Abstract
Collective behavior involving distally separate regions in a protein is known to widely affect its function. In this paper, we present an online approach to study and characterize collective behavior in proteins as molecular dynamics simulations progress. Our representation of MD simulations as a stream of continuously evolving data allows us to succinctly capture spatial and temporal dependencies that may exist and analyze them efficiently using data mining techniques. By using multi-way analysis we identify (a) parts of the protein that are dynamically coupled, (b) constrained residues/ hinge sites that may potentially affect protein function and (c) time-points during the simulation where significant deviation in collective behavior occurred. We demonstrate the applicability of this method on two different protein simulations for barnase and cyclophilin A. For both these proteins we were able to identify constrained/ flexible regions, showing good agreement with experimental results and prior computational work. Similarly, for the two simulations, we were able to identify time windows where there were significant structural deviations. Of these time-windows, for both proteins, over 70% show collective displacements in two or more functionally relevant regions. Taken together, our results indicate that multi-way analysis techniques can be used to analyze protein dynamics and may be an attractive means to automatically track and monitor molecular dynamics simulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R., Yener, B.: Multiway analysis of epilepsy tensors. Bioinformatics 23(13), i10–i18 (2007)
Agarwal, P.K.: Cis/trans isomerization in hiv-1 capsid protein catalyzed by cyclophilin a: Insights from computational and theoretical studies. Proteins: Struct., Funct., Bioinformatics 56, 449–463 (2004)
Agarwal, P.K.: Enzymes: An integrated view of structure, dynamics and function. Microbial Cell Factories 5 (2006)
Agarwal, P.K., Billeter, S.R., Rajagopalan, P.T.R., Hammes-Schiffer, S., Benkovic, S.J.: Network of coupled promoting motions in enzyme catalysis. Proc. Natl. Acad. Sci. USA 99, 2794–2799 (2002)
Agarwal, P.K., Geist, A., Gorin, A.: Protein dynamics and enzymatic catalysis: Investigating the peptidyl-prolyl cis-trans isomerization activity of cyclophilin a. Biochemistry 43(33), 10605–10618 (2004)
Atilgan, A.R., Durell, S.R., Jernigan, R.L., Demirel, M.C., Keskin, O., Bahar, I.: Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80, 505–515 (2001)
Bader, B.W., Kolda, T.G.: Algorithm 862: MATLAB tensor classes for fast algorithm prototyping. ACM Transactions on Mathematical Software 32(4), 635–653 (2006)
Bader, B.W., Kolda, T.G.: Efficient MATLAB computations with sparse and factored tensors. SIAM Journal on Scientific Computing 30(1), 205–231 (2007)
Bahar, I., Atilgan, A.R., Demirel, M.C., Erman, B.: Vibrational dynamics of folded proteins. significance of slow and fast modes in relation to function and stability. Phys. Rev. Lett. 80, 2733–2736 (1998)
Bahar, I., Cui, Q.: Normal Mode Analysis: Theory and Applications to Biological and Chemical Systems. Mathematical and Computational Biology Series. Chapman and Hall/ CRC, New York (2003)
Bahar, I., Rader, A.J.: Coarse grained normal mode analysis in structural biology. Cur. Op. Struct. Biol. 15, 1–7 (2005)
Beazley, D.M., Lomdahl, P.S.: Lightweight computational steering of very large scale molecular dynamics simulations. In: Supercomputing 1996 proceedings of the 1996 ACM/IEEE conference on Supercomputing (CDROM), Washington, DC, USA, p. 50. IEEE Computer Society Press, Los Alamitos (1996)
Berendsen, H.J.C., Hayward, S.: Collective protein dynamics in relation to function. Current Opinion in Structural Biology 10(2), 165–169 (2000)
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 Research 28, 235–242 (2002)
Bowers, K.J., Chow, E., Xu, H., Dror, R.O., Eastwood, M.P., Gregersen, B.A., Klepeis, J.L., Kolossvary, I., Moraes, M.A., Sacerdoti, F.D., Salmon, J.K., Shan, Y., Shaw, D.E.: Scalable algorithms for molecular dynamics simulations on commodity clusters. In: SC Conference, p. 43 (2006)
Chodera, J.D., Singhal, N., Pander, V.S., Dill, K.A., Swope, W.C.: Automatic discovery of metastable states for the construction of markov models of macromolecular conformational dynamics. J. Chem. Phys. 126, 155101 (2007)
DeLano, W.L.: The pymol molecular graphics system (2003)
Eisenmesser, E.Z., Bosco, D.A., Akke, M., Kern, D.: Enzyme dynamics during catalysis. Science 295(5559), 1520–1523 (2002)
Fersht, A.R., Daggett, V.: Protein folding and unfolding at atomic resolution. Cell 108(4), 573–582 (2002)
Fersht, A.R., Matouschek, A., Sancho, J., Serrano, L., Vuilleumier, S.: Pathway of protein folding. Faraday Discuss 93, 183–193 (1992)
Fersht, A.R.: Protein folding and stability: the pathway of folding of barnase. FEBS Letters 325(1-2), 5–16 (1993)
Gerstein, M., Krebs, W.: A database of macromolecular motions. Nucl. Acids Res. 26(18), 4280–4290 (1998)
