Electrostatic pKa computations in proteins: Role of internal cavities

The solvent accessible surface area (SASA) algorithm is conventionally used to characterize protein surfaces in electrostatic energy computations of proteins. Unfortunately, it often fails to find narrow cavities inside a protein. As a consequence pKa computations based on this algorithm perform badly. In this study a new cavity‐algorithm is introduced, which solves this problem and provides improved pKa values. The procedure is applied to 20 pKa values of titratable groups introduced as point mutations in SNase variants, where crystal structures are available. The computations of these pKas are particular challenging, since they are placed in a rather hydrophobic environment. For nine mutants, where the titratable residue is in contact with a large cavity, the RMSD  pK a between computed and measured pKa values is 2.04, which is a considerable improvement as compared to the original results obtained with Karlsberg+ (http://agknapp.chemie.fu‐berlin.de/karlsberg/) that yielded an RMSD  pK a of 8.8. However, for 11 titratable residues the agreement with experiments remains poor (RMSD  pK a = 6.01). Considering 15 pKas of SNase, which are in a more conventional less hydrophobic protein environment, the RMSD  pK a is 2.1 using the SASA‐algorithm and 1.7 using the new cavity‐algorithm. The agreement is reasonable but less good than what one would expect from the general performance of Karlsberg+ indicating that SNase belongs to the more difficult proteins with respect to pKa computations. We discuss the possible reasons for the remaining discrepancies between computed and measured pKas. Proteins 2011; © 2011 Wiley‐Liss, Inc.

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