Abstract
Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein–protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein–protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein–protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein–protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Qin S, Pang X, Zhou H-X (2011) Automated prediction of protein association rate constants. Structure 19(12):1744–1751
Schreiber G, Haran G, Zhou H-X (2009) Fundamental aspects of protein-protein association kinetics. Chem Rev 109(3):839–860
Garcia-Garcia J, Bonet J, Guney E, Fornes O, Planas J, Oliva B (2012) Networks of protein–protein interactions: from uncertainty to molecular details. Mol Inform 31(5):342–362
Ladbury JE, Chowdhry BZ (1996) Sensing the heat: the application of isothermal titration calorimetry to thermodynamic studies of biomolecular interactions. Chem Biol 3(10):791–801
Willander M, Al-Hilli S (2009) Analysis of biomolecules using surface plasmons. Methods Mol Biol 544:201–229
Lin T, Scott BL, Hoppe AD, Chakravarty S (2018) FRETting about the affinity of bimolecular protein-protein interactions. Protein Sci 27(10):1850–1856
Ciruela F (2008) Fluorescence-based methods in the study of protein-protein interactions in living cells. Curr Opin Biotechnol 19(4):338–343
Dries DR, Newton AC (2008) Kinetic analysis of the interaction of the C1 domain of protein kinase C with lipid membranes by stopped-flow spectroscopy. J Biol Chem 283(12):7885–7893
Li M, Simonetti FL, Goncearenco A, Panchenko AR (2016) MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions. Nucleic Acids Res 44(W1):W494–W501
Vangone A, Bonvin AM (2015) Contacts-based prediction of binding affinity in protein-protein complexes. Elife 4:e07454
Abbasi WA, Asif A, Ben-Hur A, Minhas FUAA (2018) Learning protein binding affinity using privileged information. BMC Bioinformatics 19(1):425
Yugandhar K, Gromiha MM (2014) Feature selection and classification of protein-protein complexes based on their binding affinities using machine learning approaches. Proteins 82(9):2088–2096
Horton N, Lewis M (1992) Calculation of the free energy of association for protein complexes. Protein Sci 1(1):169–181
Moal IH, Agius R, Bates PA (2011) Protein-protein binding affinity prediction on a diverse set of structures. Bioinformatics 27(21):3002–3009
Marín-López MA, Planas-Iglesias J, Aguirre-Plans J, Bonet J, Garcia-Garcia J, Fernandez-Fuentes N et al (2018) On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures. Bioinformatics 34(4):592–598
Milo R (2013) What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35(12):1050–1055
McGuffee SR, Elcock AH (2010) Diffusion, crowding & protein stability in a dynamic molecular model of the bacterial cytoplasm. PLoS Comput Biol 6(3):e1000694
Yu I, Mori T, Ando T, Harada R, Jung J, Sugita Y et al (2016) Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterial cytoplasm. Elife 01:5
Mika JT, Poolman B (2011) Macromolecule diffusion and confinement in prokaryotic cells. Curr Opin Biotechnol 22(1):117–126
Ellis RJ (2001) Macromolecular crowding: an important but neglected aspect of the intracellular environment. Curr Opin Struct Biol 11(1):114–119
Planas-Iglesias J, Marin-Lopez MA, Bonet J, Garcia-Garcia J, Oliva B (2013) iLoops: a protein-protein interaction prediction server based on structural features. Bioinformatics 29(18):2360–2362
Levinthal C (1968) Are there pathways for protein folding? J Chem Phys 65:44–45
Wass MN, Fuentes G, Pons C, Pazos F, Valencia A (2011) Towards the prediction of protein interaction partners using physical docking. Mol Syst Biol 7:469
Schweke H, Mucchielli M-H, Sacquin-Mora S, Bei W, Lopes A (2020) Protein interaction energy landscapes are shaped by functional and also non-functional partners. J Mol Biol 432(4):1183–1198
Richardson JS, Richardson DC (2002) Natural beta-sheet proteins use negative design to avoid edge-to-edge aggregation. Proc Natl Acad Sci U S A 99(5):2754–2759
Pechmann S, Levy ED, Tartaglia GG, Vendruscolo M (2009) Physicochemical principles that regulate the competition between functional and dysfunctional association of proteins. Proc Natl Acad Sci U S A 106(25):10159–10164
Deeds EJ, Ashenberg O, Gerardin J, Shakhnovich EI (2007) Robust protein protein interactions in crowded cellular environments. Proc Natl Acad Sci U S A 104(38):14952–14957
Karanicolas J, Corn JE, Chen I, Joachimiak LA, Dym O, Peck SH et al (2011) A de novo protein binding pair by computational design and directed evolution. Mol Cell 42(2):250–260
Garcia-Seisdedos H, Empereur-Mot C, Elad N, Levy ED (2017) Proteins evolve on the edge of supramolecular self-assembly. Nature 548(7666):244–247
Lo Conte L, Chothia C, Janin J (1999) The atomic structure of protein-protein recognition sites. J Mol Biol 285(5):2177–2198
Chakrabarti P, Janin J (2002) Dissecting protein-protein recognition sites. Proteins 47(3):334–343
Li X, Keskin O, Ma B, Nussinov R, Liang J (2004) Protein-protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking. J Mol Biol 344(3):781–795
Keskin O, Ma B, Nussinov R (2005) Hot regions in protein--protein interactions: the organization and contribution of structurally conserved hot spot residues. J Mol Biol 345(5):1281–1294
McCammon JA (1998) Theory of biomolecular recognition. Curr Opin Struct Biol 8(2):245–249
