for the prediction of 3D structural models." Protein Engineering Design and Selection 17, no. 8 (September 23, 2004): 613–24. http://dx.doi.org/10.1093/protein/gzh072.

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32

Gaasterland, Teri. "Strategies for Structural Genomics Target Selection." Scientific World JOURNAL 2 (2002): 67. http://dx.doi.org/10.1100/tsw.2002.33.

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We have developed a protein sequence analysis pipeline that ranks proteins as targets for high throughput structure determination. The ranking is designed to maximize both the biological and informational impact of new 3D protein structures solved through the structural genomics initiative. The analysis system accepts proteins from multiple genomes as input, builds sequence families based on remote homology, identifies families with one or more solved structures, and ranks the remaining families according to criteria designed to maximize structure determination efficacy, increase the likelihood of a novel fold, and maximize the number of new protein structure models that can be built from a solved structure.

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33

Thomas, Jens, Ronan Keegan, Jaclyn Bibby, Martyn Winn, Olga Mayans, and Daniel Rigden. "Rapid molecular replacement of coiled-coil and transmembrane proteins with AMPLE." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C347. http://dx.doi.org/10.1107/s2053273314096521.

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Molecular Replacement (MR) is an increasingly popular route to protein structure solution. AMPLE[1] is a software pipeline that uses either cheaply obtained ab inito protein models, or NMR structures to extend the scope of MR, allowing it to solve entirely novel protein structures in a completely automated pipeline on a standard desktop computer. AMPLE employs a cluster-and-truncate approach, combined with multiple modes of side chain treatment, to analyse the candidate models and extract the consensual features most likely to solve the structure. The search models generated in this way are screened by MrBump using Phaser and Molrep and correct solutions are detected using main chain tracing and phase modification with Shelxe. AMPLE proved capable of processing rapidly obtained ab initio structure predictions into successful search models and more recently proved effective in assembling NMR structures for MR[2]. Coiled-coil proteins are a distinct class of protein fold whose structure solution by MR is not typically straightforward. We show here that AMPLE can quickly and routinely solve most coiled-coil structures using ab initio predictions from Rosetta. The predictions are generally not globally accurate, but by encompassing different degrees of truncation of clustered models, AMPLE succeeds by sampling across a range of search models. These sometimes succeed through capturing locally well-modelled conformations, but often simply contain small helical units. Remarkably, the latter regularly succeed despite out-of-register placement and poor MR statistics. We demonstrate that single structures derived from successful ensembles perform less well, and comparable ideal helices solve few targets. Thus, both modelling of distortions from ideal helical geometry and the ensemble nature of the search models contribute to success. AMPLE is a framework applicable to any set of input structures in which variability is correlated with inaccuracy. We also present preliminary data demonstrating structure solution of transmembrane helical structures using Rosetta modelling. We finally consider future sources of starting models which offer the hope that MR with AMPLE, in the absence of close homology between a known structure and the target, may soon be possible with larger proteins.

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34

Duong, Vy T., Elizabeth M. Diessner, Gianmarc Grazioli, Rachel W. Martin, and Carter T. Butts. "Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures." Biomolecules 11, no. 12 (November 30, 2021): 1788. http://dx.doi.org/10.3390/biom11121788.

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Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly useful way of obtaining highly compressed representations of molecular structures, and simulations operating via such representations can achieve substantial computational savings. A drawback of coarse-graining, however, is the loss of atomistic detail—an effect that is especially acute for topological representations such as protein structure networks (PSNs). Here, we introduce an approach based on a combination of machine learning and physically-guided refinement for inferring atomic coordinates from PSNs. This “neural upscaling” procedure exploits the constraints implied by PSNs on possible configurations, as well as differences in the likelihood of observing different configurations with the same PSN. Using a 1 μs atomistic molecular dynamics trajectory of Aβ1–40, we show that neural upscaling is able to effectively recapitulate detailed structural information for intrinsically disordered proteins, being particularly successful in recovering features such as transient secondary structure. These results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired, with upscaling employed to impute atomic coordinates from PSNs.

