Effective Scoring Function for Protein Sequence Design

Shide Liang, Nick V. Grishin

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

We have developed an effective scoring function for protein design. The atomic solvation parameters, together with the weights of energy terms, were optimized so that residues corresponding to the native sequence were predicted with low energy in the training set of 28 protein structures. The solvation energy of non-hydrogen-bonded hydrophilic atoms was considered separately and expressed in a nonlinear way. As a result, our scoring function predicted native residues as the most favorable in 59% of the total positions in 28 proteins. We then tested the scoring function by comparing the predicted stability changes for 103 T4 lysozyme mutants with the experimental values. The correlation coefficients were 0.77 for surface mutations and 0.71 for all mutations. Finally, the scoring function combined with Monte Carlo simulation was used to predict favorable sequences on a fixed backbone. The designed sequences were similar to the natural sequences of the family to which the template structure belonged. The profile of the designed sequences was helpful for identification of remote homologues of the native sequence.

Original languageEnglish (US)
Pages (from-to)271-281
Number of pages11
JournalProteins: Structure, Function and Genetics
Volume54
Issue number2
DOIs
StatePublished - Feb 1 2004

Keywords

  • Atomic solvation parameters
  • Homology detection
  • Monte Carlo simulation
  • Profile
  • Protein design

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology

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