Literature DB >> 10794424

Scoring functions in protein folding and design.

R I Dima1, J R Banavar, A Maritan.   

Abstract

We present an analysis of the assumptions behind some of the most commonly used methods for evaluating the goodness of the fit between a sequence and a structure. Our studies on a lattice model show that methods based on statistical considerations are easy to use and can capture some of the features of protein-like sequences and their corresponding native states, but unfortunately are incapable of recognizing, with certainty, the native-like conformation of a sequence among a set of decoys. Meanwhile, an optimization method, entailing the determination of the parameters of an effective free energy of interaction, is much more reliable in recognizing the native state of a sequence. However, the statistical method is shown to perform quite well in tests of protein design.

Mesh:

Year:  2000        PMID: 10794424      PMCID: PMC2144606          DOI: 10.1110/ps.9.4.812

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  41 in total

1.  Contact potential that recognizes the correct folding of globular proteins.

Authors:  V N Maiorov; G M Crippen
Journal:  J Mol Biol       Date:  1992-10-05       Impact factor: 5.469

2.  Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes.

Authors:  M R Betancourt; D Thirumalai
Journal:  Protein Sci       Date:  1999-02       Impact factor: 6.725

3.  Simulations of the folding of a globular protein.

Authors:  J Skolnick; A Kolinski
Journal:  Science       Date:  1990-11-23       Impact factor: 47.728

4.  Optimizing energy potentials for success in protein tertiary structure prediction.

Authors:  T L Chiu; R A Goldstein
Journal:  Fold Des       Date:  1998

5.  Self-consistently optimized energy functions for protein structure prediction by molecular dynamics.

Authors:  K K Koretke; Z Luthey-Schulten; P G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  1998-03-17       Impact factor: 11.205

6.  Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?

Authors:  J Skolnick; L Jaroszewski; A Kolinski; A Godzik
Journal:  Protein Sci       Date:  1997-03       Impact factor: 6.725

7.  Monte Carlo simulations of protein folding. I. Lattice model and interaction scheme.

Authors:  A Kolinski; J Skolnick
Journal:  Proteins       Date:  1994-04

8.  Statistical potentials extracted from protein structures: how accurate are they?

Authors:  P D Thomas; K A Dill
Journal:  J Mol Biol       Date:  1996-03-29       Impact factor: 5.469

9.  Conserved residues and the mechanism of protein folding.

Authors:  E Shakhnovich; V Abkevich; O Ptitsyn
Journal:  Nature       Date:  1996-01-04       Impact factor: 49.962

10.  An empirical energy function for threading protein sequence through the folding motif.

Authors:  S H Bryant; C E Lawrence
Journal:  Proteins       Date:  1993-05
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  3 in total

1.  Protein threading by learning.

Authors:  I Chang; M Cieplak; R I Dima; A Maritan; J R Banavar
Journal:  Proc Natl Acad Sci U S A       Date:  2001-11-20       Impact factor: 11.205

2.  Scoring predictive models using a reduced representation of proteins: model and energy definition.

Authors:  Federico Fogolari; Lidia Pieri; Agostino Dovier; Luca Bortolussi; Gilberto Giugliarelli; Alessandra Corazza; Gennaro Esposito; Paolo Viglino
Journal:  BMC Struct Biol       Date:  2007-03-23

Review 3.  Protein-protein interaction prediction with deep learning: A comprehensive review.

Authors:  Farzan Soleymani; Eric Paquet; Herna Viktor; Wojtek Michalowski; Davide Spinello
Journal:  Comput Struct Biotechnol J       Date:  2022-09-19       Impact factor: 6.155

  3 in total

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