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35

Perkins, Stephen J., and Alexandra Bonner. "Structure determinations of human and chimaeric antibodies by solution scattering and constrained molecular modelling." Biochemical Society Transactions 36, no. 1 (January 22, 2008): 37–42. http://dx.doi.org/10.1042/bst0360037.

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X-ray and neutron scattering and analytical ultracentrifugation provide multiparameter structural and compositional information on proteins that complements high-resolution protein crystallography and NMR studies. They are ideal methods to use when either a large protein cannot be crystallized, when scattering provides the only means to obtain a solution structure, or the protein crystal structure has been determined and it is necessary to validate this. Once these results have been obtained, we apply automated constrained modelling methods based on known subunit crystal structures to identify the best-fit structure. Using our antibody structures as examples, we describe the generation of appropriate starting models, randomizing these for trial-and-error scattering fits, identifying the final best-fit models and interpreting these in terms of function. We discuss our structure determinations for IgA and IgD, an IgA–human serum albumin complex, the dimer of IgA and secretory component associated with this and chimaeras of mouse IgG with two complement proteins. Constrained modelling confirms the experimental data analysis and produces families of best-fit molecular models. Its usage has clarified several aspects of antibody structure and function in solution.

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36

Kmiecik, Sebastian, Maksim Kouza, Aleksandra Badaczewska-Dawid, Andrzej Kloczkowski, and Andrzej Kolinski. "Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models." International Journal of Molecular Sciences 19, no. 11 (November 6, 2018): 3496. http://dx.doi.org/10.3390/ijms19113496.

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Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

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37

Azzaz, Fodil, and Jacques Fantini. "The epigenetic dimension of protein structure." Biomolecular Concepts 13, no. 1 (January 1, 2022): 55–60. http://dx.doi.org/10.1515/bmc-2022-0006.

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Abstract Accurate prediction of protein structure is one of the most challenging goals of biology. The most recent achievement is AlphaFold, a machine learning method that has claimed to have solved the structure of almost all human proteins. This technological breakthrough has been compared to the sequencing of the human genome. However, this triumphal statement should be treated with caution, as we identified serious flaws in some AlphaFold models. Disordered regions are often represented by large loops that clash with the overall protein geometry, leading to unrealistic structures, especially for membrane proteins. In fact, AlphaFold comes up against the notion that protein folding is not solely determined by genomic information. We suggest that all parameters controlling the structure of a protein without being strictly encoded in its amino acid sequence should be coined “epigenetic dimension of protein structure.” Such parameters include for instance protein solvation by membrane lipids, or the structuration of disordered proteins upon ligand binding, but exclude sequence-encoded sites of post-translational modifications such as glycosylation. In our view, this paradigm is necessary to reconcile two opposite properties of living systems: beyond rigorous biological coding, evolution has given way to a certain level of uncertainty and anarchy.

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38

Chakravarty, S. "Accuracy of structure-derived properties in simple comparative models of protein structures." Nucleic Acids Research 33, no. 1 (January 7, 2005): 244–59. http://dx.doi.org/10.1093/nar/gki162.

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39

Guo, Yuzhi, Jiaxiang Wu, Hehuan Ma, and Junzhou Huang. "Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6801–9. http://dx.doi.org/10.1609/aaai.v36i6.20636.

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The protein tertiary structure largely determines its interaction with other molecules. Despite its importance in various structure-related tasks, fully-supervised data are often time-consuming and costly to obtain. Existing pre-training models mostly focus on amino-acid sequences or multiple sequence alignments, while the structural information is not yet exploited. In this paper, we propose a self-supervised pre-training model for learning structure embeddings from protein tertiary structures. Native protein structures are perturbed with random noise, and the pre-training model aims at estimating gradients over perturbed 3D structures. Specifically, we adopt SE(3)-invariant features as model inputs and reconstruct gradients over 3D coordinates with SE(3)-equivariance preserved. Such paradigm avoids the usage of sophisticated SE(3)-equivariant models, and dramatically improves the computational efficiency of pre-training models. We demonstrate the effectiveness of our pre-training model on two downstream tasks, protein structure quality assessment (QA) and protein-protein interaction (PPI) site prediction. Hierarchical structure embeddings are extracted to enhance corresponding prediction models. Extensive experiments indicate that such structure embeddings consistently improve the prediction accuracy for both downstream tasks.

