A pH‐dependent computational approach to the effect of mutations on protein stability

This article describes a novel software implementation for high‐throughput scanning mutagenesis with a focus on protein stability. The approach combines molecular mechanics calculations with calculations of protein ionization and a Gaussian‐chain model of electrostatic interactions in unfolded state. Comprehensive testing demonstrates a state‐of‐the‐art accuracy for predicted free energy differences on single, double, and triple mutations with a correlation coefficient R above 0.7, which takes about 1.5 min per mutation on a single CPU. Unlike most of existing in silico methods for fast mutagenesis, the stability changes are reported as a continuous function of solution pH for wide pH intervals. We also propose a novel in silico strategy for searching stabilized protein variants that is based on combinatorial scanning mutagenesis using representative amino acid types. Our in silico predictions are in excellent agreement with the hyper‐stabilized variants of mesophilic cold shock protein found using the Proside method of direct evolution. © 2016 Wiley Periodicals, Inc.

[1]  L. Serrano,et al.  Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. , 2002, Journal of molecular biology.

[2]  W. C. Still,et al.  The GB/SA Continuum Model for Solvation. A Fast Analytical Method for the Calculation of Approximate Born Radii , 1997 .

[3]  M. Karplus,et al.  pKa's of ionizable groups in proteins: atomic detail from a continuum electrostatic model. , 1990, Biochemistry.

[4]  F. Schmid,et al.  In-vitro selection of highly stabilized protein variants with optimized surface. , 2001, Journal of molecular biology.

[5]  C. Pace,et al.  Protein structure, stability and solubility in water and other solvents. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[6]  Lisa Yan,et al.  The dominant role of side‐chain backbone interactions in structural realization of amino acid code. ChiRotor: A side‐chain prediction algorithm based on side‐chain backbone interactions , 2007, Protein science : a publication of the Protein Society.

[7]  J. Kirsch,et al.  Design and structural analysis of an engineered thermostable chicken lysozyme , 1995, Protein science : a publication of the Protein Society.

[8]  T. Miki,et al.  Left-sided substrate binding of lysozyme: evidence for the involvement of asparagine-46 in the initial binding of substrate to chicken lysozyme. , 1992, Biochemistry.

[9]  Susanne Hertz,et al.  Statistical Mechanics Of Chain Molecules , 2016 .

[10]  Piero Fariselli,et al.  I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure , 2005, Nucleic Acids Res..

[11]  D. Baker,et al.  Role of conformational sampling in computing mutation‐induced changes in protein structure and stability , 2011, Proteins.

[12]  F. Momany,et al.  Validation of the general purpose QUANTA ®3.2/CHARMm® force field , 1992 .

[13]  Bert L de Groot,et al.  Protein thermostability calculations using alchemical free energy simulations. , 2010, Biophysical journal.

[14]  Michael Wunderlich,et al.  Stabilization of the cold shock protein CspB from Bacillus subtilis by evolutionary optimization of Coulombic interactions. , 2005, Journal of molecular biology.

[15]  Akinori Sarai,et al.  ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions , 2005, Nucleic Acids Res..

[16]  B. García-Moreno E.,et al.  Large shifts in pKa values of lysine residues buried inside a protein , 2011, Proceedings of the National Academy of Sciences.

[17]  Lisa Yan,et al.  A fast and accurate computational approach to protein ionization , 2008, Protein science : a publication of the Protein Society.

[18]  Yang Gao,et al.  Predicting folding free energy changes upon single point mutations , 2012, Bioinform..

[19]  D. L. Veenstra,et al.  Free energy calculations of the mutation of Ile96-->Ala in barnase: contributions to the difference in stability. , 1996, Protein engineering.

[20]  B Honig,et al.  On the pH dependence of protein stability. , 1993, Journal of molecular biology.

[21]  Udo Heinemann,et al.  Two exposed amino acid residues confer thermostability on a cold shock protein , 2000, Nature Structural Biology.

[22]  Gordon M. Crippen,et al.  Predicting p K a . , 2009 .

[23]  David Baker,et al.  Macromolecular modeling with rosetta. , 2008, Annual review of biochemistry.

[24]  S J Wodak,et al.  Contribution of the hydrophobic effect to protein stability: analysis based on simulations of the Ile-96----Ala mutation in barnase. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Jianpeng Ma,et al.  CHARMM: The biomolecular simulation program , 2009, J. Comput. Chem..

[26]  V L Arcus,et al.  pKA values of carboxyl groups in the native and denatured states of barnase: the pKA values of the denatured state are on average 0.4 units lower than those of model compounds. , 1995, Biochemistry.

[27]  A. Elcock.  Realistic modeling of the denatured states of proteins allows accurate calculations of the pH dependence of protein stability. , 1999, Journal of molecular biology.

[28]  M Karplus,et al.  Simulation analysis of the stability mutant R96H of T4 lysozyme. , 1991, Biochemistry.

[29]  D. Laurents,et al.  pH dependence of the urea and guanidine hydrochloride denaturation of ribonuclease A and ribonuclease T1. , 1990, Biochemistry.

[30]  N. Pokala,et al.  Energy functions for protein design: adjustment with protein-protein complex affinities, models for the unfolded state, and negative design of solubility and specificity. , 2005, Journal of molecular biology.

[31]  B. Dominy,et al.  Development of a generalized Born model parameterization for proteins and nucleic acids , 1999 .

[32]  G. Schreiber,et al.  Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. , 2009, Protein engineering, design & selection : PEDS.

[33]  R. Abagyan,et al.  Large‐scale prediction of protein geometry and stability changes for arbitrary single point mutations , 2004, Proteins.

[34]  R. Abagyan,et al.  Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. , 1994, Journal of molecular biology.

[35]  Martin Karplus,et al.  pH-Dependence of Protein Stability: Absolute Electrostatic Free Energy Differences between Conformations† , 1997 .

[36]  Huan-Xiang Zhou,et al.  Polymer models of protein stability, folding, and interactions. , 2004, Biochemistry.

[37]  D. Bashford,et al.  Multiple‐site ligand binding to flexible macromolecules: Separation of global and local conformational change and an iterative mobile clustering approach , 1999 .

[38]  Philippe Bogaerts,et al.  Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 , 2009, Bioinform..

[39]  Kevin L. Shaw,et al.  Charge–charge interactions influence the denatured state ensemble and contribute to protein stability , 2000, Protein science : a publication of the Protein Society.

[40]  B. García-Moreno E.,et al.  Charges in the hydrophobic interior of proteins , 2010, Proceedings of the National Academy of Sciences.

[41]  Y. Hashimoto,et al.  Stabilization of a protein by removal of unfavorable abnormal pKa: substitution of undissociable residue for glutamic acid-35 in chicken lysozyme. , 1992, Biochemistry.

[42]  Huan-Xiang Zhou,et al.  A Gaussian-chain model for treating residual charge–charge interactions in the unfolded state of proteins , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Velin Z. Spassov,et al.  Introducing an Implicit Membrane in Generalized Born/Solvent Accessibility Continuum Solvent Models , 2002 .

[44]  A. Plückthun,et al.  Selecting proteins with improved stability by a phage-based method , 1998, Nature Biotechnology.

[45]  B. L. de Groot,et al.  Predicting free energy changes using structural ensembles. , 2009, Nature methods.

[46]  Lisa Yan,et al.  pH-selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half-life , 2012, Proteins.