Effective scoring function for protein sequence design.

Shide Liang; Nick V Grishin

doi:10.1002/prot.10560

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Effective scoring function for protein sequence design.

Shide Liang·Nick V Grishin

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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.

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Bacteriophage T4 Computational Biology Computer Simulation Drug Design Enzyme Stability Hydrogen Bonding Monte Carlo Method Muramidase Mutation Protein Engineering Protein Folding Proteins Static Electricity Structure-Activity Relationship Thermodynamics Journal Article

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Shide Liang, Nick V Grishin. 2004-02-01. Effective scoring function for protein sequence design.. https://doi.org/10.1002/prot.10560

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