Scoring functions for protein-protein interactions.

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.

[1]  Z. Weng,et al.  A structure‐based benchmark for protein–protein binding affinity , 2011, Protein science : a publication of the Protein Society.

[2]  Le Chang,et al.  Protein‐specific force field derived from the fragment molecular orbital method can improve protein–ligand binding interactions , 2013, J. Comput. Chem..

[3]  David W. Ritchie,et al.  Using Kendall-τ Meta-Bagging to Improve Protein-Protein Docking Predictions , 2011, PRIB.

[4]  B. Kuhlman,et al.  A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds , 2013, Protein science : a publication of the Protein Society.

[5]  Chao Yang,et al.  Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity , 2013, Journal of Computer-Aided Molecular Design.

[6]  Alfonso Valencia,et al.  Towards the prediction of protein interaction partners using physical docking , 2011, Molecular systems biology.

[7]  Paola Gramatica,et al.  The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models , 2003 .

[8]  D. Baker,et al.  High Resolution Mapping of Protein Sequence–Function Relationships , 2010, Nature Methods.

[9]  D. Baker,et al.  Computational design of a new hydrogen bond network and at least a 300-fold specificity switch at a protein-protein interface. , 2006, Journal of molecular biology.

[10]  Alexander Tropsha,et al.  Scoring protein interaction decoys using exposed residues (SPIDER): A novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues , 2012, Proteins.

[11]  Julie Bernauer,et al.  A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions , 2011, PloS one.

[12]  Martin Zacharias,et al.  Combining coarse‐grained nonbonded and atomistic bonded interactions for protein modeling , 2013, Proteins.

[13]  Iain H. Moal,et al.  Kinetic Rate Constant Prediction Supports the Conformational Selection Mechanism of Protein Binding , 2012, PLoS Comput. Biol..

[14]  Bernhard Sendhoff,et al.  Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[15]  Julie C. Mitchell,et al.  Community‐wide evaluation of methods for predicting the effect of mutations on protein–protein interactions , 2013, Proteins.

[16]  Haruki Nakamura,et al.  Computer-aided antibody design , 2012, Protein engineering, design & selection : PEDS.

[17]  Nicholas P. Schafer,et al.  Predictive energy landscapes for protein–protein association , 2012, Proceedings of the National Academy of Sciences.

[18]  David Baker,et al.  Role of the Biomolecular Energy Gap in Protein Design, Structure, and Evolution , 2012, Cell.

[19]  Dima Kozakov,et al.  Relationship between Hot Spot Residues and Ligand Binding Hot Spots in Protein-Protein Interfaces , 2012, J. Chem. Inf. Model..

[20]  Wei Huang,et al.  Coarse-grained simulations of protein-protein association: an energy landscape perspective. , 2012, Biophysical journal.

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

[22]  Luke N Robinson,et al.  Redesign of a cross-reactive antibody to dengue virus with broad-spectrum activity and increased in vivo potency , 2013, Proceedings of the National Academy of Sciences.

[23]  Qiang Yang,et al.  A Survey of Transfer and Multitask Learning in Bioinformatics , 2011, J. Comput. Sci. Eng..

[24]  J. Fernández-Recio,et al.  Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation. , 2013, Journal of chemical theory and computation.

[25]  Kengo Kinoshita,et al.  Community-wide assessment of protein-interface modeling suggests improvements to design methodology. , 2011, Journal of molecular biology.

[26]  Guilhem Faure,et al.  Versatility and Invariance in the Evolution of Homologous Heteromeric Interfaces , 2012, PLoS Comput. Biol..

[27]  Ilya A. Vakser,et al.  DECK: Distance and environment-dependent, coarse-grained, knowledge-based potentials for protein-protein docking , 2011, BMC Bioinformatics.

[28]  Simon J. Henderson,et al.  Monoclonal antibody therapeutics: history and future. , 2012, Current opinion in pharmacology.

[29]  Timothy A. Whitehead,et al.  Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing , 2012, Nature Biotechnology.

[30]  Miles Congreve,et al.  Deal watch: Valuation benefits of structure-enabled drug discovery , 2011, Nature Reviews Drug Discovery.

[31]  Guilhem Faure,et al.  InterEvScore: a novel coarse-grained interface scoring function using a multi-body statistical potential coupled to evolution , 2013, Bioinform..

[32]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[33]  D. V. S. Ravikant,et al.  Improving ranking of models for protein complexes with side chain modeling and atomic potentials , 2013, Proteins.

[34]  Rainer Merkl,et al.  PROCOS: Computational analysis of protein–protein complexes , 2011, J. Comput. Chem..

[35]  Harry Jubb,et al.  Structural biology and drug discovery for protein-protein interactions. , 2012, Trends in pharmacological sciences.

[36]  Alexandre M J J Bonvin,et al.  Are scoring functions in protein-protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark. , 2010, Journal of proteome research.

[37]  Brian Kuhlman,et al.  From Computational Design to a Protein That Binds , 2011, Science.

[38]  Jianpeng Ma,et al.  OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing. , 2008, Journal of molecular biology.

[39]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[40]  David Baker,et al.  A de novo protein binding pair by computational design and directed evolution. , 2011, Molecular cell.

[41]  Martin Zacharias,et al.  Scoring optimisation of unbound protein–protein docking including protein binding site predictions , 2012, Journal of molecular recognition : JMR.

[42]  Zhiping Weng,et al.  Prediction of protein–protein binding free energies , 2012, Protein science : a publication of the Protein Society.

[43]  Chen Yanover,et al.  Optimizing energy functions for protein–protein interface design , 2011, J. Comput. Chem..

[44]  A. Barabasi,et al.  An empirical framework for binary interactome mapping , 2008, Nature Methods.

[45]  Dima Kozakov,et al.  Application of asymmetric statistical potentials to antibody-protein docking , 2012, Bioinform..

[46]  D. Baker,et al.  Restricted sidechain plasticity in the structures of native proteins and complexes , 2011, Protein science : a publication of the Protein Society.

[47]  David Baker,et al.  Hotspot-centric de novo design of protein binders. , 2011, Journal of molecular biology.

[48]  Hong Liang,et al.  A method for integrative structure determination of protein-protein complexes , 2012, Bioinform..

[49]  Carles Pons,et al.  Scoring by Intermolecular Pairwise Propensities of Exposed Residues (SIPPER): A New Efficient Potential for Protein-Protein Docking , 2011, J. Chem. Inf. Model..

[50]  Alexandre M. J. J. Bonvin,et al.  CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK , 2011, PloS one.

[51]  Albert Solernou,et al.  pyDockCG: new coarse-grained potential for protein-protein docking. , 2011, The journal of physical chemistry. B.

[52]  Iain H. Moal,et al.  Protein-protein binding affinity prediction on a diverse set of structures , 2011, Bioinform..

[53]  P. Aloy,et al.  Interactome3D: adding structural details to protein networks , 2013, Nature Methods.

[54]  Naim Dahnoun,et al.  Studies in Computational Intelligence , 2013 .

[55]  R. Dror,et al.  Systematic Validation of Protein Force Fields against Experimental Data , 2012, PloS one.