Comparison of three Monte Carlo conformational search strategies for a proteinlike homopolymer model: Folding thermodynamics and identification of low-energy structures

Gront, Dominik; Koliński, Andrzej; Skolnick, Jeffrey

doi:10.1063/1.1289533

Cited by 68 publications

(75 citation statements)

References 25 publications

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“…In our simulations, temperature swaps were attempted after each cycle of MN attempted displacements (pass). The attempted swap of systems i and j between temperatures T m and T n , was accepted with the probability 49 :…”

Section: Ii2 Simulationmentioning

confidence: 99%

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

et al. 2006

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Sequences of contemporary proteins are believed to have evolved through process that optimized their overall fitness including their resistance to deleterious aggregation.Biotechnological processing may expose therapeutic proteins to conditions that are much more conducive to aggregation than those encountered in a cellular environment. An important task of protein engineering is to identify alternative sequences that would protect proteins when processed at high concentrations without altering their native structure associated with specific biological function. Our computational studies exploit parallel tempering simulations of coarse-grained model proteins to demonstrate that isolated amino-acid residue substitutions can result in significant changes in the aggregation resistance of the protein in a crowded environment while retaining protein structure in isolation. A thermodynamic analysis of protein clusters subject to competing processes of folding and association shows that moderate mutations can produce effects similar to those caused by changes in system conditions, including temperature, concentration, and solvent composition that affect the aggregation propensity. The range of conditions where a protein can resist aggregation can therefore be tuned by sequence alterations although the protein generally may retain its generic ability for aggregation. *dnb@berkeley.edu 2

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Section: Ii2 Simulationmentioning

confidence: 99%

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

et al. 2006

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“…This protocol has been shown to be more effective than the conventional simulated annealing in a simple Table 1. Predicted tertiary restraints and folding simulation results protein-like model (19). Fifty copies at different temperatures covering the entire folding transition region are used.…”

Section: Methodsmentioning

confidence: 99%

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

Kihara

Koliński

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

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139

113

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The successful prediction of protein structure from amino acid sequence requires two features: an efficient conformational search algorithm and an energy function with a global minimum in the native state. As a step toward addressing both issues, a threadingbased method of secondary and tertiary restraint prediction has been developed and applied to ab initio folding. Such restraints are derived by extracting consensus contacts and local secondary structure from at least weakly scoring structures that, in some cases, can lack any global similarity to the sequence of interest. Furthermore, to generate representative protein structures, a reduced lattice-based protein model is used with replica exchange Monte Carlo to explore conformational space. We report results on the application of this methodology, termed TOUCHSTONE, to 65 proteins whose lengths range from 39 to 146 residues. For 47 (40) proteins, a cluster centroid whose rms deviation from native is below 6.5 (5) Å is found in one of the five lowest energy centroids. The number of correctly predicted proteins increases to 50 when atomic detail is added and a knowledge-based atomic potential is combined with clustered and nonclustered structures for candidate selection. The combination of the ratio of the relative number of contacts to the protein length and the number of clusters generated by the folding algorithm is a reliable indicator of the likelihood of successful fold prediction, thereby opening the way for genome-scale ab initio folding.T he inability to predict routinely the tertiary structure of a protein from its amino acid sequence remains one of the most challenging unsolved problems in biophysics. Contemporary approaches to this problem can be divided roughly into three categories of increasing complexity: (i) homology modeling (1, 2), (ii) threading (3, 4), and (iii) ab initio folding (5-9). The first two methods use the structures of already solved proteins as templates. The third, the ab initio method, does not require that an example of the fold of the protein of interest be previously solved. In principle, such an approach is very powerful; however, significant unresolved issues remain. First, there are problems with the search algorithms used to explore the protein's conformational space (10). Second, the energy functions used to evaluate the fitness of a given conformation cannot, in general, distinguish the native structure from alternative, protein-like decoys (11). To compensate for the imperfections in the energy functions, another way of selecting representative folds is required, with clustering of the structures being a promising approach (7-9). Finally, for a folding algorithm to be practical, one has to develop criteria that allow one to estimate the likelihood that a given prediction will be successful.In this article, we address each of these issues and present the results on the application of our ab initio method to a representative 65-protein test set. To restrict the protein's conformational space, we employ the SICHO (SId...

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“…Several advanced techniques have been proposed in attempts to improve simulations by smoothing out such landscapes. [1][2][3][4][5][6][7] These include parallel tempering, umbrella sampling and generalized ensemble techniques. 8 Multicanonical methods are based on nonBoltzmann probability distributions and are particularly attractive in that they rely on the idea of artificially eliminating energy barriers, thereby circumventing some of the problems associated with traditional sampling techniques ͑e.g., trapping in local free energy minima͒.…”

Section: Introductionmentioning

confidence: 99%

Density of states simulations of proteins

Rathore

Knotts

Pablo

2003

The Journal of Chemical Physics

102

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A modified version of a recently introduced algorithm that calculates density of states by performing a random walk in energy space has been proposed and implemented to study protein folding in a continuum. A united atom representation and the CHARMM19 ͓B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S. Swaminathan, and M. Karplus, J. Comput. Chem. 4, 187 ͑1983͔͒ force field are employed for these simulations. This method permits estimation of the density of states of a protein via a random walk in the energy space, thereby allowing the system to escape from local free-energy minima with relative ease. Unlike the earlier formulation that showed slow convergence for continuum simulations, this methodology is designed to achieve better sampling and faster convergence. The modified method is used to examine folding transitions of two peptides: deca-alanine and Met-enkephalin. Protein folding both with and without an implicit solvent ͑solvent accessible surface area model͒ has been studied to validate the usefulness of the proposed algorithm.

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Comparison of three Monte Carlo conformational search strategies for a proteinlike homopolymer model: Folding thermodynamics and identification of low-energy structures

Cited by 68 publications

References 25 publications

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

Effect of Single-Point Sequence Alterations on the Aggregation Propensity of a Model Protein

TOUCHSTONE: An ab initio protein structure prediction method that uses threading-based tertiary restraints

Density of states simulations of proteins

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