Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions

  • Alexey Strokach
  • Carles Corbi-Verge
  • Joan Teyra
  • Philip M. KimEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1851)


The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein’s amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.

Key words

Computational biology Structural biology Bioinformatics Protein stability Mutations Protein engineering 



Funding: P.M.K. acknowledges support from a NSERC Discovery Grant (RGPIN-2017-064).


  1. 1.
    Rockah-Shmuel L, Tóth-Petróczy Á, Tawfik DS (2015) Systematic mapping of protein mutational space by prolonged drift reveals the deleterious effects of seemingly neutral mutations. PLoS Comput Biol 11:e1004421CrossRefGoogle Scholar
  2. 2.
    Huber CD, Kim BY, Marsden CD, Lohmueller KE (2017) Determining the factors driving selective effects of new nonsynonymous mutations. Proc Natl Acad Sci U S A 114:4465–4470CrossRefGoogle Scholar
  3. 3.
    Brender JR, Zhang Y (2015) Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLoS Comput Biol 11:e1004494CrossRefGoogle Scholar
  4. 4.
    Albanaz ATS, Rodrigues CHM, Pires DEV, Ascher DB (2017) Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design. Expert Opin Drug Discov 12:553–563CrossRefGoogle Scholar
  5. 5.
    Jelesarov I, Bosshard HR (1999) Isothermal titration calorimetry and differential scanning calorimetry as complementary tools to investigate the energetics of biomolecular recognition. J Mol Recognit 12:3–18CrossRefGoogle Scholar
  6. 6.
    Sahni N, Yi S, Taipale M et al (2015) Widespread macromolecular interaction perturbations in human genetic disorders. Cell 161:647–660CrossRefGoogle Scholar
  7. 7.
    Sun MGF, Seo M-H, Nim S et al (2016) Protein engineering by highly parallel screening of computationally designed variants. Sci Adv 2:e1600692CrossRefGoogle Scholar
  8. 8.
    Weile J, Sun S, Cote AG, et al (2017) Expanding the atlas of functional missense variation for human genes. BioRxiv 166595Google Scholar
  9. 9.
    Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31:3812–3814CrossRefGoogle Scholar
  10. 10.
    Adzhubei I, Jordan DM, Sunyaev SR (2013) Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet Chapter 7: Unit 7.20Google Scholar
  11. 11.
    Li B, Krishnan VG, Mort ME et al (2009) Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25:2744–2750CrossRefGoogle Scholar
  12. 12.
    Kircher M, Witten DM, Jain P et al (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46:310–315CrossRefGoogle Scholar
  13. 13.
    Shihab HA, Gough J, Mort M et al (2014) Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum Genomics 8:11CrossRefGoogle Scholar
  14. 14.
    Choi Y, Sims GE, Murphy S et al (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7:e46688CrossRefGoogle Scholar
  15. 15.
    Dorfman R, Nalpathamkalam T, Taylor C et al (2010) Do common in silico tools predict the clinical consequences of amino-acid substitutions in the CFTR gene? Clin Genet 77:464–473CrossRefGoogle Scholar
  16. 16.
    Shirts M, Mobley D (2013) An introduction to best practices in free energy calculations. In: Monticelli L, Salonen E (eds) Biomolecular simulations, Methods in molecular biology. Humana Press, Totowa, NJ, pp 271–311CrossRefGoogle Scholar
  17. 17.
    Benedix A, Becker CM, de Groot BL et al (2009) Predicting free energy changes using structural ensembles. Nat Methods 6:3–4CrossRefGoogle Scholar
  18. 18.
    Pires DEV, Ascher DB, Blundell TL (2014) mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30:335–342CrossRefGoogle Scholar
  19. 19.
    Laimer J, Hofer H, Fritz M et al (2015) MAESTRO - multi agent stability prediction upon point mutations. BMC Bioinformatics 16:116CrossRefGoogle Scholar
  20. 20.
    Petukh M, Li M, Alexov E (2015) Predicting binding free energy change caused by point mutations with knowledge-modified MM/PBSA method. PLoS Comput Biol 11:e1004276CrossRefGoogle Scholar
  21. 21.
    Dehouck Y, Grosfils A, Folch B et al (2009) Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 25:2537–2543CrossRefGoogle Scholar
  22. 22.
    Baugh EH, Simmons-Edler R, Müller CL et al (2016) Robust classification of protein variation using structural modelling and large-scale data integration. Nucleic Acids Res 44:2501–2513CrossRefGoogle Scholar
  23. 23.
    Berliner N, Teyra J, Çolak R et al (2014) Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation. PLoS One 9:e107353CrossRefGoogle Scholar
  24. 24.
    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:W494–W501CrossRefGoogle Scholar
  25. 25.
    Kumar MDS, Bava KA, Gromiha MM et al (2006) ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res 34:D204–D206CrossRefGoogle Scholar
  26. 26.
    Moal IH, Fernández-Recio J (2012) SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics 28:2600–2607CrossRefGoogle Scholar
  27. 27.
    Rose PW, Prlić A, Altunkaya A et al (2017) The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 45:D271–D281CrossRefGoogle Scholar
  28. 28.
    Witvliet DK, Strokach A, Giraldo-Forero AF et al (2016) ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity. Bioinformatics 32:1589–1591CrossRefGoogle Scholar
  29. 29.
    Chakravarty D, Gao J, Phillips SM et al (2017) OncoKB: a precision oncology knowledge base. JCO Precis Oncol 2017. Scholar
  30. 30.
    Das R, Baker D (2008) Macromolecular modeling with rosetta. Annu Rev Biochem 77:363–382CrossRefGoogle Scholar
  31. 31.
    Moult J, Fidelis K, Kryshtafovych A et al (2014) Critical assessment of methods of protein structure prediction (CASP)--round x. Proteins 82(Suppl 2):1–6CrossRefGoogle Scholar
  32. 32.
    McGibbon RT, Beauchamp KA, Harrigan MP et al (2015) MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109:1528–1532CrossRefGoogle Scholar
  33. 33.
    Consortium TU (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212CrossRefGoogle Scholar
  34. 34.
    Calderone A, Castagnoli L, Cesareni G (2013) mentha: a resource for browsing integrated protein-interaction networks. Nat Methods 10:690–691CrossRefGoogle Scholar
  35. 35.
    McGinnis S, Madden TL (2004) BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res 32:W20–W25CrossRefGoogle Scholar
  36. 36.
    Webb B, Sali A (2016) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 54:5.6.1–5.6.37Google Scholar
  37. 37.
    Choi Y (2012) A fast computation of pairwise sequence alignment scores between a protein and a set of single-locus variants of another protein. In: Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB ’12. ACM, New York, NY.Google Scholar
  38. 38.
    Schymkowitz J, Borg J, Stricher F et al (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:W382–W388CrossRefGoogle Scholar
  39. 39.
    Sanner MF, Olson AJ, Spehner J (1996) Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 38:305–320CrossRefGoogle Scholar
  40. 40.
    Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32:W500–W502CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Alexey Strokach
    • 1
    • 2
  • Carles Corbi-Verge
    • 2
  • Joan Teyra
    • 2
  • Philip M. Kim
    • 1
    • 2
    • 3
    Email author
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.Terrence Donnelly Centre for Cellular and Biomolecular ResearchUniversity of TorontoTorontoCanada
  3. 3.Department of Molecular GeneticsUniversity of TorontoTorontoCanada

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