Tsai H-HG, Reches M, Tsai C-J, Gunasekaran K, Gazit E, Nussinov R (2005) Energy landscape of amyloidogenic peptide oligomerization by parallel-tempering molecular dynamics simulation: significant role of Asn ladder. Proc Natl Acad Sci U S A 102(23):8174–8179
Wolynes PG (2015) Evolution, energy landscapes and the paradoxes of protein folding. Biochimie 119:218–230
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res 33:W363–W367
Mashiach E, Nussinov R, Wolfson HJ (2010) FiberDock: flexible induced-fit backbone refinement in molecular docking. Proteins 78(6):1503–1519
Feliu E, Aloy P, Oliva B (2011) On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking. Protein Sci 20(3):529–541
Vreven T, Hwang H, Pierce BG, Weng Z (2012) Prediction of protein-protein binding free energies. Protein Sci 21(3):396–404
Mosca R, Céol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10(1):47–53
Zhang QC, Petrey D, Garzón JI, Deng L, Honig B (2013) PrePPI: a structure-informed database of protein-protein interactions. Nucleic Acids Res 41(Database issue):D828–D833
Poglayen D, Marín-López MA, Bonet J, Fornes O, Garcia-Garcia J, Planas-Iglesias J et al (2016) InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research. Biochem Soc Trans 44(3):917–924
Meseguer A, Dominguez L, Bota PM, Aguirre-Plans J, Bonet J, Fernandez-Fuentes N et al (2020) Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions. Protein Sci 29(10):2112–2130
Levy Y, Cho SS, Onuchic JN, Wolynes PG (2005) A survey of flexible protein binding mechanisms and their transition states using native topology based energy landscapes. J Mol Biol 346(4):1121–1145
Andrusier N, Mashiach E, Nussinov R, Wolfson HJ (2008) Principles of flexible protein-protein docking. Proteins 73(2):271–289
Stein A, Rueda M, Panjkovich A, Orozco M, Aloy P (2011) A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks. Structure 19(6):881–889
Goh C-S, Milburn D, Gerstein M (2004) Conformational changes associated with protein-protein interactions. Curr Opin Struct Biol 14(1):104–109
Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L (2005) The FoldX web server: an online force field. Nucleic Acids Res 33(Web Server issue):W382–W388
Stranges PB, Kuhlman B (2013) A comparison of successful and failed protein interface designs highlights the challenges of designing buried hydrogen bonds. Protein Sci 22(1):74–82
Jankauskaite J, Jiménez-García B, Dapkunas J, Fernández-Recio J, Moal IH (2019) SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics 35(3):462–469
Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F et al (2014) The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42(Database issue):D358–D363
Siebenmorgen T, Zacharias M (2020) Computational prediction of protein–protein binding affinities. WIREs Comput Mol Sci 10(3):e1448
Siebenmorgen T, Zacharias M (2019) Evaluation of predicted protein-protein complexes by binding free energy simulations. J Chem Theory Comput 15(3):2071–2086
Pierce B, Weng Z (2007) ZRANK: reranking protein docking predictions with an optimized energy function. Proteins 67(4):1078–1086
Alford RF, Leaver-Fay A, Jeliazkov JR, O’Meara MJ, DiMaio FP, Park H et al (2017) The Rosetta all-atom energy function for macromolecular modeling and design. J Chem Theory Comput 13(6):3031–3048
Aguirre-Plans J, Meseguer A, Molina-Fernandez R, Marín-López MA, Jumde G, Casanova K, Bonet J, Fornes O, Fernandez-Fuentes N, Oliva B (2018) SPServer: Split-Statistical Potentials for the analysis of protein structures and protein-protein interactions. BMC Bioinformatics 22:4
Takemura K, Matubayasi N, Kitao A (2018) Binding free energy analysis of protein-protein docking model structures by evERdock. J Chem Phys 148(10):105101
Barradas-Bautista D, Moal IH, Fernández-Recio J (2017) A systematic analysis of scoring functions in rigid-body protein docking: the delicate balance between the predictive rate improvement and the risk of overtraining. Proteins 85(7):1287–1297
Segura J, Marín-López MA, Jones PF, Oliva B, Fernandez-Fuentes N (2015) VORFFIP-driven dock: V-D2OCK, a fast and accurate protein docking strategy. PLoS One 10(3):e0118107
Segura J, Jones PF, Fernandez-Fuentes N (2011) Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams. BMC Bioinformatics 12:352
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC et al (2019) The Pfam protein families database in 2019. Nucleic Acids Res 47(D1):D427–D432
Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285(4):1735–1747
Garcia-Garcia J, Valls-Comamala V, Guney E, Andreu D, Muñoz FJ, Fernandez-Fuentes N et al (2017) iFrag: a protein–protein Interface prediction server based on sequence fragments. J Mol Biol 429(3):382–389
UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47(D1):D506–D515
Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC et al (2004) UCSF chimera--a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612
Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 86:2.9.1–2.9.37
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Meseguer, A., Bota, P., Fernández-Fuentes, N., Oliva, B. (2022). Prediction of Protein–Protein Binding Affinities from Unbound Protein Structures. In: Vanhaelen, Q. (eds) Computational Methods for Estimating the Kinetic Parameters of Biological Systems. Methods in Molecular Biology, vol 2385. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1767-0_16
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1767-0_16
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1766-3
Online ISBN: 978-1-0716-1767-0
eBook Packages: Springer Protocols