Literature DB >> 15852305

A consistent set of statistical potentials for quantifying local side-chain and backbone interactions.

Qiaojun Fang1, David Shortle.   

Abstract

The frequencies of occurrence of atom arrangements in high-resolution protein structures provide some of the most accurate quantitative measures of interaction energies in proteins. In this report we extend our development of a consistent set of statistical potentials for quantifying local interactions between side-chains and the polypeptide backbone, as well as nearby side-chains. Starting with phi/psi/chi1 propensities that select for optimal interactions of the 20 amino acid side-chains with the 2 flanking peptide bonds, the following 3 new terms are added: (1) a distance-dependent interaction between the side-chain at i and the carbonyl oxygens and amide protons of the peptide units at i +/- 2, i +/- 3, and i +/- 4; (2) a distance-dependent interaction between the side-chain at position i and side-chains at positions i + 1 through i + 4; and (3) an orientation-dependent interaction between the side-chain at position i and side-chains at i + 1 through i + 4. The relative strengths of these 4 pseudo free energy terms are estimated by the average information content of each scoring matrix and by assessing their performance in a simple fragment threading test. They vary from -0.4 - -0.5 kcal/mole per residue for phi/psi/chi1 propensities to a range of -0.15 - -0.6 kcal/mole per residue for each of the other 3 terms. The combined energy function, containing no interactions between atoms more than 4 residues apart, identifies the correct structural fragment for randomly selected 15 mers over 40% of the time, after searching through 232,000 alternative conformations. For 14 out of 20 sets of all-atom Rosetta decoys analyzed, the native structure has a combined score lower than any of the 1700-1900 decoy conformations. The ability of this energy function to detect energetically important details of local structure is demonstrated by its power to distinguish high-resolution crystal structures from NMR solution structures.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 15852305     DOI: 10.1002/prot.20482

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  16 in total

Review 1.  Modeling loop entropy.

Authors:  Gregory S Chirikjian
Journal:  Methods Enzymol       Date:  2011       Impact factor: 1.600

2.  Dihedral-angle information entropy as a gauge of secondary structure propensity.

Authors:  Shi Zhong; Jeremy M Moix; Stephen Quirk; Rigoberto Hernandez
Journal:  Biophys J       Date:  2006-09-15       Impact factor: 4.033

3.  Statistical potential for assessment and prediction of protein structures.

Authors:  Min-Yi Shen; Andrej Sali
Journal:  Protein Sci       Date:  2006-11       Impact factor: 6.725

4.  Local quality assessment in homology models using statistical potentials and support vector machines.

Authors:  Marc Fasnacht; Jiang Zhu; Barry Honig
Journal:  Protein Sci       Date:  2007-06-28       Impact factor: 6.725

5.  Statistical potential for modeling and ranking of protein-ligand interactions.

Authors:  Hao Fan; Dina Schneidman-Duhovny; John J Irwin; Guangqiang Dong; Brian K Shoichet; Andrej Sali
Journal:  J Chem Inf Model       Date:  2011-11-21       Impact factor: 4.956

6.  Intrinsic backbone preferences are fully present in blocked amino acids.

Authors:  Franc Avbelj; Simona Golic Grdadolnik; Joze Grdadolnik; Robert L Baldwin
Journal:  Proc Natl Acad Sci U S A       Date:  2006-01-19       Impact factor: 11.205

7.  New statistical potential for quality assessment of protein models and a survey of energy functions.

Authors:  Dmitry Rykunov; Andras Fiser
Journal:  BMC Bioinformatics       Date:  2010-03-12       Impact factor: 3.169

Review 8.  Finding the needle in the haystack: towards solving the protein-folding problem computationally.

Authors:  Bian Li; Michaela Fooksa; Sten Heinze; Jens Meiler
Journal:  Crit Rev Biochem Mol Biol       Date:  2017-10-04       Impact factor: 8.250

9.  DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking.

Authors:  Shiyong Liu; Ilya A Vakser
Journal:  BMC Bioinformatics       Date:  2011-07-11       Impact factor: 3.169

10.  The H-factor as a novel quality metric for homology modeling.

Authors:  Eric di Luccio; Patrice Koehl
Journal:  J Clin Bioinforma       Date:  2012-11-02
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.