Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation
Tóm tắt
Protein folding is the dynamic process by which a protein folds into its final native structure. This is different to the traditional problem of the prediction of the final protein structure, since it requires a modeling of how protein components interact over time to obtain the final folded structure. In this study we test whether a model of the folding process can be obtained exclusively through machine learning. To this end, protein folding is considered as an emergent process and the cellular automata tool is used to model the folding process. A neural cellular automaton is defined, using a connectionist model that acts as a cellular automaton through the protein chain to define the dynamic folding. Differential evolution is used to automatically obtain the optimized neural cellular automata that provide protein folding. We tested the methods with the Rosetta coarse-grained atomic model of protein representation, using different proteins to analyze the modeling of folding and the structure refinement that the modeling can provide, showing the potential advantages that such methods offer, but also difficulties that arise.
Tài liệu tham khảo
C. Anfinsen, Principles that govern the folding of proteins. Science 181(96), 223–230 (1973)
R. Calabretta, S. Nolfi, D. Parisi, An artificial life model for predicting the tertiary structure of unknown proteins that emulates the folding process, in Proceedings Third European Conference on Advances in Artificial Life - LNCS Vol 929, (1995), pp. 862–875
E. Callaway, The protein-imaging technique taking over structural biology. Nature 578, 201 (2020)
G. Danks, S. Stepney, L. Caves, Protein folding with stochastic L-systems, in Artificial Life XI: Proceedings of 11th International Conference on the Simulation and Synthesis of Living Systems (MIT Press, 2008), pp. 150–157
S. Das, P. Suganthan, Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)
S. Englander, L. Mayne, The nature of protein folding pathways. Proc. Natl. Acad. Sci. 111(45), 15873–15880 (2014)
M. Feig, V. Mirjalili, Protein structure refinement via molecular-dynamics simulations: what works and what does not? Proteins Suppl 1, 282–292 (2016)
V. Feoktistov, Differential evolution: In search of solutions (Springer, Berlin, 2006)
M. Garza-Fabre, S. Kandathil, J. Handl, J. Knowles, S. Lovell, Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction. Evol. Comput. 24(4), 577–607 (2016)
A. Hagler, S. Lifson, Energy functions for peptides and proteins, II: the amide hydrogen bond and calculation of amide crystal properties. J. Am. Chem. Soc. 96, 5319–5327 (1974)
A. Ilachinski, Cellular automata. A discrete universe (World Scientific, Singapore, 2001)
K. Kaufmann, G. Lemmon, S. DeLuca, J. Sheehan, J. Meiler, Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry 49, 2987–2998 (2010)
S. Kmiecik, D. Gront, M. Kolinski, L. Wieteska, A. Dawid, A. Kolinski, Coarse-grained protein models and their applications. Chem. Rev. 116, 7898–7936 (2016)
N. Krasnogor, G. Terrazas, D. Pelta, G. Ochoa, A critical view of the evolutionary design of self-assembling systems. Proceedings of the 2005 Conference on Artificial Evolution, LNCS 3871, 179–188 (2006)
S. Kriegman, N. Cheney, J. Bongard, How morphological development can guide evolution. Sci. Rep. 8, 13934 (2018)
V. Krishnan, B. Rupp, Macromolecular structure determination: comparison of X-ray crystallography and NMR spectroscopy (Wliley, Hoboken, 2012). https://doi.org/10.1002/9780470015902.a0002716.pub2
J. Lee, S. Wu, Y. Zhang, Ab initio protein structure prediction, in From Protein Structure to Function with Bioinformatics (Springer, London, 2009), pp. 3–25
C. Levinthal, Are there pathways for protein folding? J. Chim. Phys. 65, 44–45 (1968)
A. Márquez-Chamorro, G. Asencio-Cortés, C. Santiesteban-Toca, J. Aguilar-Ruiz, Soft computing methods for the prediction of protein tertiary structures: a survey. Appl. Soft Comput. 35, 398–410 (2015)
