Abstract
BackgroundProtein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters.ResultsUsing the optimized parameters, we obtain a mean unsigned error of 2.28 kcal/mol for the stability mutations. The performance of the CASA model is assessed by two further applications: (i) calculation of protein-ligand binding affinities and (ii) computational protein design. For these two applications, the previous parameters and the ones optimized here give a similar performance. For ligand binding, we obtain reasonable agreement with a set of 55 experimental mutation data, with a mean unsigned error of 1.76 kcal/mol with the new parameters and 1.47 kcal/mol with the earlier ones. We show that the optimized CASA model is not inferior to the Generalized Born/Surface Area (GB/SA) model for the prediction of these binding affinities. Likewise, the new parameters perform well for the design of 8 SH3 domain proteins where an average of 32.8% sequence identity relative to the native sequences was achieved. Further, it was shown that the computed sequences have the character of naturally-occuring homologues of the native sequences.ConclusionOverall, the two CASA variants explored here perform very well for a wide variety of applications. Both variants provide an efficient solvent treatment for the computational engineering of ligands and proteins.
Highlights
Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent
accessible surface area (ASA) models have become widely accepted within available implicit solvent treatments and have been used successfully in many applications, such as protein molecular dynamics [5,6,7], structure prediction [8] and protein:ligand binding [9,10]
A data set of 140 experimental stability mutations was used to adjust the parameters: these data correspond to 7 different helical peptides and 3 different proteins
Summary
Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. The computation of stability changes requires a model for the unfolded state of the protein In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. The screening of a large library of ligand molecules as a function of their binding affinity to a protein is a costly procedure and requires efficient methods. To avoid these problems, implicit solvent models are often used, which yield significant computational efficiency [3]. The contribution of each atom is quantified by a surface coefficient, which reflects the hydrophobicity or hydrophilicity of the particular atom type
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