The Iterative Protein Redesign and Optimization (IPRO) suite of programs

Proteins are an important class of biomolecules with applications spanning across biotechnology and medicine. In many cases, native proteins must be redesigned to improve various performance metrics by changing their amino acid sequences. Algorithms can help sharpen protein library design by focusing the library on sequences that optimize computationally accessible proxies. The Iterative Protein Redesign and Optimization (IPRO) suite of programs offers an integrated environment for (1) altering protein binding affinity and specificity, (2) grafting a binding pocket into an existing protein scaffold, (3) predicting an antibody's tertiary structure based on its sequence, (4) enhancing enzymatic activity, and (5) assessing the structure and binding energetics for a specific mutant. This manuscript provides an overview of the methods involved in IPRO, input language terminology, algorithmic details, software implementation specifics and application highlights. IPRO can be downloaded at http://maranas.che.psu.edu. © 2014 Wiley Periodicals, Inc.

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

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

[3]  Roberto A. Chica,et al.  Iterative approach to computational enzyme design , 2012, Proceedings of the National Academy of Sciences.

[4]  Jérôme Lane,et al.  IMGT®, the international ImMunoGeneTics information system® , 2004, Nucleic Acids Res..

[5]  Hossein Fazelinia,et al.  OptGraft: A computational procedure for transferring a binding site onto an existing protein scaffold , 2008, Protein science : a publication of the Protein Society.

[6]  Anthony J. Wilkinson,et al.  Protein engineering 20 years on , 2002, Nature Reviews Molecular Cell Biology.

[7]  O. Leavy.  Therapeutic antibodies: past, present and future , 2010, Nature Reviews Immunology.

[8]  Roland L. Dunbrack,et al.  Bayesian statistical analysis of protein side‐chain rotamer preferences , 1997, Protein science : a publication of the Protein Society.

[9]  S. L. Mayo,et al.  Enzyme-like proteins by computational design , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Arieh Warshel,et al.  Exploring challenges in rational enzyme design by simulating the catalysis in artificial kemp eliminase , 2010, Proceedings of the National Academy of Sciences.

[11]  Costas D. Maranas,et al.  OptZyme: Computational Enzyme Redesign Using Transition State Analogues , 2013, PloS one.

[12]  Jasmine L. Gallaher,et al.  Alteration of enzyme specificity by computational loop remodeling and design , 2009, Proceedings of the National Academy of Sciences.

[13]  M. Karplus,et al.  Effective energy function for proteins in solution , 1999, Proteins.

[14]  D. Baker,et al.  Computational redesign of endonuclease DNA binding and cleavage specificity , 2006, Nature.

[15]  Jens Schneider-Mergener,et al.  Crystallographic Analysis of Anti-p24 (HIV-1) Monoclonal Antibody Cross-Reactivity and Polyspecificity , 1997, Cell.

[16]  George A. Khoury,et al.  Computational design of Candida boidinii xylose reductase for altered cofactor specificity , 2009, Protein science : a publication of the Protein Society.

[17]  C D Maranas,et al.  OptCDR: a general computational method for the design of antibody complementarity determining regions for targeted epitope binding. , 2010, Protein engineering, design & selection : PEDS.

[18]  Chenggang Xu,et al.  Factors influencing cellulosome activity in consolidated bioprocessing of cellulosic ethanol. , 2010, Bioresource technology.

[19]  Iren Valova,et al.  QRS Complex Detector Implementing Orthonormal Functions , 2012, Complex Adaptive Systems.

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

[21]  Patrice Duroux,et al.  IMGT/Collier de Perles: IMGT standardized representation of domains (IG, TR, and IgSF variable and constant domains, MH and MhSF groove domains). , 2011, Cold Spring Harbor protocols.

[22]  Charles L. Brooks,et al.  New analytic approximation to the standard molecular volume definition and its application to generalized Born calculations , 2003, J. Comput. Chem..

[23]  J Liu,et al.  Crystal structure of human insulin-like growth factor-1: detergent binding inhibits binding protein interactions. , 2001, Biochemistry.

[24]  Ryo Takeuchi,et al.  Computational redesign of a mononuclear zinc metalloenzyme for organophosphate hydrolysis. , 2012, Nature chemical biology.

[25]  Pablo Gainza,et al.  Osprey: Protein Design with Ensembles, Flexibility, and Provable Algorithms , 2022 .

