Single-Point Mutation with a Rotamer Library Toolkit: Toward Protein Engineering

Protein engineers have long been hard at work to harness biocatalysts as a natural source of regio-, stereo-, and chemoselectivity in order to carry out chemistry (reactions and/or substrates) not previously achieved with these enzymes. The extreme labor demands and exponential number of mutation combinations have induced computational advances in this domain. The first step in our virtual approach is to predict the correct conformations upon mutation of residues (i.e., rebuilding side chains). For this purpose, we opted for a combination of molecular mechanics and statistical data. In this work, we have developed automated computational tools to extract protein structural information and created conformational libraries for each amino acid dependent on a variable number of parameters (e.g., resolution, flexibility, secondary structure). We have also developed the necessary tool to apply the mutation and optimize the conformation accordingly. For side-chain conformation prediction, we obtained overall average root-mean-square deviations (RMSDs) of 0.91 and 1.01 Å for the 18 flexible natural amino acids within two distinct sets of over 3000 and 1500 side-chain residues, respectively. The commonly used dihedral angle differences were also evaluated and performed worse than the state of the art. These two metrics are also compared. Furthermore, we generated a family-specific library for kinases that produced an average 2% lower RMSD upon side-chain reconstruction and a residue-specific library that yielded a 17% improvement. Ultimately, since our protein engineering outlook involves using our docking software, Fitted/Impacts, we applied our mutation protocol to a benchmarked data set for self- and cross-docking. Our side-chain reconstruction does not hinder our docking software, demonstrating differences in pose prediction accuracy of approximately 2% (RMSD cutoff metric) for a set of over 200 protein/ligand structures. Similarly, when docking to a set of over 100 kinases, side-chain reconstruction (using both general and biased conformation libraries) had minimal detriment to the docking accuracy.

[1]  Emidio Capriotti,et al.  Computational and theoretical methods for protein folding. , 2013, Biochemistry.

[2]  Chang-Guo Zhan,et al.  Computational design of a thermostable mutant of cocaine esterase via molecular dynamics simulations. , 2011, Organic & biomolecular chemistry.

[3]  M. Reetz,et al.  Biocatalysis in organic chemistry and biotechnology: past, present, and future. , 2013, Journal of the American Chemical Society.

[4]  S. Pack,et al.  Thermostabilization of Bacillus circulans xylanase via computational design of a flexible surface cavity. , 2010, Journal of biotechnology.

[5]  Roland L. Dunbrack,et al.  Prediction of protein side-chain rotamers from a backbone-dependent rotamer library: a new homology modeling tool. , 1997, Journal of molecular biology.

[6]  Andrew Currin,et al.  Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently , 2014, Chemical Society reviews.

[7]  Ruth Nussinov,et al.  FireDock: a web server for fast interaction refinement in molecular docking† , 2008, Nucleic Acids Res..

[8]  David Baker,et al.  An exciting but challenging road ahead for computational enzyme design , 2010, Protein science : a publication of the Protein Society.

[9]  W U Primrose,et al.  A single mutation in cytochrome P450 BM3 changes substrate orientation in a catalytic intermediate and the regiospecificity of hydroxylation. , 1997, Biochemistry.

[10]  Young Je Yoo,et al.  Prediction of the solvent affecting site and the computational design of stable Candida antarctica lipase B in a hydrophilic organic solvent. , 2013, Journal of biotechnology.

[11]  Joelle N. Pelletier,et al.  Expanding the organic toolbox: a guide to integrating biocatalysis in synthesis. , 2012, Chemical Society reviews.

[12]  Sabine Laschat,et al.  Rational Design of a Minimal and Highly Enriched CYP102A1 Mutant Library with Improved Regio‐, Stereo‐ and Chemoselectivity , 2009, Chembiochem : a European journal of chemical biology.

[13]  John D. Westbrook,et al.  The Protein Model Portal , 2008, Journal of Structural and Functional Genomics.

[14]  Amy E Keating,et al.  X‐ray vs. NMR structures as templates for computational protein design , 2009, Proteins.

[15]  Andreas S Bommarius,et al.  Status of protein engineering for biocatalysts: how to design an industrially useful biocatalyst. , 2011, Current opinion in chemical biology.

[16]  Rocco Moretti,et al.  Computational enzyme design. , 2013, Angewandte Chemie.

[17]  António M. Baptista,et al.  Implicit solvation in the self-consistent mean field theory method: sidechain modelling and prediction of folding free energies of protein mutants , 2001, J. Comput. Aided Mol. Des..

[18]  Niles A Pierce,et al.  Protein design is NP-hard. , 2002, Protein engineering.

[19]  Lucas B Johnson,et al.  Methods for library-scale computational protein design. , 2014, Methods in molecular biology.

[20]  Amanda L. Smith,et al.  Computational protein design enables a novel one-carbon assimilation pathway , 2015, Proceedings of the National Academy of Sciences.