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40

Fontove, Fernando, and Gabriel Del Rio. "Residue Cluster Classes: A Unified Protein Representation for Efficient Structural and Functional Classification." Entropy 22, no. 4 (April 20, 2020): 472. http://dx.doi.org/10.3390/e22040472.

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Proteins are characterized by their structures and functions, and these two fundamental aspects of proteins are assumed to be related. To model such a relationship, a single representation to model both protein structure and function would be convenient, yet so far, the most effective models for protein structure or function classification do not rely on the same protein representation. Here we provide a computationally efficient implementation for large datasets to calculate residue cluster classes (RCCs) from protein three-dimensional structures and show that such representations enable a random forest algorithm to effectively learn the structural and functional classifications of proteins, according to the CATH and Gene Ontology criteria, respectively. RCCs are derived from residue contact maps built from different distance criteria, and we show that 7 or 8 Å with or without amino acid side-chain atoms rendered the best classification models. The potential use of a unified representation of proteins is discussed and possible future areas for improvement and exploration are presented.

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Lampros, Christos, Thomas Simos, Themis P. Exarchos, Konstantinos P. Exarchos, Costas Papaloukas, and Dimitrios I. Fotiadis. "Assessment of optimized Markov models in protein fold classification." Journal of Bioinformatics and Computational Biology 12, no. 04 (August 2014): 1450016. http://dx.doi.org/10.1142/s0219720014500164.

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Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.

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MacCallum, Justin L., Alberto Perez, and Ken A. Dill. "Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference." Proceedings of the National Academy of Sciences 112, no. 22 (May 18, 2015): 6985–90. http://dx.doi.org/10.1073/pnas.1506788112.

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More than 100,000 protein structures are now known at atomic detail. However, far more are not yet known, particularly among large or complex proteins. Often, experimental information is only semireliable because it is uncertain, limited, or confusing in important ways. Some experiments give sparse information, some give ambiguous or nonspecific information, and others give uncertain information—where some is right, some is wrong, but we don’t know which. We describe a method called Modeling Employing Limited Data (MELD) that can harness such problematic information in a physics-based, Bayesian framework for improved structure determination. We apply MELD to eight proteins of known structure for which such problematic structural data are available, including a sparse NMR dataset, two ambiguous EPR datasets, and four uncertain datasets taken from sequence evolution data. MELD gives excellent structures, indicating its promise for experimental biomolecule structure determination where only semireliable data are available.

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Lin, Zeming, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." Science 379, no. 6637 (March 17, 2023): 1123–30. http://dx.doi.org/10.1126/science.ade2574.

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Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large language model. As language models of protein sequences are scaled up to 15 billion parameters, an atomic-resolution picture of protein structure emerges in the learned representations. This results in an order-of-magnitude acceleration of high-resolution structure prediction, which enables large-scale structural characterization of metagenomic proteins. We apply this capability to construct the ESM Metagenomic Atlas by predicting structures for >617 million metagenomic protein sequences, including >225 million that are predicted with high confidence, which gives a view into the vast breadth and diversity of natural proteins.

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Aubel, Margaux, Lars Eicholt, and Erich Bornberg-Bauer. "Assessing structure and disorder prediction tools for de novo emerged proteins in the age of machine learning." F1000Research 12 (March 29, 2023): 347. http://dx.doi.org/10.12688/f1000research.130443.1.

