Electrostatic pKa computations in proteins: Role of internal cavities
Electrostatic pKa computations in proteins: Role of internal cavities
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DOI：
10.1002/prot.23092
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发表时间：
2011-12-01
影响因子：
2.9
通讯作者：
Knapp, Ernst-Walter
中科院分区：
文献类型：
--
作者：
Meyer, Tim;Kieseritzky, Gernot;Knapp, Ernst-Walter
在蛋白质的静电能计算中，通常使用溶剂可达表面积（SASA）算法来表征蛋白质表面。不幸的是，它经常找不到蛋白质内部的狭窄空洞。因此，基于该算法的pK(a)计算表现不佳。本文引入了一种新的空腔算法，解决了这一问题，并提供了改进的pK(a)值。该方法适用于SNase变异体中作为点突变引入的可滴定基团的20pk (a)值，其中晶体结构可用。这些pK(a)s的计算特别具有挑战性，因为它们被放置在相当疏水的环境中。对于9个突变体，其中可滴定残留物与大腔接触，计算和测量的pK(a)值之间的RMSDpKa为2.04，与Karlsberg(+) （http://agknapp.chemie.fu-berlin.de/karlsberg/）获得的RMSDpKa为8.8相比，这是一个相当大的改进。然而，对于11个可滴定残留物，与实验的一致性仍然很差（RMSDpKa = 6.01）。考虑到SNase的15个pK(a)s，它们处于更传统的疏水蛋白环境中，使用sasa算法的RMSDpKa为2.1，使用新空腔算法的RMSDpKa为1.7。这种一致性是合理的，但不如Karlsberg（+）的一般性能所期望的那样好，这表明SNase属于相对于pK(a)计算更困难的蛋白质。我们讨论了计算和测量的pK(a)s之间存在差异的可能原因。蛋白质2011;79:3320 - 3332。(C) 2011 Wiley-Liss, Inc。
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 pK(a) computations based on this algorithm perform badly. In this study a new cavity-algorithm is introduced, which solves this problem and provides improved pK(a) values. The procedure is applied to 20 pK(a) values of titratable groups introduced as point mutations in SNase variants, where crystal structures are available. The computations of these pK(a)s 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 RMSDpKa between computed and measured pK(a) 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 RMSDpKa of 8.8. However, for 11 titratable residues the agreement with experiments remains poor (RMSDpKa = 6.01). Considering 15 pK(a)s of SNase, which are in a more conventional less hydrophobic protein environment, the RMSDpKa 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 pK(a) computations. We discuss the possible reasons for the remaining discrepancies between computed and measured pK(a)s. Proteins 2011; 79:3320-3332. (C) 2011 Wiley-Liss, Inc.