Protein structure prediction force fields: Parametrization with quasi‐newtonian dynamics

We present an unusual method for parametrizing low‐resolution force fields of the type used for protein structure prediction. Force field parameters were‐determined by assigning each a fictitious mass and using a quasi‐molecular dynamics algorithm in parameter space. The quasi‐energy term favored folded native structures and specifically penalized folded nonnative structures. The force field was generated after optimizing less than 70 adjustable parameters, but shows a strong ability to discriminate between native structures and compact misfolded‐alternatives. The functional form of the force field was chosen as in molecular mechanics and is not table‐driven. It is continuous with continuous derivatives and is thus suitable for use with algorithms such as energy minimization or newtonian dynamics. Proteins 27:367–384, 1997. © 1997 Wiley‐Liss, Inc.

[1]  J. Berger.  Statistical Decision Theory and Bayesian Analysis , 1988 .

[2]  K. Dill,et al.  A simple protein folding algorithm using a binary code and secondary structure constraints. , 1995, Protein engineering.

[3]  Adam Godzik,et al.  Lattice representations of globular proteins: How good are they? , 1993, J. Comput. Chem..

[4]  M Levitt,et al.  Recognizing native folds by the arrangement of hydrophobic and polar residues. , 1995, Journal of molecular biology.

[5]  K. Dill.  Folding proteins: finding a needle in a haystack , 1993 .

[6]  I. Kuntz,et al.  Calculation of protein tertiary structure. , 1976, Journal of molecular biology.

[7]  P. Seetharamulu,et al.  A potential function for protein folding , 1991 .

[8]  G M Crippen,et al.  Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. , 1994, Journal of molecular biology.

[9]  Shoshana J. Wodak,et al.  Generating and testing protein folds , 1993 .

[10]  H. Scheraga,et al.  Medium- and long-range interaction parameters between amino acids for predicting three-dimensional structures of proteins. , 1976, Macromolecules.

[11]  R. Srinivasan,et al.  LINUS: A hierarchic procedure to predict the fold of a protein , 1995, Proteins.

[12]  M. Levitt.  A simplified representation of protein conformations for rapid simulation of protein folding. , 1976, Journal of molecular biology.

[13]  N. D. Clarke,et al.  Identification of protein folds: Matching hydrophobicity patterns of sequence sets with solvent accessibility patterns of known structures , 1990, Proteins.

[14]  S. Wodak,et al.  Factors influencing the ability of knowledge-based potentials to identify native sequence-structure matches. , 1994, Journal of molecular biology.

[15]  H. Berendsen,et al.  Molecular dynamics with coupling to an external bath , 1984 .

[16]  S. Doniach,et al.  A computer model to dynamically simulate protein folding: Studies with crambin , 1989, Proteins.

[17]  R. Jernigan,et al.  Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation , 1985 .

[18]  Janet M. Thornton,et al.  Protein fold recognition , 1993, J. Comput. Aided Mol. Des..

[19]  D. Covell,et al.  Conformations of folded proteins in restricted spaces. , 1990, Biochemistry.

[20]  D. Eisenberg,et al.  VERIFY3D: assessment of protein models with three-dimensional profiles. , 1997, Methods in enzymology.

[21]  G. Crippen.  Prediction of protein folding from amino acid sequence over discrete conformation spaces. , 1991, Biochemistry.

[22]  Manfred J. Sippl,et al.  Boltzmann's principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures , 1993, J. Comput. Aided Mol. Des..

[23]  J. Skolnick,et al.  Monte carlo simulations of protein folding. I. Lattice model and interaction scheme , 1994, Proteins.

[24]  J. Onuchic,et al.  Funnels, pathways, and the energy landscape of protein folding: A synthesis , 1994, Proteins.

[25]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[26]  Timothy F. Havel,et al.  The sampling properties of some distance geometry algorithms applied to unconstrained polypeptide chains: A study of 1830 independently computed conformations , 1990, Biopolymers.

[27]  M. Sippl.  Recognition of errors in three‐dimensional structures of proteins , 1993, Proteins.

[28]  D. Eisenberg,et al.  A method to identify protein sequences that fold into a known three-dimensional structure. , 1991, Science.

[29]  S. Bryant,et al.  Statistics of sequence-structure threading. , 1995, Current opinion in structural biology.

[30]  G M Crippen,et al.  A 1.8 Å resolution potential function for protein folding , 1990, Biopolymers.

[31]  A. Kolinski,et al.  Simulations of the Folding of a Globular Protein , 1990, Science.

[32]  G. Crippen,et al.  Contact potential that recognizes the correct folding of globular proteins. , 1992, Journal of molecular biology.

[33]  M. Levitt,et al.  Computer simulation of protein folding , 1975, Nature.

[34]  C Sander,et al.  Prediction of protein structure by evaluation of sequence-structure fitness. Aligning sequences to contact profiles derived from three-dimensional structures. , 1993, Journal of molecular biology.

[35]  J. Banavar,et al.  Computer Simulation of Liquids , 1988 .

[36]  U. Hobohm,et al.  Enlarged representative set of protein structures , 1994, Protein science : a publication of the Protein Society.

[37]  D. Eisenberg,et al.  Assessment of protein models with three-dimensional profiles , 1992, Nature.

[38]  R. Abagyan,et al.  Recognition of distantly related proteins through energy calculations , 1994, Proteins.