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Background: De novo protein coding genes emerge from scratch in the non-coding regions of the genome and have, per definition, no homology to other genes. Therefore, their encoded de novo proteins belong to the so-called "dark protein space". So far, only four de novo protein structures have been experimentally approximated. Low homology, presumed high disorder and limited structures result in low confidence structural predictions for de novo proteins in most cases. Here, we look at the most widely used structure and disorder predictors and assess their applicability for de novo emerged proteins. Since AlphaFold2 is based on the generation of multiple sequence alignments and was trained on solved structures of largely conserved and globular proteins, its performance on de novo proteins remains unknown. More recently, natural language models of proteins have been used for alignment-free structure predictions, potentially making them more suitable for de novo proteins than AlphaFold2. Methods: We applied different disorder predictors (IUPred3 short/long, flDPnn) and structure predictors, AlphaFold2 on the one hand and language-based models (Omegafold, ESMfold, RGN2) on the other hand, to four de novo proteins with experimental evidence on structure. We compared the resulting predictions between the different predictors as well as to the existing experimental evidence. Results: Results from IUPred, the most widely used disorder predictor, depend heavily on the choice of parameters and differ significantly from flDPnn which has been found to outperform most other predictors in a comparative assessment study recently. Similarly, different structure predictors yielded varying results and confidence scores for de novo proteins. Conclusions: We suggest that, while in some cases protein language model based approaches might be more accurate than AlphaFold2, the structure prediction of de novo emerged proteins remains a difficult task for any predictor, be it disorder or structure.

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GORIELY, ALAIN, ANDREW HAUSRATH, and SÉBASTIEN NEUKIRCH. "THE DIFFERENTIAL GEOMETRY OF PROTEINS AND ITS APPLICATIONS TO STRUCTURE DETERMINATION." Biophysical Reviews and Letters 03, no. 01n02 (April 2008): 77–101. http://dx.doi.org/10.1142/s1793048008000629.

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Understanding the three-dimensional structure of proteins is critical to understand their function. While great progress is being made in understanding the structures of soluble proteins, large classes of proteins such as membrane proteins, large macromolecular assemblies, and partially organized or heterogeneous structures are being comparatively neglected. Part of the difficulty is that the coordinate models we use to represent protein structure are discrete and static, whereas the molecules themselves are flexible and dynamic. In this article, we review methods to develop a continuous description of proteins more general than the traditional coordinate models and which can describe smooth changes in form. This description can be shown to be strictly equivalent to the traditional atomic coordinate description.

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Prajapat, Rajneesh, Avinash Marwal, and R. K. Gaur. "Recognition of Errors in the Refinement and Validation of Three-Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA-Web." Journal of Viruses 2014 (January 2, 2014): 1–6. http://dx.doi.org/10.1155/2014/752656.

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The structural model of begomovirus AC1 protein is useful for understanding biological function at molecular level and docking study. For this study we have used the ProSA program (Protein Structure Analysis) tool to establish the structure prediction and modeling of protein. This tool was used for refinement and validation of experimental protein structures. Potential problems of protein structures based on energy plots are easily seen by ProSA and are displayed in a three-dimensional manner. In the present study we have selected different AC1 proteins of begomovirus strains (YP_003288785, YP_002004579, and YP_003288773) for structural analysis and display of energy plots that highlight potential problems spotted in protein structures. The 3D models of Rep proteins with recognized errors can be effectively used for in silico docking study for development of potential ligand molecules against begomovirus infection.

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Rashid, Mahmood A., M. A. Hakim Newton, Md Tamjidul Hoque, and Abdul Sattar. "Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction." BioMed Research International 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/924137.

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Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution20×20energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.

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Adiyaman and McGuffin. "Methods for the Refinement of Protein Structure 3D Models." International Journal of Molecular Sciences 20, no. 9 (May 9, 2019): 2301. http://dx.doi.org/10.3390/ijms20092301.

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The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.

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Pieper, U. "MODBASE, a database of annotated comparative protein structure models." Nucleic Acids Research 30, no. 1 (January 1, 2002): 255–59. http://dx.doi.org/10.1093/nar/30.1.255.

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Sanchez, R. "MODBASE, a database of annotated comparative protein structure models." Nucleic Acids Research 28, no. 1 (January 1, 2000): 250–53. http://dx.doi.org/10.1093/nar/28.1.250.

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