Literature DB >> 7846026

Learning about protein folding via potential functions.

V N Maiorov1, G M Crippen.   

Abstract

Over the last few years we have developed an empirical potential function that solves the protein structure recognition problem: given the sequence for an n-residue globular protein and a collection of plausible protein conformations, including the native conformation for that sequence, identify the correct, native conformation. Having determined this potential on the basis of only some 6500 native/nonnative pairs of structures for 58 proteins, we find it recognizes the native conformation for essentially all compact, soluble, globular proteins having known native conformations in comparisons with 10(4) to 10(6) reasonable alternative conformations apiece. In this sense, the potential encodes nearly all the essential features of globular protein conformational preference. In addition it "knows" about many additional factors in protein folding, such as the stabilization of multimeric proteins, quaternary structure, the role of disulfide bridges and ligands, proproteins vs. processed proteins, and minimal strand lengths in globular proteins. Comparisons are made with other sorts of protein folding problems, and applications in protein conformational determination and prediction are discussed.

Entities:  

Mesh:

Year:  1994        PMID: 7846026     DOI: 10.1002/prot.340200206

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


  4 in total

1.  A method for parameter optimization in computational biology.

Authors:  J B Rosen; A T Phillips; S Y Oh; K A Dill
Journal:  Biophys J       Date:  2000-12       Impact factor: 4.033

2.  Feasibility in the inverse protein folding protocol.

Authors:  M Ota; K Nishikawa
Journal:  Protein Sci       Date:  1999-05       Impact factor: 6.725

3.  Self-consistently optimized statistical mechanical energy functions for sequence structure alignment.

Authors:  K K Koretke; Z Luthey-Schulten; P G Wolynes
Journal:  Protein Sci       Date:  1996-06       Impact factor: 6.725

4.  Learning To Fold Proteins Using Energy Landscape Theory.

Authors:  N P Schafer; B L Kim; W Zheng; P G Wolynes
Journal:  Isr J Chem       Date:  2014-08       Impact factor: 3.333

  4 in total

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