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Modeling Binding Affinity of Pathological Mutations for Computational Protein Design

Part of the Methods in Molecular Biology book series (MIMB,volume 1529)

Abstract

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, time-consuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein–protein interfaces.

Key words

  • Protein–protein interactions
  • Hot-spots identification
  • Interface prediction
  • Evolutionary conservation
  • Protein–protein docking
  • Biomolecular dynamics simulation
  • In silico alanine scanning
  • pyDock
  • AMBER package
  • ConSurf

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References

  1. Arkin MR, Wells JA (2004) Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3:301–317

    CAS  CrossRef  PubMed  Google Scholar 

  2. DeLano WL (2002) Unraveling hot spots in binding interfaces: progress and challenges. Curr Opin Struct Biol 12:14–20

    CAS  CrossRef  PubMed  Google Scholar 

  3. Toogood PL (2002) Inhibition of protein-protein association by small molecules: approaches and progress. J Med Chem 45:1543–1558

    CAS  CrossRef  PubMed  Google Scholar 

  4. Glaser F, Pupko T, Paz I et al (2003) ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics 19:163–164

    CAS  CrossRef  PubMed  Google Scholar 

  5. Ashkenazy H, Erez E, Martz E et al (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38:W529–533

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  6. Landau M, Mayrose I, Rosenberg Y et al (2005) ConSurf 2005: the projection of evolutionary conservation scores of residues on protein structures. Nucleic Acids Res 33:W299–302

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  7. Celniker G, Nimrod G, Ashkenazy H et al (2013) ConSurf: using evolutionary data to raise testable hypotheses about protein function. Isr J Chem 53:199–206

    CAS  CrossRef  Google Scholar 

  8. Grosdidier S, Fernandez-Recio J (2008) Identification of hot-spot residues in protein-protein interactions by computational docking. BMC Bioinformatics 9:447

    CrossRef  PubMed  PubMed Central  Google Scholar 

  9. Branden CI, Tooze J (1999) Introduction to protein structure, 2nd edn. Garland Pub, New York, NY

    Google Scholar 

  10. Cheng TM, Blundell TL, Fernandez-Recio J (2007) pyDock: electrostatics and desolvation for effective scoring of rigid-body protein-protein docking. Proteins 68:503–515

    CAS  CrossRef  PubMed  Google Scholar 

  11. Case DA, Cheatham TE, Darden T et al (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

    CAS  CrossRef  PubMed  PubMed Central  Google Scholar 

  12. Miller BRI, McGee DTJ, Swails JM et al (2012) MMPBSA.py: an efficient program for end-state free energy calculations. J Chem Theor Comput 8:3314–3321

    CAS  CrossRef  Google Scholar 

  13. Haling JR, Sudhamsu J, Yen I et al (2014) Structure of the BRAF-MEK complex reveals a kinase activity independent role for BRAF in MAPK signaling. Cancer Cell 26:402–413

    CAS  CrossRef  PubMed  Google Scholar 

  14. Kiel C, Serrano L (2014) Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations. Mol Syst Biol 10:727

    CrossRef  PubMed  PubMed Central  Google Scholar 

  15. Jimenez-Garcia B, Pons C, Fernandez-Recio J (2013) pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring. Bioinformatics 29:1698–1699

    CAS  CrossRef  PubMed  Google Scholar 

  16. Gabb HA, Jackson RM, Sternberg MJ (1997) Modelling protein docking using shape complementarity, electrostatics and biochemical information. J Mol Biol 272:106–120

    CAS  CrossRef  PubMed  Google Scholar 

  17. Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25:1605–1612

    CAS  CrossRef  PubMed  Google Scholar 

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Correspondence to Juan Fernández-Recio .

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Romero-Durana, M., Pallara, C., Glaser, F., Fernández-Recio, J. (2017). Modeling Binding Affinity of Pathological Mutations for Computational Protein Design. In: Samish, I. (eds) Computational Protein Design. Methods in Molecular Biology, vol 1529. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6637-0_6

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  • DOI: https://doi.org/10.1007/978-1-4939-6637-0_6

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6635-6

  • Online ISBN: 978-1-4939-6637-0

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