Efficient Traversal of Beta-Sheet Protein Folding Pathways Using Ensemble Models
Molecular Dynamics (MD) simulations can now predict ms-timescale folding processes of small proteins — however, this presently requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions. Larger and slower-folding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary. Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then model the transition from random coil to native state as a Markov process, using a master equation to simulate population dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction, we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied, while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.
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- 8.Hosur, R., Singh, R., Berger, B.: Sparse estimation for structural variability. Algorithms Mol. Biol. (2011)Google Scholar
- 12.Wolfinger, M.T., Andreas Svrcek-Seiler, W.A., Flamm, C., Hofacker, I.L., Stadler, P.F.: Efficient computation of RNA folding dynamics. Journal of Physics A: Mathematical and General 37(17) (2004)Google Scholar
- 14.Mamitsuka, H., Abe, N.: Predicting location and structure of beta-sheet regions using stochastic tree grammars. In: ISMB, pp. 276–284 (1994)Google Scholar
- 17.Tran, V.D., Chassignet, P., Sheikh, S., Steyaert, J.M.: Energy-based classification and structure prediction of transmembrane beta-barrel proteins. In: Proceedings of the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS) (2011)Google Scholar
- 21.Cowen, L., Bradley, P., Menke, M., King, J., Berger, B.: Predicting the beta-helix fold from protein sequence data. J. Comput. Bio.l, 261–276 (2001)Google Scholar
- 26.Song, G., Thomas, S., Dill, K.A., Scholtz, J.M., Amato, N.M.: A path planning-based study of protein folding with a case study of hairpin formation in protein G and L. Pac. Symp. Biocomput., 240–251 (2003)Google Scholar