Literature DB >> 2021616

Prediction of protein folding from amino acid sequence over discrete conformation spaces.

G M Crippen1.   

Abstract

Predicting the three-dimensional structure of a protein given only its amino acid sequence is a long-standing goal in computational chemistry. In the thermodynamic approach, one needs a potential function of conformation that resembles the free energy of the real protein to the extent that the global minimum of the potential is attained by the native conformation and no other. In practice, this has never been achieved with certainty because even with greatly simplified representations of the polypeptide chain, there are an astronomical number of local minima to examine. If one chooses instead a protein representation with only a large but manageable number of discrete conformations, then the global preference of the potential for the native can be directly verified. Representing a protein as a walk on a two-dimensional square lattice makes it easy to see that simple functions of the interresidue contacts are sufficient to globally favor a given "native" conformation, as long as it is a compact, globular structure. Explicit representation of the solvent is not required. Another more realistic way to confine the conformational search to a finite set is to draw alternative conformations from fragments of larger proteins having known crystal structure. Then it is possible to construct a simple function of interresidue contacts in three dimensions such that only 8 proteins are required to determine the adjustable parameters, and the native conformations of 37 other proteins are correctly preferred over all alternative conformations. The deduced function favors short-range backbone-backbone contacts regardless of residue type and long-range hydrophobic associations. Interactions over long distances, such as electrostatics, are not required.

Mesh:

Year:  1991        PMID: 2021616     DOI: 10.1021/bi00231a018

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  11 in total

1.  A self-consistent knowledge-based approach to protein design.

Authors:  A Rossi; C Micheletti; F Seno; A Maritan
Journal:  Biophys J       Date:  2001-01       Impact factor: 4.033

2.  Selective mapping: a strategy for optimizing the construction of high-density linkage maps.

Authors:  T J Vision; D G Brown; D B Shmoys; R T Durrett; S D Tanksley
Journal:  Genetics       Date:  2000-05       Impact factor: 4.562

3.  Protein tertiary structure recognition using optimized Hamiltonians with local interactions.

Authors:  R A Goldstein; Z A Luthey-Schulten; P G Wolynes
Journal:  Proc Natl Acad Sci U S A       Date:  1992-10-01       Impact factor: 11.205

4.  Sequence-structure matching in globular proteins: application to supersecondary and tertiary structure determination.

Authors:  A Godzik; J Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  1992-12-15       Impact factor: 11.205

5.  Database-derived potentials dependent on protein size for in silico folding and design.

Authors:  Yves Dehouck; Dimitri Gilis; Marianne Rooman
Journal:  Biophys J       Date:  2004-07       Impact factor: 4.033

6.  CONTSOR--a new knowledge-based fold recognition potential, based on side chain orientation and contacts between residue terminal groups.

Authors:  Boris Vishnepolsky; Malak Pirtskhalava
Journal:  Protein Sci       Date:  2011-11-23       Impact factor: 6.725

7.  Modeling large RNAs and ribonucleoprotein particles using molecular mechanics techniques.

Authors:  A Malhotra; R K Tan; S C Harvey
Journal:  Biophys J       Date:  1994-06       Impact factor: 4.033

Review 8.  Protein fold recognition.

Authors:  D Jones; J Thornton
Journal:  J Comput Aided Mol Des       Date:  1993-08       Impact factor: 3.686

Review 9.  Prediction and analysis of structure, stability and unfolding of thermolysin-like proteases.

Authors:  G Vriend; V Eijsink
Journal:  J Comput Aided Mol Des       Date:  1993-08       Impact factor: 3.686

10.  Unearthing the root of amino acid similarity.

Authors:  James D Stephenson; Stephen J Freeland
Journal:  J Mol Evol       Date:  2013-06-07       Impact factor: 2.395

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