Abstract
Protein–protein interactions (PPI) are central to most biological process and include aspects such as viral self-assembly, cell proliferation, growth, differentiation, signal transduction, and programmed cell death [1], and the last decade has seen an explosion of interest [2–4]. The human interactome has been estimated to involve ~25,000 proteins and ~650,000 interactions [5], and currently, only about 0.3 % of these have been identified [6]. In silico approaches targeting PPIs are still limited, however the versatility of GRID Molecular Interaction Fields is demonstrated through several case studies that explore how novel PPI inhibitors can be identified.
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References
Toogood PL (2002) Inhibition of protein–protein association by small molecules: approaches and progress. J Med Chem 45:1543–1558
Wilson AJ (2009) Inhibition of protein–protein interactions using designed molecules. Chem Soc Rev 38:3289–3300
Wells JA, McLendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein–protein interfaces. Nature 450:1001–1009
Yin H, Hamilton AD (2005) Strategies for targeting Protein–Protein interactions with synthetic agents. Angew Chem Int Ed, 44, 4130–4163
Stumpf MPH, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C (2008) Estimating the size of the human interactome. Proc Natl Acad Sci USA 105:6959–6964
Amaral LAN (2008) A truer measure of our ignorance. Proc Natl Acad Sci USA 105:6795–6796
Arkin MR, Wells JA (2004) Small molecule inhibitors of protein-protien interactions: progressing towards the dream. Nat Rev Drug Discovery 3:301–317
Hudis CA (2007) Trastuzumab–Mechanism of action and use in clinical practice. New Engl J Med 357:39–51
Sia SK, Carr PA, Cochran AG, Malashkevich VN, Kim PS (2002) Short constrained peptides that inhibit HIV-1 entry. Proc Natl Acad Sci USA 99:14664–14669
Oltersdorf T, Elmore SW, Shoemaker AR, Armstrong RC, Augeri DJ, Belli BA, Bruncko M, Deckwerth TL, Dinges J, Hajduk PJ, Joseph MK, Kitada S, Korsmeyer SJ, Kunzer AR, Letai A, Li C, Mitten MJ, Nettesheim DG, Ng S-C, Nimmer PM, O’Connor JM, Oleksijew A, Petros AM, Reed JC, Shen W, Tahir SK, Thompson CB, Tomaselli KJ, Wang B, Wendt MD, Zhang H, Fesik SW, Rosenberg SH (2005) An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435:677–681
Vassilev LT, Vu BT, Graves B (2004) Carvaja, l D., Podlaski, F., Filipovic, Z., Kong, N., Kammlott, U., Lukacs, C., Klein, C., Fotouhi, N., Liu, E. A.: In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2. Science 303:844–848
Higueruelo AP, Schreyer A, Bickerton GRJ, Pitt WR, Groom CR, Blundell TL (2009) Atomic interactions and profile of small molecules disrupting protein–protein interfaces: the TIMBAL Database. Chem Biol Drug Des 74:457–467
Robin WS (1998) High-throughput screening of historic collections: observations on file size, biological targets, and file diversity. Biotechnol Bioeng 61:61–67
Cochran AG (2000) Antagonists of protein–protein interactions. Chem Biol 7:R85–R94
Gandhi L, Camidge DR, de Oliveira MR, Bonomi P, Gandara D, Khaira D, Hann CL, McKeegan EM, Litvinovich E, Hemken PM, Dive C, Enschede SH, Nolan C, Chiu Y-L, Busman T, Xiong H, Krivoshik AP, Humerickhouse R, Shapiro GI, Rudin CM (2011) Phase I study of navitoclax (ABT-263), a Novel Bcl-2 family inhibitor, in patients with small-cell lung cancer and other solid tumors. J Clin Oncology 29:909–916
Goodford PJ (1985) A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 28:849–857
Ortuso F, Langer T, Alcaro S (2006) GBPM: GRID-based pharmacophore model: concept and application studies to protein–protein recognition. Bioinformatics 22:1449–1455
Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS (2007) A common reference framework for analyzing/comparing ligands and proteins. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model 47:279–294