Gu, W., Eisenhauer, G., Kraemer, E., Schwan, K., Stasko, J., Vetter, J., Mallavarupu, N.: Falcon: on-line monitoring and steering of large-scale parallel programs. In: Symposium on the Frontiers of Massively Parallel Processing, p. 422 (1995)
Gussio, R., Pattabiraman, N., Kellogg, G.E., Zaharevitz, D.W.: Use of 3d qsar methodology for data mining the national cancer institute repository of small molecules: Application to hiv-1 reverse transcriptase inhibition. Methods 14, 255–263 (1998)
Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. App. Stat. 28(1), 100–108 (1979)
Hayward, S., Go, N.: Collective variable description of native protein dynamics. Annual Review of Physical Chemistry 46(1), 223–250 (1995)
Hespenheide, B.M., Rader, A.J., Thorpe, M.F., Kuhn, L.A.: Identifying protein folding cores: observing the evolution of rigid and flexible regions during unfolding. J. Mol. Graph. and Model. 21, 195–207 (2002)
Jacobs, D.J., Rader, A.J., Kuhn, L.A., Thorpe, M.F.: Protein flexibility predictions using graph theory. Proteins: Struct., Funct., Genet. 44(2), 150–165 (2001)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)
Karplus, M., McCammon, J.A.: Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646–652 (2002)
Karplus, M., Kushick, J.N.: Method for estimating the configurational entropy of macromolecules. Macromolecules 14(2), 325–332 (1981)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. Technical report, Sandia National Laboratories (2007)
Lange, O.F., Grubmuller, H.: Full correlation analysis of conformational protein dynamics. Proteins: Struct., Funct. and Bioinformatics 70, 1294–1312 (2008)
Lenaerts, T., Ferkinghoff-Borg, J., Stricher, F., Serrano, L., Schymkowitz, J.W.H., Rousseau, F.: Quantifying information transfer by protein domains: Analysis of the fyn sh2 domain structure. BMC Struct. Biol. 8, 43 (2008)
Malmodin, D., Billeter, M.: Multiway decomposition of nmr spectra with coupled evolution periods. J. Am. Chem. Soc. 127(39), 13486–13487 (2005)
Mamonova, T., Hespenheide, B., Straub, R., Thorpe, M.F., Kurnikova, M.: Protein flexibility using constraints from molecular dynamics simulations. Phys. Biol. 2(4), S137–S147 (2005)
Nolde, S.B., Arseniev, A.S., Yu, V., Billeter, M.: Essential domain motions in barnase revealed by md simulations. Proteins: Struct., Funct. and Bioinformatics 46(3), 250–258 (2003)
Shao, J., Tanner, S.W., Thompson, N., Cheatham, T.E.: Clustering molecular dynamics trajectories: 1. characterizing the performance of different clustering algorithms. Journal of Chemical Theory and Computation 3(6), 2312–2334 (2007)
Smilde, A., Bro, R., Geladi, P.: Multi-way Analysis: Applications in the Chemical Sciences. J. Wiley and Sons, Ltd., Chichester (2004)
Staykova, D., Fredriksson, J., Bermel, W., Billeter, M.A: ssignment of protein nmr spectra based on projections, multi-way decomposition and a fast correlation approach. Journal of Biomolecular NMR (2008)
Suel, G.M., Lockless, S.W., Wall, M.A., Ranganathan, R.: Evolutionarily conserved networks of residues mediate allosteric communication in proteins. Nat. Struct. Biol. 10, 59–69 (2003)
Sun, J., Tao, D., Faloutsos, C.: Beyond streams and graphs: Dynamic tensor analysis (2006)
Tao, D., Li, X., Wu, X., Hu, W., Stephen, J.M.: Supervised tensor learning. Knowledge and Information Systems 13, 42 (2007)
Whiteley, W.: Rigidity of Molecular structures: generic and geometric analysis. In: Rigidity Theory and Applications. Kluwer Academic/ Plenum, New York (1999)
Yanagawa, H., Yoshida, K., Torigoe, C., Park, J.S., Sato, K., Shirai, T., Go, M.: Protein anatomy: functional roles of barnase module. J. Biol. Chem. 268(8), 5861–5865 (1993)
Yener, B., Acar, E., Aguis, P., Bennett, K., Vandenberg, S., Plopper, G.: Multiway modeling and analysis in stem cell systems biology. BMC Systems Biology 2(1), 63 (2008)
Zavodszky, M.I., Lei, M., Thorpe, M.F., Day, A.R., Kuhn, L.A.: Modeling correlated main-chain motions in proteins for flexible molecular recognition. Proteins: Struct. Funct. and Bioinformatics 57(2), 243–261 (2004)
Zhuravleva, A., Korzhnev, D.M., Nolde, S.B., Kay, L.E., Arseniev, A.S., Billeter, M., Orekhov, V.Y.: Propagation of dynamic changes in barnase upon binding of barstar: An nmr and computational study. Journal of Molecular Biology 367(4), 1079–1092 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramanathan, A., Agarwal, P.K., Kurnikova, M., Langmead, C.J. (2009). An Online Approach for Mining Collective Behaviors from Molecular Dynamics Simulations. In: Batzoglou, S. (eds) Research in Computational Molecular Biology. RECOMB 2009. Lecture Notes in Computer Science(), vol 5541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02008-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-02008-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02007-0
Online ISBN: 978-3-642-02008-7
eBook Packages: Computer ScienceComputer Science (R0)