Literature DB >> 8003975

De novo protein design using pairwise potentials and a genetic algorithm.

D T Jones1.   

Abstract

One of the major goals of molecular biology is to understand how protein chains fold into a unique 3-dimensional structure. Given this knowledge, perhaps the most exciting prospect will be the possibility of designing new proteins to perform designated tasks, an application that could prove to be of great importance in medicine and biotechnology. It is possible that effective protein design may be achieved without the requirement for a full understanding of the protein folding process. In this paper a simple method is described for designing an amino acid sequence to fit a given 3-dimensional structure. The compatibility of a designed sequence with a given fold is assessed by means of a set of statistically determined potentials (including interresidue pairwise and solvation terms), which have been previously applied to the problem of protein fold recognition. In order to generate sequences that best fit the fold, a genetic algorithm is used, whereby the sequence is optimized by a stochastic search in the style of natural selection.

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Year:  1994        PMID: 8003975      PMCID: PMC2142856          DOI: 10.1002/pro.5560030405

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


  19 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.  Three-dimensional structure of acylphosphatase. Refinement and structure analysis.

Authors:  A Pastore; V Saudek; G Ramponi; R J Williams
Journal:  J Mol Biol       Date:  1992-03-20       Impact factor: 5.469

3.  The SWISS-PROT protein sequence data bank.

Authors:  A Bairoch; B Boeckmann
Journal:  Nucleic Acids Res       Date:  1991-04-25       Impact factor: 16.971

4.  Protein design on computers. Five new proteins: Shpilka, Grendel, Fingerclasp, Leather, and Aida.

Authors:  C Sander; G Vriend; F Bazan; A Horovitz; H Nakamura; L Ribas; A V Finkelstein; A Lockhart; R Merkl; L J Perry
Journal:  Proteins       Date:  1992-02

5.  Detection of native-like models for amino acid sequences of unknown three-dimensional structure in a data base of known protein conformations.

Authors:  M J Sippl; S Weitckus
Journal:  Proteins       Date:  1992-07

6.  De novo design, expression, and characterization of Felix: a four-helix bundle protein of native-like sequence.

Authors:  M H Hecht; J S Richardson; D C Richardson; R C Ogden
Journal:  Science       Date:  1990-08-24       Impact factor: 47.728

7.  The Protein Data Bank: a computer-based archival file for macromolecular structures.

Authors:  F C Bernstein; T F Koetzle; G J Williams; E F Meyer; M D Brice; J R Rodgers; O Kennard; T Shimanouchi; M Tasumi
Journal:  J Mol Biol       Date:  1977-05-25       Impact factor: 5.469

8.  The folding type of a protein is relevant to the amino acid composition.

Authors:  H Nakashima; K Nishikawa; T Ooi
Journal:  J Biochem       Date:  1986-01       Impact factor: 3.387

Review 9.  Protein fold recognition.

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

10.  Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

Authors:  W Kabsch; C Sander
Journal:  Biopolymers       Date:  1983-12       Impact factor: 2.505

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  20 in total

1.  BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.

Authors:  Jonathan D Jou; Swati Jain; Ivelin S Georgiev; Bruce R Donald
Journal:  J Comput Biol       Date:  2016-01-08       Impact factor: 1.479

2.  Action-at-a-distance interactions enhance protein binding affinity.

Authors:  Brian A Joughin; David F Green; Bruce Tidor
Journal:  Protein Sci       Date:  2005-03-31       Impact factor: 6.725

3.  Protein design automation.

Authors:  B I Dahiyat; S L Mayo
Journal:  Protein Sci       Date:  1996-05       Impact factor: 6.725

4.  Patenting computer-designed peptides.

Authors:  S Patel; I P Stott; M Bhakoo; P Elliott
Journal:  J Comput Aided Mol Des       Date:  1998-11       Impact factor: 3.686

Review 5.  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

6.  Rational design of new binding specificity by simultaneous mutagenesis of calmodulin and a target peptide.

Authors:  David F Green; Andrew T Dennis; Peter S Fam; Bruce Tidor; Alan Jasanoff
Journal:  Biochemistry       Date:  2006-10-17       Impact factor: 3.162

7.  PRO-LIGAND: an approach to de novo molecular design. 3. A genetic algorithm for structure refinement.

Authors:  D R Westhead; D E Clark; D Frenkel; J Li; C W Murray; B Robson; B Waszkowycz
Journal:  J Comput Aided Mol Des       Date:  1995-04       Impact factor: 3.686

8.  Comparison of atomic solvation parametric sets: applicability and limitations in protein folding and binding.

Authors:  A H Juffer; F Eisenhaber; S J Hubbard; D Walther; P Argos
Journal:  Protein Sci       Date:  1995-12       Impact factor: 6.725

9.  Expanded explorations into the optimization of an energy function for protein design.

Authors:  Yao-Ming Huang; Christopher Bystroff
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2013 Sep-Oct       Impact factor: 3.710

10.  Improved prediction of protein side-chain conformations with SCWRL4.

Authors:  Georgii G Krivov; Maxim V Shapovalov; Roland L Dunbrack
Journal:  Proteins       Date:  2009-12
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