Literature DB >> 8637846

A simple protein folding algorithm using a binary code and secondary structure constraints.

S Sun1, P D Thomas, K A Dill.   

Abstract

We describe an algorithm to predict tertiary structures of small proteins. In contrast to most current folding algorithms, it uses very few energy parameters. Given the secondary structural elements in the sequence--alpha-helices and beta-strands--the algorithm searches the remaining conformational space of a simplified real-space representation of chains to find a minimum energy of an exceedingly simple potential function. The potential is based only on a single type of favorable interaction between hydrophobic residues, an unfavorable excluded volume term of spatial overlaps and, for sheet proteins, an interstrand hydrogen bond interaction. Where appropriate, the known disulfide bonds are constrained by a square-law potential. Conformations are searched by a genetic algorithm. The model predicts reasonably well the known tertiary folds of seven out of the 10 small proteins we consider. We draw two conclusions. First, for the proteins we tested, this exceedingly simple potential function is no worse than others having hundreds of energy parameters in finding the right general tertiary structures. Second, despite its simplicity, the potential function is not the weak link in this algorithm. Differences between our predicted structures and the correct targets can be ascribed to shortcomings in our search strategy. This potential function may be useful for testing other conformational search strategies.

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Year:  1995        PMID: 8637846     DOI: 10.1093/protein/8.8.769

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  10 in total

1.  A minimalist model protein with multiple folding funnels.

Authors:  C R Locker; R Hernandez
Journal:  Proc Natl Acad Sci U S A       Date:  2001-07-24       Impact factor: 11.205

2.  Spontaneous fibril formation by polyalanines; discontinuous molecular dynamics simulations.

Authors:  Hung D Nguyen; Carol K Hall
Journal:  J Am Chem Soc       Date:  2006-02-15       Impact factor: 15.419

3.  Distance geometry generates native-like folds for small helical proteins using the consensus distances of predicted protein structures.

Authors:  E S Huang; R Samudrala; J W Ponder
Journal:  Protein Sci       Date:  1998-09       Impact factor: 6.725

4.  Protein fold recognition without Boltzmann statistics or explicit physical basis.

Authors:  T Huber; A E Torda
Journal:  Protein Sci       Date:  1998-01       Impact factor: 6.725

Review 5.  Protein folding for realists: a timeless phenomenon.

Authors:  D Shortle; Y Wang; J R Gillespie; J O Wrabl
Journal:  Protein Sci       Date:  1996-06       Impact factor: 6.725

6.  A fast conformational search strategy for finding low energy structures of model proteins.

Authors:  T C Beutler; K A Dill
Journal:  Protein Sci       Date:  1996-10       Impact factor: 6.725

7.  Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

Authors:  A A Rabow; H A Scheraga
Journal:  Protein Sci       Date:  1996-09       Impact factor: 6.725

Review 8.  Evolutionary algorithms in computer-aided molecular design.

Authors:  D E Clark; D R Westhead
Journal:  J Comput Aided Mol Des       Date:  1996-08       Impact factor: 3.686

9.  Determinants of strand register in antiparallel beta-sheets of proteins.

Authors:  E G Hutchinson; R B Sessions; J M Thornton; D N Woolfson
Journal:  Protein Sci       Date:  1998-11       Impact factor: 6.725

10.  BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches.

Authors:  Bin Liu; Xin Gao; Hanyu Zhang
Journal:  Nucleic Acids Res       Date:  2019-11-18       Impact factor: 16.971

  10 in total

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