J. Comput. Biol. - Efficient traversal of beta-sheet protein folding pathways using ensemble models.

Tópicos

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Resumo

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 ?-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 ?-sheet proteins using sequence data alone. To do so, we extend existing ensemble ?-sheet prediction techniques, which permitted only a fixed anti-parallel ?-barrel shape, with a method that predicts arbitrary ?-strand/?-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.

Resumo Limpo

molecular dynam md simul can now predict mstimescal fold process small protein howev present requir hundr thousand cpu hour primarili applic short peptid longrang interact larger slowerfold protein mani extend sheet structur requir order magnitud time comput resourc furthermor object determin fold event necessari limit atomist detail md simul can prove unnecessari introduc program tfolder effici method model fold process larg sheet protein use sequenc data alon extend exist ensembl sheet predict techniqu permit fix antiparallel barrel shape method predict arbitrari strandstrand orient strandord permut account partial final structur state can model transit random coil nativ state markov process use master equat simul popul dynam fold time thus putat fold pathway can energet score includ transit present greatest barrier sinc correct fold pathway predict like determin accuraci contact predict demonstr accuraci tfolder compar stateoftheart method design specif contact predict problem alon valid method dynam predict appli fold pathway wellstudi protein g relat littl comput time tfolder abl reveal critic featur fold pathway previous observ timeconsum md simul experiment studi result great expand number protein whose fold pathway can studi algorithm integr ensembl predict markovian dynam can appli mani problem

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