A scoring function based on solvation thermodynamics for protein structure prediction

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We predict protein structure using our recently developed free energy function for describing protein stability, which is focused on solvation thermodynamics. The function is combined with the current most reliable sampling methods, i.e., fragment assembly (FA) and comparative modeling (CM). The prediction is tested using 11 small proteins for which high-resolution crystal structures are available. For 8 of these proteins, sequence similarities are found in the database, and the prediction is performed with CM. Fairly accurate models with average C<symbol>a</symbol> root mean square deviation (RMSD) ~ 2.0 Å are successfully obtained for all cases. For the rest of the target proteins, we perform the prediction following FA protocols. For 2 cases, we obtain predicted models with an RMSD ~ 3.0 Å as the best-scored structures. For the other case, the RMSD remains larger than 7 Å. For all the 11 target proteins, our scoring function identifies the experimentally determined native structure as the best structure. Starting from the predicted structure, replica exchange molecular dynamics is performed to further refine the structures. However, we are unable to improve its RMSD toward the experimental structure. The exhaustive sampling by coarse-grained normal mode analysis around the native structures reveals that our function has a linear correlation with RMSDs 3.0 Å. These results suggest that the function is quite reliable for the protein structure prediction while the sampling method remains one of the major limiting factors in it. The aspects through which the methodology could further be improved are discussed.br

A scoring function based on solvation thermodynamics for protein structure prediction

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