[26]  Liang Tong,et al.  Computational design of catalytic dyads and oxyanion holes for ester hydrolysis. , 2012, Journal of the American Chemical Society.

[27]  M. Lefranc.  IMGT unique numbering for the variable (V), constant (C), and groove (G) domains of IG, TR, MH, IgSF, and MhSF. , 2011, Cold Spring Harbor protocols.

[28]  Thomas S. Peat,et al.  A Human Monoclonal Antibody against Insulin-Like Growth Factor-II Blocks the Growth of Human Hepatocellular Carcinoma Cell Lines In vitro and In vivo , 2010, Molecular Cancer Therapeutics.

[29]  S. L. Mayo,et al.  De novo protein design: fully automated sequence selection. , 1997, Science.

[30]  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.

[31]  Costas D. Maranas,et al.  MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies , 2013, BMC Bioinformatics.

[32]  Fang Zheng,et al.  Free-energy perturbation simulation on transition states and redesign of butyrylcholinesterase. , 2009, Biophysical journal.

[33]  M. Lefranc.  IMGT Collier de Perles for the variable (V), constant (C), and groove (G) domains of IG, TR, MH, IgSF, and MhSF. , 2011, Cold Spring Harbor protocols.

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

[35]  Mathieu Rouard,et al.  IMGT unique numbering for immunoglobulin and T cell receptor constant domains and Ig superfamily C-like domains. , 2005, Developmental and comparative immunology.

[36]  Costas D Maranas,et al.  Using a residue clash map to functionally characterize protein recombination hybrids. , 2003, Protein engineering.

[37]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

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

[39]  Salvador Ventura,et al.  Conformational strain in the hydrophobic core and its implications for protein folding and design , 2002, Nature Structural Biology.

[40]  Costas D. Maranas,et al.  Computational challenges in combinatorial library design for protein engineering , 2004 .

[41]  Hossein Fazelinia,et al.  Extending Iterative Protein Redesign and Optimization (IPRO) in protein library design for ligand specificity. , 2007, Biophysical journal.

[42]  Costas D Maranas,et al.  Identifying residue–residue clashes in protein hybrids by using a second-order mean-field approach , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[44]  Amy C. Anderson,et al.  Computational structure-based redesign of enzyme activity , 2009, Proceedings of the National Academy of Sciences.

[45]  Frances H. Arnold,et al.  Structure-guided SCHEMA recombination of distantly related β-lactamases , 2006 .

[46]  Bruce Randall Donald,et al.  Algorithms in Structural Molecular Biology , 2011 .

[47]  Tanja Kortemme,et al.  Computational design of protein-protein interactions. , 2004, Current opinion in chemical biology.

[48]  Jeffrey B. Endelman,et al.  Structure-Guided Recombination Creates an Artificial Family of Cytochromes P450 , 2006, PLoS biology.

[49]  Ian W. Davis,et al.  Structure validation by Cα geometry: ϕ,ψ and Cβ deviation , 2003, Proteins.

[50]  Stefan Becker,et al.  Cooperative structure of the heterotrimeric pre-mRNA retention and splicing complex , 2014, Nature Structural &Molecular Biology.

[51]  C. Maranas,et al.  IPRO: an iterative computational protein library redesign and optimization procedure. , 2006, Biophysical journal.

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

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

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

[55]  A. Fersht,et al.  Protein-protein recognition: crystal structural analysis of a barnase-barstar complex at 2.0-A resolution. , 1994, Biochemistry.

[56]  K. Sharp,et al.  Potential energy functions for protein design. , 2007, Current opinion in structural biology.

[57]  Costas D Maranas,et al.  Optimal protein library design using recombination or point mutations based on sequence-based scoring functions. , 2007, Protein engineering, design & selection : PEDS.

[58]  V. Menon,et al.  Trends in bioconversion of lignocellulose: Biofuels, platform chemicals & biorefinery concept , 2012 .

[59]  R. Friesner,et al.  Ab initio quantum chemical and mixed quantum mechanics/molecular mechanics (QM/MM) methods for studying enzymatic catalysis. , 2005, Annual review of physical chemistry.

[60]  Jayaraman Seetharaman,et al.  Computational design of enone-binding proteins with catalytic activity for the Morita-Baylis-Hillman reaction. , 2013, ACS chemical biology.

[61]  Bruce Tidor,et al.  Progress in computational protein design. , 2007, Current opinion in biotechnology.

[62]  Juan Fernández-Recio,et al.  SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models , 2012, Bioinform..