Abstract
Proteins are vital to most biological processes by performing a variety of functions. Structure and function are intimately related, thus highlighting the importance of predicting a proteins 3-D conformation. We propose GMASTERS, a multiagent tool to address the protein structure prediction (PSP) problem. GMASTERS is a general-purpose ab initio graphical program based on cooperative agents that explore the protein conformational space using Monte Carlo and Simulated Annealing methods. The user can choose the abstraction level, energy function and force field to perform simulations. Because bioinformatics demands knowledge from diverse scientific fields, its tools are intrinsically complex. GMASTERS abstracts away some of this complexity while still allowing the user to learn and explore research hypotheses with the advantage of an embedded graphical interface. Although this abstraction comes at a cost, its performance is similar to state-of-the-art methods. Here, we describe GMASTERS and how to use it to explore the PSP problem.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lesk, A.M.: Introduction to Bioinformatics, 3rd edn. Oxford University Press, Oxford (2008)
Clark, K., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Sayers, E.W.: Genbank. Nucleic Acids Res. 44, D67–D72 (2016)
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000)
Pavlopoulou, A., Michalopoulos, I.: State-of-the-art bioinformatics protein structure prediction tools. Int. J. Mol. Med. 28(3), 295–310 (2011)
Dill, K., MacCallum, J.: The protein-folding problem, 50 years on. Science 338(6110), 1042–1046 (2012)
Duan, Y., Kollman, P.A.: Computational protein folding: from lattice to all-atom. IBM Syst. J. 40(297–309), 0018–8670 (2001)
Amigoni, F., Schiaffonati, V.: Multiagent-based simulation in biology. In: Magnani, L., Li, P. (eds.) Model-based Reasoning in Science, Technology, and Medicine, SCI, vol. 64, pp. 179–191. Springer, Heidelberg (2007)
Tisseau, J.: Virtual reality, in virtuo autonomy. Ph.D. thesis, University of Rennes 1 (2001)
Nelson, D., Cox, M.: Lehninger Principles of Biochemistry, 5th edn. W. H. Freeman and Company, New York (2008)
Snow, C.D., Sorin, E.J., Rhee, Y.M., Pande, V.S.: How well can simulation predict protein folding kinetics and thermodynamics? Annu. Rev. Biophys. 34, 43–69 (2005). Annual Reviews, Palo Alto
Zvelebil, M., Baum, J.: Understanding Bioinformatics. Garland Science, US (2007)
Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding. J. Comput. Biol. 5, 597–603 (1998)
Levinthal, C.: Are there pathways for protein folding? J. Med. Phys. 65(1), 44–45 (1968)
Tramontano, A.: Integral and differential form of the protein folding problem. Phys. Life Rev. 1(2), 103–127 (2004)
Helles, G.: A comparative study of the reported performance of ab initio protein structure prediction algorithms. J. Roy. Soc. Interface 5(21), 387–396 (2008)
Wooldridge, M., Jennings, N.: Intelligent agents - theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2003)
Bradshaw, J.M.: An introduction to software agents. In: Bradshaw, J.M. (ed.) Software Agents, pp. 3–46. AAAI Press / The MIT Press, Cambridge (1997)
Tisue, S., Wilensky, U.: Netlogo: a simple environment for modeling complexity (2004)
Lipinski-Paes, T., Norberto de Souza, O.: MASTERS: a general sequence-based multiagent system for protein tertiary structure prediction. Electron. Notes Theor. Comput. Sci. 306, 45–59 (2014)
Osguthorpe, D.J.: Ab initio protein folding. Curr. Opin. Struct. Biol. 10(2), 146–152 (2000)
Roli, A., Milano, M.: MAGMA: a multiagent architecture for metaheuristics. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 34, 925–941 (2004)
Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)
Allen, P., Tildesley, D.: Computer Simulation of Liquids. Oxford science publications, Clarendon Press (1987)
Schrödinger, L.L.C.: The PyMOL molecular graphics system, February 2016
Bachega, J.F.R., Timmers, L.F.S.M., Assirati, L., Bachega, L.R., Field, M.J., Wymore, T.: GTKDynamo: A PyMOL plug-in for QC/MM hybrid potential simulations. J. Comput. Chem. 34(25), 2190–2196 (2013)
Dill, K.: Theory for the folding and stability of globular-proteins. Biochemistry 24(6), 1501–1509 (1985)
William, E.H., Alantha, N.: Protein structure prediction with lattice models. Chapman & Hall/CRC Computer & Information Science Series, pp. 30-1–30-24. Chapman and Hall/CRC (2005)
Berger, B., Leighton, T.: Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. J. Comput. Biol. 5(1), 27–40 (1998)
Bachmann, M., Arkin, H., Janke, W.: Multicanonical study of coarse-grained off-lattice models for folding heteropolymers. Phys. Rev. E Stat. Nonlin Soft Matter Phys. 71(3), 031906 (2005). doi:10.1103/PhysRevE.71.031906
Hsu, H.P., Mehra, V., Grassberger, P.: Structure optimization in an off-lattice protein model. Physical Review E 68(3), 037703 (2003). doi:10.1103/PhysRevE.68.037703
Stillinger, F., Head-Gordon, T.: Collective aspects of protein-folding illustrated by a toy model. Phys. Rev. E 52(3), 2872–2877 (1995)
Irback, A., Peterson, C., Potthast, F., Sommelius, O.: Local interactions and protein folding: a three-dimensional off-lattice approach. J. Chem. Phys. 107(1), 273–282 (1997)
Stillinger, F., Head-Gordon, T., Hirshfeld, C.: Toy model for protein-folding. Phys. Rev. E 48(2), 1469–1477 (1993)
Krezel, A.M., Kasibhatla, C., Hidalgo, P., Mackinnon, R., Wagner, G.: Solution structure of the potassium channel inhibitor agitoxin 2: Caliper for probing channel geometry. Protein Sci. 4(8), 1478–1489 (1995)
Acknowledgements
This work was supported by grants CNPq-305984/20128 and FAPERGS-TO2054-2551/13-0 to ONS, a CNPq Research Fellow. TLP and MSS are supported by CAPES, JFRB by a CAPES/FAPERGS DOCFIX postdoctoral fellowship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lipinski-Paes, T., Tanus, M.d.S.d.S., Bachega, J.F.R., Norberto de Souza, O. (2016). A Multiagent Ab Initio Protein Structure Prediction Tool for Novices and Experts. In: Bourgeois, A., Skums, P., Wan, X., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2016. Lecture Notes in Computer Science(), vol 9683. Springer, Cham. https://doi.org/10.1007/978-3-319-38782-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-38782-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-38781-9
Online ISBN: 978-3-319-38782-6
eBook Packages: Computer ScienceComputer Science (R0)