Grigoriev A (2001) A relationship between gene expression and protein interactions on the proteome scale: Analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res 29:3513–3519
Ge H, Liu Z, Church GM, Vidal M (2001) Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat Genet 29:482–486
Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome expression data with protein–protein interactions. Genome Res 12:37–46
Skrabanek L, Saini HK, Bader GD, Enright AJ (2008) Computational prediction of protein–protein interactions. Mol Biotechnol 38:1–17
Gallet X, Charloteaux B, Thomas A, Brasseur R (2000) A fast method to predict protein interaction sites from sequences. J Mol Biol 302:917–926
Korn AP, Burnett RM (1991) Distribution and complementarity of hydropathy in multisubunit proteins. Proteins-Struct Funct Genetics 9:37–55
Young L, Jernigan RL, Covell DG (1994) A role for surface hydrophobicity in protein–protein recognition. Protein Sci 3:717–729
Mueller TD, Feigon J (2002) Solution structures of UBA domains reveal a conserved hydrophobic surface for protein–protein interactions. J Mol Biol 319:1243–1255
Ofran Y, Rost B (2003) Analysing six types of protein–protein interfaces. J Mol Biol 325:377–387
Jones S, Thornton JM (1997) Prediction of protein–protein interaction sites using patch analysis. J Mol Biol 272:133–143
Janin J, Henrick K, Moult J, Eyck LT, Sternberg MJ, Vajda S, Vakser I, Wodak SJ (2003) CAPRI: a critical assessment of predicted interactions. Proteins 52:2–9
Zhou HX, Shan YB (2001) Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins-Struct Funct Genet 44:336–343
Fariselli P, Pazos F, Valencia A, Casadio R (2002) Prediction of protein–protein interaction sites in heterocomplexes with neural networks. Eur J Biochem 269:1356–1361
Ofran Y, Rost B (2003) Predicted protein–protein interaction sites from local sequence information. FEBS Lett 544:236–239
De Genst E, Areskoug D, Decanniere K, Muyldermans S, Andersson K (2002) Kinetic and affinity predictions of a protein–protein interaction using multivariate experimental design. J Biol Chem 277:29897–29907
Thorn K, Bogan A (2001) ASEdb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics 3:284–285
Fischer T, Arunachalam K, Bailey D, Mangual V, Bakhru S, Russo R, Huang D, Paczkowski M, Lalchandani V, Ramachandra C (2003) The binding interface database (BID): a compilation of amino acid hot spots in protein interfaces. Bioinformatics 11:1453–1454
Bogan A, Thorn K (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 280:1–9
Moreira I, Fernandes P, Ramos M (2007) Hot spots-A review of the protein–protein interface determinant amino-acid residues. Proteins 68:803–812
Li J, Liu Q (2009) ‘Double water exclusion’: a hypothesis refining the O-ring theory for the hot spots at protein interfaces. Bioinformatics 25:743–750
Kruger DM, Garzon JI, Montes PC, Gohlke H (2011) Predicting protein–protein interactions with DrugScorePPI: fully flexible docking, scoring, and in silico alanine scanning. J. Cheminf 3:P36
Kruger DM, Gohlke H (2010) DrugScorePPI webserver: fast and accurate in silico alanine scanning for scoring protein–protein interactions. Nucleic Acids Res 38:W480–W486
Lise S, Archambeau C, Pontil M, Jones DT (2009) Prediction of hot spot residues at protein–protein interfaces by combining machine learning and energy-based methods. BMC Bioinformatics 10:365–382
Lise S, Buchan D, Pontil M, Jones DT (2011) Predictions of hot spot residues at Protein–Protein interfaces using support vector machines. PLoS ONE 6:e16774
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:W382–W388
Ofran Y, Rost B (2007) Protein-protein interaction hotspots carved into sequences. PLoS Comput Biol 3:e119
Darnell S, Page D, Mitchell J (2007) An automated decision-tree approach to predicting protein interaction hot spots. Proteins 68:813–823
