Abstract
Three-dimensional structures of proteins offer an opportunity for the rational design of small molecules to modulate protein-protein interactions. The presence of a well-defined binding pocket on the surface of protein complexes, particularly at their interface, can be used for docking-based virtual screening of chemical libraries. Several approaches have been developed to identify binding pockets that are implemented in programs such as SiteMap, fpocket, and FTSite. These programs enable the scoring of these pockets to determine whether they are suitable to accommodate high-affinity small molecules. Virtual screening of commercial or combinatorial libraries can be carried out to enrich these libraries and select compounds for further experimental validation. In virtual screening, a compound library is docked to the target protein. The resulting structures are scored and ranked for the selection and experimental validation of top candidates. Molecular docking has been implemented in a number of computer programs such as AutoDock Vina. We select a set of protein-protein interactions that have been successfully inhibited with small molecules in the past. Several computer programs are applied to identify pockets on the surface, and molecular docking is conducted in an attempt to reproduce the binding pose of the inhibitors. The results highlight the strengths and limitations of computational methods for the design of PPI inhibitors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144:986–998
Ngounou Wetie AG, Sokolowska I, Woods AG et al (2013) Protein–protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches. Cell Mol Life Sci 71:205–228
White AW, Westwell AD, Brahemi G (2008) Protein–protein interactions as targets for small-molecule therapeutics in cancer. Expert Rev Mol Med 10:e8
Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242
Lipman DJ, Pearson WR (1985) Rapid and sensitive protein similarity searches. Science 227:1435–1441
Li L, Bum-Erdene K, Baenziger PH et al (2010) BioDrugScreen: a computational drug design resource for ranking molecules docked to the human proteome. Nucleic Acids Res 38:D765–D773
Huang YJ, Hang D, Lu LJ et al (2008) Targeting the human cancer pathway protein interaction network by structural genomics. Mol Cell Proteomics 7(10):2048–2060
Mosca R, Ceol A, Aloy P (2013) Interactome3D: adding structural details to protein networks. Nat Methods 10:47–53
Li L, Meroueh SO (2008) Receptor-ligand interactions in biological systems. In: Encyclopedia for the life sciences. Wiley, London, p. 19. http://onlinelibrary.wiley.com/book/10.1002/9780470048672/homepage/EditorsContributors.html
Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148
Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168
Kuhn D, Weskamp N, Hullermeier E et al (2007) Functional classification of protein kinase binding sites using cavbase. ChemMedChem 2:1432–1447
Ngan CH, Hall DR, Zerbe B et al (2012) FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28:286–287
Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49:377–389
Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53:5858–5867
Stark C, Breitkreutz BJ, Reguly T et al (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539
Licata L, Briganti L, Peluso D et al (2012) MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 40:D857–D861
UniProt C (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75
Porter CT, Bartlett GJ, Thornton JM (2004) The catalytic site atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res 32:D129–D133
Leach AR, Gillet VJ, Lewis RA et al (2009) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539–558
Hubbard RE (2011) Structure-based drug discovery and protein targets in the CNS. Neuropharmacology 60:7–23
Cheng T, Li Q, Zhou Z et al (2012) Structure-based virtual screening for drug discovery: a problem-centric review. AAPS J 14:133–141
Khanna M, Wang F, Jo I et al (2011) Targeting multiple conformations leads to small molecule inhibitors of the uPAR·uPA protein–protein interaction that block cancer cell invasion. ACS Chem Biol 6:1232–1243
Scheper J, Guerra-Rebollo M, Sanclimens G et al (2010) Protein–protein interaction antagonists as novel inhibitors of non-canonical polyubiquitylation. PLoS One 5:e11403
Koes D, Khoury K, Huang Y et al (2012) Enabling large-scale design, synthesis and validation of small molecule protein–protein antagonists. PLoS One 7:e32839
Geppert T, Bauer S, Hiss JA et al (2012) Immunosuppressive small molecule discovered by structure-based virtual screening for inhibitors of protein–protein interactions. Angew Chem Int Edit 51:258–261
Kuntz ID, Blaney JM, Oatley SJ et al (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288
Makino S, Kuntz ID (1997) Automated flexible ligand docking method and its application for database search. J Comput Chem 18:1812–1825
Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202
Jones G, Willett P, Glen RC et al (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748
Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245:43–53
Rarey M, Kramer B, Lengauer T et al (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489
Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749
Halgren TA, Murphy RB, Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759
Pierce BG, Hourai Y, Weng Z (2011) Accelerating protein docking in ZDOCK using an advanced 3D convolution library. PLoS One 6:e24657
McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51:578–596
Pedretti A, Villa L, Vistoli G (2004) VEGA – an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming. J Comput-Aided Mol Des 18:167–173
Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321
Abagyan R, Totrov M, Kuznetsov D (1994) ICM – a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comp Chem 15:488–506
Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461
Obiol-Pardo C, Alcarraz-Vizán G, Cascante M et al (2012) Diphenyl urea derivatives as inhibitors of transketolase: a structure-based virtual screening. PLoS One 7:e32276
Dessal AL, Prades R, Giralt E et al (2011) Rational design of a selective covalent modifier of G protein βγ subunits. Mol Pharm 79:24–33
Trosset J-Y, Dalvit C, Knapp S et al (2006) Inhibition of protein–protein interactions: the discovery of druglike β-catenin inhibitors by combining virtual and biophysical screening. Proteins 64:60–67
Grüneberg S, Stubbs MT, Klebe G (2002) Successful virtual screening for novel inhibitors of human carbonic anhydrase: strategy and experimental confirmation. J Med Chem 45:3588–3602
Elokely KM, Doerksen RJ (2013) Docking Challenge: Protein Sampling and Molecular Docking Performance. J Chem Inf Model 53:1934–1945
Lill MA, Winiger F, Vedani A et al (2005) Impact of Induced Fit on Ligand Binding to the Androgen Receptor: A Multidimensional QSAR Study To Predict Endocrine-Disrupting Effects of Environmental Chemicals. J Med Chem 48:5666–5674
Sherman W, Day T, Jacobson MP et al (2005) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49:534–553
Arooj M, Sakkiah S, Kim S et al (2013) A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors. PLoS One 8:e63030
Zhou S, Li Y, Hou T (2013) Feasibility of using molecular docking-based virtual screening for searching dual target kinase inhibitors. J Chem Inf Model 53:982–996
Li Y, Kim DJ, Ma W et al (2011) Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures. J Chem Inf Model 51:2904–2914
Isvoran A, Badel A, Craescu C et al (2011) Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein–protein interactions. BMC Struct Biol 11:24
Knegtel RMA, Kuntz ID, Oshiro CM (1997) Molecular docking to ensembles of protein structures. J Mol Biol 266:424–440
Carlson HA, Masukawa KM, Rubins K et al (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43:2100–2114
Kukol A (2011) Consensus virtual screening approaches to predict protein ligands. Eur J Med Chem 46:4661–4664
Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757–1768
Greenwood JR, Calkins D, Sullivan AP et al (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aid Mol Des 24:591–604
Cramer CJ, Truhlar DG (1992) An SCF solvation model for the hydrophobic effect and absolute free energies of aqueous solvation. Science 256:213–217
Cramer CJ, Truhlar DG (1992) AM1-SM2 and PM3-SM3 parameterized SCF solvation models for free energies in aqueous solution. J Comput Aided Mol Des 6:629–666
Hawkins PCD, Skillman AG, Nicholls A (2006) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50:74–82
Tetko IV, Gasteiger J, Todeschini R et al (2005) Virtual computational chemistry laboratory-design and description. J Comput-Aided Mol Des 19:453–463
Gaulton A, Bellis LJ, Bento AP et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100–D1107
Li Q, Cheng T, Wang Y et al (2010) PubChem as a public resource for drug discovery. Drug Discov Today 15:1052–1057
Knox C, Law V, Jewison T et al (2011) DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res 39:D1035–D1041
Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–D201
Still WC, Tempczyk A, Hawley RC et al (1990) Semianalytical treatment of solvation for molecular mechanics and dynamics. J Am Chem Soc 112:6127–6129
Luo R, David L, Gilson MK (2002) Accelerated Poisson–Boltzmann calculations for static and dynamic systems. J Comput Chem 23:1244–1253
Eldridge MD, Murray CW, Auton TR et al (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445
Li L, Wang B, Meroueh SO (2011) Support vector regression scoring of receptor–ligand complexes for rank-ordering and virtual screening of chemical libraries. J Chem Inf Model 51:2132–2138
Muegge I, Martin YC (1999) A general and fast scoring function for protein − ligand interactions: a simplified potential approach. J Med Chem 42:791–804
Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein–ligand interactions. J Mol Biol 295:337–356
Li L, Khanna M, Jo I et al (2011) Target-specific support vector machine scoring in structure-based virtual screening: computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model 51:755–759
Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein– protein interfaces. Nature 450:1001–1009
Malek TR (2003) The main function of IL-2 is to promote the development of T regulatory cells. J Leukoc Biol 74:961–965
Willis S, Day CL, Hinds MG et al (2003) The Bcl-2-regulated apoptotic pathway. J Cell Sci 116:4053–4056
Moll UM, Petrenko O (2003) The MDM2-p53 interaction. Mol Cancer Res 1:1001–1008
Muller M, Demeret C (2012) The HPV E2-host protein–protein interactions: a complex hijacking of the cellular network. Open Virol J 6:173–189
Hughes FJ, Romanos MA (1993) E1 protein of human papillomavirus is a DNA helicase/ATPase. Nucleic Acids Res 21:5817–5823
Pazos M, Natale P, Vicente M (2013) A specific role for the ZipA protein in cell division: stabilization of the FtsZ protein. J Biol Chem 288:3219–3226
Locksley RM, Killeen N, Lenardo MJ (2001) The TNF and TNF receptor superfamilies: integrating mammalian biology. Cell 104:487–501
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Xu, D., Wang, B., Meroueh, S.O. (2015). Structure-Based Computational Approaches for Small-Molecule Modulation of Protein-Protein Interactions. In: Meyerkord, C., Fu, H. (eds) Protein-Protein Interactions. Methods in Molecular Biology, vol 1278. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2425-7_5
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2425-7_5
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-2424-0
Online ISBN: 978-1-4939-2425-7
eBook Packages: Springer Protocols