Literature DB >> 11746699

Probabilistic sampling of protein conformations: new hope for brute force?

Howard J Feldman1, Christopher W V Hogue.   

Abstract

Protein structure prediction from sequence alone by "brute force" random methods is a computationally expensive problem. Estimates have suggested that it could take all the computers in the world longer than the age of the universe to compute the structure of a single 200-residue protein. Here we investigate the use of a faster version of our FOLDTRAJ probabilistic all-atom protein-structure-sampling algorithm. We have improved the method so that it is now over twenty times faster than originally reported, and capable of rapidly sampling conformational space without lattices. It uses geometrical constraints and a Leonard-Jones type potential for self-avoidance. We have also implemented a novel method to add secondary structure-prediction information to make protein-like amounts of secondary structure in sampled structures. In a set of 100,000 probabilistic conformers of 1VII, 1ENH, and 1PMC generated, the structures with smallest Calpha RMSD from native are 3.95, 5.12, and 5.95A, respectively. Expanding this test to a set of 17 distinct protein folds, we find that all-helical structures are "hit" by brute force more frequently than beta or mixed structures. For small helical proteins or very small non-helical ones, this approach should have a "hit" close enough to detect with a good scoring function in a pool of several million conformers. By fitting the distribution of RMSDs from the native state of each of the 17 sets of conformers to the extreme value distribution, we are able to estimate the size of conformational space for each. With a 0.5A RMSD cutoff, the number of conformers is roughly 2N where N is the number of residues in the protein. This is smaller than previous estimates, indicating an average of only two possible conformations per residue when sterics are accounted for. Our method reduces the effective number of conformations available at each residue by probabilistic bias, without requiring any particular discretization of residue conformational space, and is the fastest method of its kind. With computer speeds doubling every 18 months and parallel and distributed computing becoming more practical, the brute force approach to protein structure prediction may yet have some hope in the near future. Copyright 2001 Wiley-Liss, Inc.

Mesh:

Year:  2002        PMID: 11746699

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  29 in total

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Journal:  Protein Sci       Date:  2003-06       Impact factor: 6.725

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4.  A probabilistic and continuous model of protein conformational space for template-free modeling.

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5.  An information theoretic approach to macromolecular modeling: II. Force fields.

Authors:  Tiba Aynechi; Irwin D Kuntz
Journal:  Biophys J       Date:  2005-11       Impact factor: 4.033

6.  Paramagnetic relaxation enhancements in unfolded proteins: theory and application to drkN SH3 domain.

Authors:  Yi Xue; Ivan S Podkorytov; D Krishna Rao; Nathan Benjamin; Honglei Sun; Nikolai R Skrynnikov
Journal:  Protein Sci       Date:  2009-07       Impact factor: 6.725

7.  Using chemical shifts to generate structural ensembles for intrinsically disordered proteins with converged distributions of secondary structure.

Authors:  F Marty Ytreberg; Wade Borcherds; Hongwei Wu; Gary W Daughdrill
Journal:  Intrinsically Disord Proteins       Date:  2015-02-03

8.  Atomistic Modeling of Intrinsically Disordered Proteins Under Polyethylene Glycol Crowding: Quantitative Comparison with Experimental Data and Implication of Protein-Crowder Attraction.

Authors:  Valery Nguemaha; Sanbo Qin; Huan-Xiang Zhou
Journal:  J Phys Chem B       Date:  2018-10-03       Impact factor: 2.991

9.  Deciphering the "Fuzzy" Interaction of FG Nucleoporins and Transport Factors Using Small-Angle Neutron Scattering.

Authors:  Samuel Sparks; Deniz B Temel; Michael P Rout; David Cowburn
Journal:  Structure       Date:  2018-02-08       Impact factor: 5.006

10.  A Probabilistic Graphical Model for Ab Initio Folding.

Authors:  Feng Zhao; Jian Peng; Joe Debartolo; Karl F Freed; Tobin R Sosnick; Jinbo Xu
Journal:  Res Comput Mol Biol       Date:  2009
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