Darnell S, LeGault L, Mitchell J (2008) KFC server: interactive forecasting of protein interaction hot spots. Nucleic Acids Research, W265–W269
Guney E, Tuncbag N, Keskin O, Gursoy A (2008) HotSprint: database of computational hot spots in protein interfaces. Nucleic Acids Research, D662–D666
Tuncbag N, Gursoy A, Keskin O (2009) Identification of computational hot spots in protein interfaces: combining solvent accessibility and interresidue potentials improves the accuracy. Bioinformatics 1513–1520
Cho K, Kim D, Lee D (2009) A feature-based approach to modeling proteinprotein interaction hot spots. Nucleic Acids Res 37:2672–2687
Xia J-F, Zhao X-M, Song J, Huang D (2010) APIS: acurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility. BMC Bioinfo 11:174–188
Landon MR, Lancia DR Jr, Yu J, Thiel SC, Vajda S (2007) Identification of hot spots within druggable binding sites of proteins by computational solvent mapping. J Med Chem 50:1231–1240
Brenke R, Kozakov D, Chuang G-Y, Beglov D, Hall D, Landon MR, Mattos C, Vajda S (2009) Fragment-based identification of druggable “hot spots” of proteins using Fourier domain correlation techniques. Bioinformatics 25:621–627
Kozakov D, Hall DR, Chuang G-Y, Cencic R, Brenke R, Grove LE, Beglov D, Pelletier J, Whitty A, Vajda S (2011) Structural conservation of druggable hot spots in protein–protein interfaces. Proc Natl Acad Sci USA 108:13528–13533
Dennis S, Kortvelyesi T, Vajda S (2002) Computational mapping identifies the binding sites of organic solvents on proteins. Proc Natl Acad Sci USA 99:4290–4295
Goodford P (2006) The basic principles of GRID, in molecular interaction fields, Cruciani G (ed) Wiley, pp 3–26
Wade RC (2006) Calculation and application of molecular interaction fields, in molecular interaction fields, Cruciani G (ed) Wiley, pp 27–42
Cross S, Cruciani G (2010) Molecular fields in drug discovery: getting old or reaching maturity? Drug Discovery Today 15:23–32
Von Itzstein M, Wu W-Y, Kok GB, Pegg MS, Dyason JC, Jin B, Phan TV, Smythe ML, White HF, Oliver SW, Colman PM, Varghese JN, Ryan DM, Woods JM, Bethel RC, Hotham VJ, Cameron JM, Penn CR (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–423
Fox T (2006) Protein selectivity studies using GRID-MIFs, in molecular interaction fields, Cruciani G (ed), Wiley, pp 45–82
Cruciani G, Pastor M, Guba W (2000) VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci 11:S29–S39
Cruciani G, Carosati E, De Boeck B, Ethirajulu K, Mackie C, Howe T, Vianello R (2005) MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J Med Chem 48:6970–6979
Milletti F, Storchi L, Sforna G, Cruciani G (2007) New and original pKa prediction method using grid molecular interaction fields. J Chem Inf Model 47:2172–2181
Bergmann R, Linusson A, Zamora I (2007) SHOP: scaffold HOPping by GRID-based similarity searches. J Med Chem 50:2708–2717
Carosati E, Cruciani G, Chiarini A, Budriesi R, Ioan P, Spisani R, Spinelli D, Cosimelli B, Fusi F, Frosini M, Matucci R, Gasparini F, Ciogli A, Stephens PJ, Devlin FJ (2006) Calcium channel antagonists discovered by a multidisciplinary approach. J Med Chem 49:5206–5216
Huang N, Shoichet BK, Irwin J (2006) Benchmarking sets for molecular docking. J Med Chem 49:6789–6801
Cross S, Baroni M, Carosati E, Benedetti P, Clementi S (2010) FLAP: GRID molecular interaction fields in virtual screening validation using the DUD data set. J Chem Inf Model 50:1442–1450
Patel Y, Gillet VJ, Bravi G, Leach AR A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J Comp-Aided Mol Des, 16, 653–681
Cross S, Cruciani G unpublished results
Alcaro S, Artese A, Ceccherini-Silberstein F, Chiarella V, Dimonte S, Ortuso F, Perno CF (2010) Computational analysis of human immunodeficiency virus (HIV) Type-1 reverse transcriptase crystallographic models based on significant conserved residues found in highly active antiretroviral therapy (HAART)-treated patients. Curr Med Chem 17:290–308
