Lattice protein folding with two and four-body statistical potentials

Hin Hark Gan, Alexander Tropsha, Tamar Schlick

Research output: Contribution to journalArticle

Abstract

The cooperative folding of proteins implies a description by multibody potentials. Such multibody potentials can be generalized from common two-body statistical potentials through a relation to probability distributions of residue clusters via the Boltzmann condition. In this exploratory study, we compare a four-body statistical potential, defined by the Delaunay tessellation of protein structures, to the Miyazawa-Jernigan (MJ) potential for protein structure prediction, using a lattice chain growth algorithm. We use the four-body potential as a discriminatory function for conformational ensembles generated with the MJ potential and examine performance on a set of 22 proteins of 30-76 residues in length. We find that the four-body potential yields comparable results to the two-body MJ potential, namely, an average coordinate root-mean-square deviation (cRMSD) value of 8 Å for the lowest energy configurations of all-α proteins, and somewhat poorer cRMSD values for other protein classes. For both two and four-body potentials, superpositions of some predicted and native structures show a rough overall agreement. Formulating the four-body potential using larger data sets and direct, but costly, generation of conformational ensembles with multibody potentials may offer further improvements.

Original languageEnglish (US)
Pages (from-to)161-174
Number of pages14
JournalProteins: Structure, Function and Genetics
Volume43
Issue number2
DOIs
StatePublished - May 1 2001

Fingerprint

Protein folding
Protein Folding
Proteins
Probability distributions
Growth

Keywords

  • Chain growth algorithm
  • Lattice model
  • Monte Carlo
  • Multibody potentials
  • Statistical potential

ASJC Scopus subject areas

  • Genetics
  • Structural Biology
  • Biochemistry

Cite this

Lattice protein folding with two and four-body statistical potentials. / Gan, Hin Hark; Tropsha, Alexander; Schlick, Tamar.

In: Proteins: Structure, Function and Genetics, Vol. 43, No. 2, 01.05.2001, p. 161-174.

Research output: Contribution to journalArticle

@article{bffdee73b6f54b1794a750fa96ecdf56,
title = "Lattice protein folding with two and four-body statistical potentials",
abstract = "The cooperative folding of proteins implies a description by multibody potentials. Such multibody potentials can be generalized from common two-body statistical potentials through a relation to probability distributions of residue clusters via the Boltzmann condition. In this exploratory study, we compare a four-body statistical potential, defined by the Delaunay tessellation of protein structures, to the Miyazawa-Jernigan (MJ) potential for protein structure prediction, using a lattice chain growth algorithm. We use the four-body potential as a discriminatory function for conformational ensembles generated with the MJ potential and examine performance on a set of 22 proteins of 30-76 residues in length. We find that the four-body potential yields comparable results to the two-body MJ potential, namely, an average coordinate root-mean-square deviation (cRMSD) value of 8 {\AA} for the lowest energy configurations of all-α proteins, and somewhat poorer cRMSD values for other protein classes. For both two and four-body potentials, superpositions of some predicted and native structures show a rough overall agreement. Formulating the four-body potential using larger data sets and direct, but costly, generation of conformational ensembles with multibody potentials may offer further improvements.",
keywords = "Chain growth algorithm, Lattice model, Monte Carlo, Multibody potentials, Statistical potential",
author = "Gan, {Hin Hark} and Alexander Tropsha and Tamar Schlick",
year = "2001",
month = "5",
day = "1",
doi = "10.1002/1097-0134(20010501)43:2<161::AID-PROT1028>3.0.CO;2-F",
language = "English (US)",
volume = "43",
pages = "161--174",
journal = "Proteins: Structure, Function and Genetics",
issn = "0887-3585",
publisher = "Wiley-Liss Inc.",
number = "2",

}

TY - JOUR

T1 - Lattice protein folding with two and four-body statistical potentials

AU - Gan, Hin Hark

AU - Tropsha, Alexander

AU - Schlick, Tamar

PY - 2001/5/1

Y1 - 2001/5/1

N2 - The cooperative folding of proteins implies a description by multibody potentials. Such multibody potentials can be generalized from common two-body statistical potentials through a relation to probability distributions of residue clusters via the Boltzmann condition. In this exploratory study, we compare a four-body statistical potential, defined by the Delaunay tessellation of protein structures, to the Miyazawa-Jernigan (MJ) potential for protein structure prediction, using a lattice chain growth algorithm. We use the four-body potential as a discriminatory function for conformational ensembles generated with the MJ potential and examine performance on a set of 22 proteins of 30-76 residues in length. We find that the four-body potential yields comparable results to the two-body MJ potential, namely, an average coordinate root-mean-square deviation (cRMSD) value of 8 Å for the lowest energy configurations of all-α proteins, and somewhat poorer cRMSD values for other protein classes. For both two and four-body potentials, superpositions of some predicted and native structures show a rough overall agreement. Formulating the four-body potential using larger data sets and direct, but costly, generation of conformational ensembles with multibody potentials may offer further improvements.

AB - The cooperative folding of proteins implies a description by multibody potentials. Such multibody potentials can be generalized from common two-body statistical potentials through a relation to probability distributions of residue clusters via the Boltzmann condition. In this exploratory study, we compare a four-body statistical potential, defined by the Delaunay tessellation of protein structures, to the Miyazawa-Jernigan (MJ) potential for protein structure prediction, using a lattice chain growth algorithm. We use the four-body potential as a discriminatory function for conformational ensembles generated with the MJ potential and examine performance on a set of 22 proteins of 30-76 residues in length. We find that the four-body potential yields comparable results to the two-body MJ potential, namely, an average coordinate root-mean-square deviation (cRMSD) value of 8 Å for the lowest energy configurations of all-α proteins, and somewhat poorer cRMSD values for other protein classes. For both two and four-body potentials, superpositions of some predicted and native structures show a rough overall agreement. Formulating the four-body potential using larger data sets and direct, but costly, generation of conformational ensembles with multibody potentials may offer further improvements.

KW - Chain growth algorithm

KW - Lattice model

KW - Monte Carlo

KW - Multibody potentials

KW - Statistical potential

UR - http://www.scopus.com/inward/record.url?scp=0035342441&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0035342441&partnerID=8YFLogxK

U2 - 10.1002/1097-0134(20010501)43:2<161::AID-PROT1028>3.0.CO;2-F

DO - 10.1002/1097-0134(20010501)43:2<161::AID-PROT1028>3.0.CO;2-F

M3 - Article

C2 - 11276086

AN - SCOPUS:0035342441

VL - 43

SP - 161

EP - 174

JO - Proteins: Structure, Function and Genetics

JF - Proteins: Structure, Function and Genetics

SN - 0887-3585

IS - 2

ER -