[21]  Costas D Maranas,et al.  Recent advances in computational protein design. , 2011, Current opinion in structural biology.

[22]  Frances H Arnold,et al.  Expanding P450 catalytic reaction space through evolution and engineering. , 2014, Current opinion in chemical biology.

[23]  Christopher A. Voigt,et al.  Protein building blocks preserved by recombination , 2002, Nature Structural Biology.

[24]  Xin Gao,et al.  A protein-dependent side-chain rotamer library , 2011, BMC Bioinformatics.

[25]  Roberto A Chica,et al.  Multistate approaches in computational protein design , 2012, Protein science : a publication of the Protein Society.

[26]  A Joshua Wand,et al.  Improved side‐chain prediction accuracy using an ab initio potential energy function and a very large rotamer library , 2004, Protein science : a publication of the Protein Society.

[27]  Tyler J Harpole,et al.  Side-chain conformation at the selectivity filter shapes the permeation free-energy landscape of an ion channel , 2014, Proceedings of the National Academy of Sciences.

[28]  J. Richardson,et al.  The penultimate rotamer library , 2000, Proteins.

[29]  D. Baker,et al.  Design of a Novel Globular Protein Fold with Atomic-Level Accuracy , 2003, Science.

[30]  Claes Gustafsson,et al.  Optimizing the search algorithm for protein engineering by directed evolution. , 2003, Protein engineering.

[31]  Roberto A Chica,et al.  Semi-rational approaches to engineering enzyme activity: combining the benefits of directed evolution and rational design. , 2005, Current opinion in biotechnology.

[32]  Anil K. Mishra,et al.  In silico thermodynamics stability change analysis involved in BH4 responsive mutations in phenylalanine hydroxylase: QM/MM and MD simulations analysis , 2015, Journal of biomolecular structure & dynamics.

[33]  Richard Fox,et al.  Directed molecular evolution by machine learning and the influence of nonlinear interactions. , 2005, Journal of theoretical biology.

[34]  Patrice Koehl,et al.  Protein side‐chain modeling with a protein‐dependent optimized rotamer library , 2014, Proteins.

[35]  P. Koehl,et al.  Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. , 1994, Journal of molecular biology.

[36]  Manfred T Reetz,et al.  Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes , 2007, Nature Protocols.

[37]  Roberto A. Chica,et al.  Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design , 2015, Protein science : a publication of the Protein Society.

[38]  Jie Chen,et al.  Improving stability of nitrile hydratase by bridging the salt-bridges in specific thermal-sensitive regions. , 2013, Journal of biotechnology.

[39]  Manfred T Reetz,et al.  Iterative saturation mutagenesis: a powerful approach to engineer proteins by systematically simulating Darwinian evolution. , 2014, Methods in molecular biology.

[40]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[41]  Roland L. Dunbrack,et al.  Conformational Analysis of the DFG-Out Kinase Motif and Biochemical Profiling of Structurally Validated Type II Inhibitors , 2014, Journal of medicinal chemistry.

[42]  Ioannis Ch. Paschalidis,et al.  The Impact of Side-Chain Packing on Protein Docking Refinement , 2015, J. Chem. Inf. Model..

[43]  J. Ponder,et al.  Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. , 1987, Journal of molecular biology.

[44]  Roland L. Dunbrack,et al.  Backbone-dependent rotamer library for proteins. Application to side-chain prediction. , 1993, Journal of molecular biology.

[45]  Alex Nisthal,et al.  Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles , 2010, Proceedings of the National Academy of Sciences.

[46]  Gheorghe-Doru Roiban,et al.  Expanding the toolbox of organic chemists: directed evolution of P450 monooxygenases as catalysts in regio- and stereoselective oxidative hydroxylation. , 2015, Chemical communications.

[47]  Frances H Arnold,et al.  Enantioselective intramolecular C-H amination catalyzed by engineered cytochrome P450 enzymes in vitro and in vivo. , 2013, Angewandte Chemie.

[48]  David Baker,et al.  Computational Design of an α-Gliadin Peptidase , 2012, Journal of the American Chemical Society.

[49]  Uwe T Bornscheuer,et al.  Protein engineering from "scratch" is maturing. , 2014, Angewandte Chemie.

[50]  Jens Meiler,et al.  RosettaEPR: Rotamer Library for Spin Label Structure and Dynamics , 2013, PloS one.

[51]  Tom L Blundell,et al.  Advantages of fine-grained side chain conformer libraries. , 2003, Protein engineering.

[52]  Dan S. Tawfik,et al.  Stability effects of mutations and protein evolvability. , 2009, Current opinion in structural biology.

[53]  Jens Meiler,et al.  New algorithms and an in silico benchmark for computational enzyme design , 2006, Protein science : a publication of the Protein Society.