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, E. Teller, Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, A. Tramontano, Critical assessment of methods of protein structure prediction: progress and new directions in round XI. Proteins: Struct., Funct., Bioinform. 84(1), 4–14 (2016)
P. Narloch, M. Dorn, A knowledge based differential evolution algorithm for protein structure prediction, in Proceedings International Conference on the Applications of Evolutionary Computation, pp. 343–359 (2019)
F. Noé, G. De-Fabritiis, C. Clementi, Machine learning for protein folding and dynamics. Current Opin. Struct. Biol. 60, 77–84 (2020)
B. Olson, K. De-Jong, A. Shehu, Off-lattice protein structure prediction with homologous crossover, in Proceedings Conference on Genetic and Evolutionary Computation - GECCO 2013 (2013) pp. 287–294
S. Patodia, A. Bagaria, D. Chopra, Molecular dynamics simulation of proteins: a brief overview. J. Phys. Chem. Biophys. 4(6), 166 (2014)
Protein Data Bank. http://www.wwpdb.org
K. Price, R. Storn, J. Lampinen, Differential evolution. A practical approach to global optimization (Springer, Berlin, 2005)
PSIPRED protein sequence analysis workbench. http://bioinf.cs.ucl.ac.uk/psipred/
C. Rohl, C. Strauss, K. Misura, D. Baker, Protein structure prediction using Rosetta. Methods Enzymol. 383, 66–93 (2004)
Rosetta system. http://www.rosettacommons.org
J. Santos, M. Diéguez, Differential evolution for protein structure prediction using the HP model. Lecture Notes Comput. Sci. 6686, 323–323 (2011)
J. Santos, P. Villot, M. Diéguez, Cellular automata for modeling protein folding using the HP model, in Proceedings IEEE Congress on Evolutionary Computation - IEEE-CEC 2013 pp. 1586–1593 (2013)
J. Santos, P. Villot, M. Diéguez, Protein folding with cellular automata in the 3D HP model, in ACM Proceedings International Workshop on Evolutionary Computation in Bioinformatics - BIO 2013 - Genetic and Evolutionary Computation Conference (GECCO 2013), pp. 1595–1602 (2013)
J. Santos, P. Villot, M. Diéguez, Emergent protein folding modeled with evolved neural cellular automata using the 3D HP model. J. Comput. Biol. 21(11), 823–845 (2014)
E. Sapin, A. Shehu, K. De Jong, An evolutionary algorithm to model structural excursions of a protein, in Proceedings of the ACM Workshop Evolutionary Computation in Computational Biology, GECCO-Genetic and Evolutionary Computation Conference (2017), pp. 1669–1673
A. Shmygelska, M. Levitt, Generalized ensemble methods for de novo structure prediction. PNAS 106(5), 1415–1420 (2009)
Software to model the protein folding process with Rosetta. https://github.com/danielvarela/ProteinFoldCA
A. Tramontano, Protein structure prediction. Concepts and applications (Wiley, Hoboken, 2006)
R. Unger, The genetic algorithm approach to protein structure prediction. Struct. Bond. 110, 153–175 (2004)
Universal protein resource (uniprot). https://www.uniprot.org
D. Varela, J. Santos, Protein folding modeling with neural cellular automata using Rosetta, in GECCO 2016 ACM Proceedings Companion, Workshop Evolutionary Computation in Computational Structural Biology pp. 1307–1312 (2016)
D. Varela, J. Santos, A hybrid evolutionary algorithm for protein structure prediction using the Face-Centered Cubic lattice model, in Proceedings International Conference on Neural Information Processing - ICONIP 2017, Lecture Notes in Computer Science 10634 pp. 628–638 (2017)
D. Varela, J. Santos, Automatically obtaining a cellular automaton scheme for modeling protein folding using the FCC model. Nat. Comput. 18, 275–284 (2019)
D. Varela, J. Santos, Protein structure prediction in an atomic model with differential evolution integrated with the crowding niching method. Nat. Comput. (2020). https://doi.org/10.1007/s11047-020-09801-7
P. Wolynes, J. Onuchic, D. Thirumalai, Navigating the folding routes. Science 267, 1619–1620 (1995)
X. Zhao, Advances on protein folding simulations based on the lattice HP models with natural computing. Appl. Soft Comput. 8, 1029–1040 (2008)