Effective pair potentials between protein amino acids

Pliego-Pastrana, Patricia; Carbajal-Tinoco, Mauricio D.

doi:10.1103/physreve.68.011903

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…In recent years, there has been rapidly growing interest in the coarse-grained (CG) modeling of polymers (1), lipids (2,3), and proteins (4)(5)(6)(7). In biological systems, many phenomena such as protein folding and peptide aggregation occur on long timescales and may involve large lengthscales.…”

Section: Introductionmentioning

confidence: 99%

“…Other approaches to producing CG models include the generation of knowledge-based statistical potentials by using frequency distributions of pair-distances to extract effective potentials between residues. For example, Pliego-Pastrana et al used 196 crystal structures to derive the average radial distribution function (RDF) for the centroid of alanine (5). They then used this RDF to determine an effective potential for alanine through the Ornstein-Zernike equation with an appropriate closure approximation.…”

Section: Introductionmentioning

confidence: 99%

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Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach

Jian

Thorpe

Izvekov

et al. 2007

Biophysical Journal

182

215

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A systematic new approach to derive multiscale coarse-grained (MS-CG) models has been recently developed. The approach employs information from atomistically detailed simulations to derive CG forces and associated effective potentials. In this work, the MS-CG methodology is extended to study two peptides representing distinct structural motifs, alpha-helical polyalanine and the beta-hairpin V(5)PGV(5). These studies represent the first known application of this approach to peptide systems. Good agreement between the MS-CG and atomistic models is achieved for several structural properties including radial distribution functions, root mean-square deviation, and radius of gyration. The new MS-CG models are able to preserve the native states of these peptides within approximately 1 A backbone root mean-square deviation during CG simulations. The MS-CG approach, as with most coarse-grained models, has the potential to increase the length and timescales accessible to molecular simulations. However, it is also able to maintain a clear connection to the underlying atomistic-scale interactions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach

Jian

Thorpe

Izvekov

et al. 2007

Biophysical Journal

182

215

View full text Add to dashboard Cite

show abstract

“…Since then, a wide variety of KBPs have been introduced using different levels of geometry resolution, different terms contributing to the potential energy, different procedures to relate the energies to the observed frequencies, different levels of applicability (from all proteins in general to only a specific protein family), and for different purposes ranging from native fold recognition to protein stability and dynamic simulations 8–54. In particular, many variations of Sippl's atomic‐level potential have been obtained 1, 22, 23, 32, 37, 45–47. Multibody potentials introduced by Gan et al ., in the form of four‐body residue interactions, are still actively under development 34, 35, 51–53, 55.…”

Section: Introductionmentioning

confidence: 99%

Another look at the conditions for the extraction of protein knowledge‐based potentials

Betancourt

2008

Proteins

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Protein knowledge-based potentials are effective free energies obtained from databases of known protein structures. They are used to parameterize coarse-grained protein models in many folding simulation and structure prediction methods. Two common approaches are used in the derivation of knowledge-based potentials. One assumes that the energy parameters optimize the native structure stability. The other assumes that interaction events are related to their energies according to the Boltzmann distribution, and that they are distributed independently of other events, that is, the quasi-chemical approximation. Here, these assumptions are systematically tested by extracting contact energies from artificial databases of lattice proteins with predefined pairwise contact energies. Databases of protein sequences are designed to either satisfy the Boltzmann distribution at high or low temperatures, or to simultaneously optimize the native stability and folding kinetics. It is found that the quasi-chemical approximation, with the ideal reference state, accurately reproduce the true energies for high temperature Boltzmann distributed sequences (weakly interacting residues), but less accurately at low temperatures, where the sequences correspond to energy minima and the residues are strongly interacting. To overcome this problem, an iterative procedure for Boltzmann distributed sequences is introduced, which accounts for interacting residue correlations and eliminates the need for the quasi-chemical approximation. In this case, the energies are accurately reproduced at any ensemble temperature. However, when the database of sequences designed for optimal stability and kinetics is used, the energy correlation is less than optimal using either method, exhibiting random and systematic deviations from linearity. Therefore, the assumption that native structures are maximally stable or that sequences are determined according to the Boltzmann distribution seems to be inadequate for obtaining accurate energies. The limited number of sequences in the database and the inhomogeneous concentration of amino acids from one structure to another do not seem to be major obstacles for improving the quality of the extracted pairwise energies, with the exception of repulsive interactions.

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“…10,11 The second type attempts to capture the solvent and/or environmental effects into effective potentials that allow to perform calculations involving amino acids without explicitly including the solvent molecules. 19 Obviously, these interaction potentials are very dependent of the stratification, i.e., of the particular criterion used for choosing the proteins or portions of proteins to which the statistics will be applied. One of them concerns statistically calculated free energies or potentials of mean force at contact among the residues of native proteins.…”

Section: Introductionmentioning

confidence: 99%

Hydration properties and potentials of mean force of nonpolar amino acid residues in water: A pertubation theoretic approach

Renzi

Stoico

Vericat

2005

The Journal of Chemical Physics

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We use a perturbation approach to calculate the solute-solvent and solute-solute pair correlations for infinitely dilute solutions of nonpolar amino acid side chains in water. In particular, from the solute-solute correlations we derive potentials of mean force for all the distances between different residues of amino acids. Comparison with molecular dynamics simulations performed using GROMOS package shows that the goodness of the approximation varies with the amino acid considered. We discuss in terms of hydrophobicity the different effect that the inclusion of the attractions causes on the solute-water and solute-solute correlations.

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Effective pair potentials between protein amino acids

Cited by 14 publications

References 13 publications

Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach

Coarse-Grained Peptide Modeling Using a Systematic Multiscale Approach

Another look at the conditions for the extraction of protein knowledge‐based potentials

Hydration properties and potentials of mean force of nonpolar amino acid residues in water: A pertubation theoretic approach

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