[54]  Ping Wang,et al.  Enhanced thermostability of methyl parathion hydrolase from Ochrobactrum sp. M231 by rational engineering of a glycine to proline mutation , 2010, The FEBS journal.

[55]  Mona Singh,et al.  A Semidefinite Programming Approach to Side Chain Positioning with New Rounding Strategies , 2004, INFORMS J. Comput..

[56]  Jianpeng Ma,et al.  OPUS‐Rota: A fast and accurate method for side‐chain modeling , 2008, Protein science : a publication of the Protein Society.

[57]  David Baker,et al.  Centenary Award and Sir Frederick Gowland Hopkins Memorial Lecture. Protein folding, structure prediction and design. , 2014, Biochemical Society transactions.

[58]  Roland L. Dunbrack,et al.  A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. , 2011, Structure.

[59]  Eric A. Althoff,et al.  De Novo Computational Design of Retro-Aldol Enzymes , 2008, Science.

[60]  M Karplus,et al.  Construction of side-chains in homology modelling. Application to the C-terminal lobe of rhizopuspepsin. , 1989, Journal of molecular biology.

[61]  David Baker,et al.  Catalytic mechanism and performance of computationally designed enzymes for Kemp elimination. , 2008, Journal of the American Chemical Society.

[62]  Eric A. Althoff,et al.  Kemp elimination catalysts by computational enzyme design , 2008, Nature.

[63]  Christopher G. Tate,et al.  Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors , 2014, Journal of chemical theory and computation.

[64]  Jasmine L. Gallaher,et al.  Computational Design of an Enzyme Catalyst for a Stereoselective Bimolecular Diels-Alder Reaction , 2010, Science.

[65]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[66]  P. Cirino,et al.  Recent advances in engineering proteins for biocatalysis , 2014, Biotechnology and bioengineering.

[67]  Ian Walsh,et al.  NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation , 2014, BMC Genomics.

[68]  Rajni Verma,et al.  Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering , 2012, Computational and structural biotechnology journal.

[69]  Christopher R. Corbeil,et al.  Docking Ligands into Flexible and Solvated Macromolecules. 3. Impact of Input Ligand Conformation, Protein Flexibility, and Water Molecules on the Accuracy of Docking Programs , 2009, J. Chem. Inf. Model..

[70]  Alexander D. MacKerell,et al.  CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields , 2009, J. Comput. Chem..

[71]  John C Whitman,et al.  Improving catalytic function by ProSAR-driven enzyme evolution , 2007, Nature Biotechnology.

[72]  N. Grishin,et al.  Side‐chain modeling with an optimized scoring function , 2002, Protein science : a publication of the Protein Society.

[73]  Roberto A Chica,et al.  Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles , 2014, Proteins.

[74]  Valérie Campagna-Slater,et al.  Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery , 2012, J. Chem. Inf. Model..

[75]  D. Baker,et al.  A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins. , 2003, Journal of molecular biology.

[76]  Dan S. Tawfik,et al.  Protein engineers turned evolutionists , 2007, Nature Methods.

[77]  D. Baker,et al.  Native protein sequences are close to optimal for their structures. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[78]  Alfonso Jaramillo,et al.  Challenges in the computational design of proteins , 2009, Journal of The Royal Society Interface.

[79]  Valérie Campagna-Slater,et al.  Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s , 2012, J. Chem. Inf. Model..

[80]  Roland L. Dunbrack.  Rotamer libraries in the 21st century. , 2002, Current opinion in structural biology.

[81]  Roland L. Dunbrack,et al.  Conformational analysis of the backbone-dependent rotamer preferences of protein sidechains , 1994, Nature Structural Biology.

[82]  Timothy W. Craven,et al.  A Rotamer Library to Enable Modeling and Design of Peptoid Foldamers , 2014, Journal of the American Chemical Society.

[83]  Nathanael Weill,et al.  Docking Ligands into Flexible and Solvated Macromolecules, 7. Impact of Protein Flexibility and Water Molecules on Docking-Based Virtual Screening Accuracy , 2014, J. Chem. Inf. Model..

[84]  G. Huisman,et al.  Engineering the third wave of biocatalysis , 2012, Nature.

[85]  Frances H Arnold,et al.  Library analysis of SCHEMA‐guided protein recombination , 2003, Protein science : a publication of the Protein Society.

[86]  K. Auclair,et al.  Controlling substrate specificity and product regio- and stereo-selectivities of P450 enzymes without mutagenesis. , 2014, Bioorganic & medicinal chemistry.

[87]  Yang Cao,et al.  RASP: rapid modeling of protein side chain conformations , 2011, Bioinform..

[88]  Adrian H Elcock,et al.  Parametrization of Backbone Flexibility in a Coarse-Grained Force Field for Proteins (COFFDROP) Derived from All-Atom Explicit-Solvent Molecular Dynamics Simulations of All Possible Two-Residue Peptides. , 2015, Journal of chemical theory and computation.