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In Silico Approaches to Identify Mutagenesis Targets to Probe and Alter Protein–Cofactor and Protein–Protein Functional Relationships

  • Brian A. Dow
  • Esha Sehanobish
  • Victor L. Davidson
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1498)

Abstract

When performing site-directed mutagenesis experiments to study protein structure–function relationships, ideally one would know the structure of the protein under study. It is also very useful to have structures of multiple related proteins in order to determine whether or not particular amino acid residues are conserved in the structures either in the active site of an enzyme at the surface of a protein or at a putative protein–protein interface. While many protein structures are available in the Protein Data Base (PDB), a structure of the protein of interest may not be available. In the study of reversible and often transient protein–protein interactions it is rare to have a structure of the complex of the two interacting proteins. In this chapter, methods are described for comparing protein structures, generating putative structures of proteins with homology models based on the protein primary sequence, and generating docking models to predict interaction sites between proteins and cofactor–protein interactions. The rationale used to predict mutagenesis targets from these structures and models is also described.

Key words

Homology model Ligand docking Protein Data Bank (PDB) Protein docking Structural alignment 

References

  1. 1.
    Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics 22:195–201CrossRefPubMedGoogle Scholar
  2. 2.
    Ye Y, Godzik A (2003) Flexible structure alignment by chaining aligned fragment pairs allowing twists. Bioinformatics 19(Suppl 2):246–255CrossRefGoogle Scholar
  3. 3.
    Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 11:739–747CrossRefPubMedGoogle Scholar
  4. 4.
    Pierce BG, Wiehe K, Hwang H, Kim BH, Vreven T, Weng Z (2014) ZDOCK server: interactive docking prediction of protein-protein complexes and symmetric multimers. Bioinformatics 30:1771–1773CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Grosdidier A, Zoete V, Michielin O (2011) SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res 39:W270–277CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Grosdidier A, Zoete V, Michielin O (2011) Fast docking using the CHARMM force field with EADock DSS. J Comput Chem 32:2149–2159CrossRefPubMedGoogle Scholar
  8. 8.
    Campillo-Brocal JC, Chacon-Verdu MD, Lucas-Elio P, Sanchez-Amat A (2015) Distribution in microbial genomes of genes similar to lodA and goxA which encode a novel family of quinoproteins with amino acid oxidase activity. Bmc Genomics 16:231CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Brian A. Dow
    • 1
  • Esha Sehanobish
    • 1
  • Victor L. Davidson
    • 1
  1. 1.Burnett School of Biomedical Sciences, College of MedicineUniversity of Central FloridaOrlandoUSA

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