Accelrys 2003 http://www.accelrys.com
Wu G, Chai J, Suber TL, Wu JW, Du C, Wang X, Shi Y (2000) Structural basis of IAP recognition by Smac/DIABLO. Nature 408:1008–1012
Shiozaki EN, Chai J, Rigotti DJ, Riedl SJ, Li P, Srinivasula SM, Alnemri ES, Fairman R, Shi Y (2003) Mechanism of XIAP-mediated inhibition of caspase-9. Mol Cell 11:519–527
Oost TK, Sun Ch, Armstrong RC, Al-Assaad A-S, Betz SF, Decworth TL, Ding H, Elmore SW, Meadows RP, Olejniczak ET, Oleksijew A, Oltersdorf T, Rosenberrg SH, Shoemaker AR, Tomaselli KJ, Zou H, Fesik SW (2004) Discovery of potent antagonists of the antiapoptotic protein XIAP for the treatment of cancer. J Med Chem 47:4417–4426
Liu Z, Sun C, Olejniczak ET, Meadows RP, Betz SF, Oost T, Herrmann J, Wu JC, Fesik SW (2000) Structural basis for binding of Smac/DIABLO to the XIAP BIR3 domain. Nature 408:1004–1008
Sun H, Stuckey JA, Nikolovska-Coleska Z, Qin D, Meagher JL, Qiu S, Lu J, Yang C, Saito NG, Wang S (2008) Structure-based design, synthesis, evaluation, and crystallographic studies of conformationally constrained Smac mimetics as inhibitors of the X-linked inhibitor of apoptosis protein (XIAP). J Med Chem 51:7169–7180
Wist AD, Gu L, Riedl SJ, Shi Y, McLendon GL (2007) Structure-activity based study of the Smac-binding pocket within the BIR3 domain of XIAP. Bioorg Med Chem 15:2935–2943
Nikolovska-Coleska Z, Meagher JL, Jiang S, Yang CY, Qiu S, Roller PP, Stuckey JA, Wang S (2008) Interaction of a cyclic, bivalent smac mimetic with the x-linked inhibitor of apoptosis protein. Biochemistry 47:9811–9824
Cossu F, Mastrangelo E, Milani M, Sorrentino G, Lecis D, Delia D, Manzoni L, Seneci P, Scolastico C, Bolognesi M (2009) Designing smac-mimetics as antagonists of XIAP, cIAP1, and cIAP2. Biochem Biophys Res Commun 378:162–167
Mastrangelo E, Cossu F, Milani M, Sorrentino G, Lecis D, Delia D, Manzoni L, Drago C, Seneci P, Scolastico C, Rizzo V, Bolognesi M (2008) Targeting the X-linked inhibitor of apoptosis protein through 4-substituted azabicyclo [5.3.0]alkane smac mimetics. Structure, activity, and recognition principles. J Mol Biol 384:673–689
Cossu F, Milani M, Mastrangelo E, Vachette P, Servida F, Lecis D, Canevari G, Delia D, Drago C, Rizzo V, Manzoni L, Seneci P, Scolastico C, Bolognesi M (2009) Structural basis for bivalent Smac-mimetics recognition in the IAP protein family. J Mol Biol 392:630–644
Ndubaku C, Varfolomeev E, Wang L, Zobel K, Lau K, Elliott LO, Maurer B, Fedorova AV, Dynek JN, Koehler M, Hymowitz SG, Tsui V, Deshayes K, Fairbrother WJ, Flygare JA, Vucic D (2009) Antagonism of c-IAP and XIAP proteins is required for efficient induction of cell death by small-molecule IAP antagonists. ACS Chem Biol 4:557–566
Levine AJ, Hu W, Feng Z (2006) The p53 pathway: what questions remain to be explored? Cell Death Differ 13:1027–1036
Grasberger BL, Lu T, Schubert C, Parks DJ, Carver TE, Koblish HK, Cummings MD, LaFrance LV, Milkiewicz KL (2005) Discovery and cocrystal structure of benzodiazepinedione HDM2 antagonists that activate p53 in cells. J Med Chem 48:909–912
Vassilev LT, Vu BT, Graves B, Carvajal D, Podlaski F, Filipovic Z, Kong N, Kammlott U, Lukacs C, Klein C, Fotouhi N, Liu EA (2004) In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303:844–848
Fry DC, Emerson SD, Palme S, Vu BT, Liu CM, Podlaski F (2004) NMR structure of a complex between MDM2 and a small molecule inhibitor. J Biomol NMR 30:163–173
Fasan R, Dias RL, Moehle K, Zerbe O, Obrecht D, Mittl PR, Robinson JA (2006) Structure-activity studies in a family of beta-hairpin protein epitope mimetic inhibitors of the p53-HDM2 protein–protein interaction. ChemBioChem 7:515–526
Sakurai K, Schubert C, Kahne D (2006) Crystallographic analysis of an 8-mer p53 peptide analogue complexed with MDM2. J Am Chem Soc 128:11000–11001
Pazgier M, Liu M, Zou G, Yuan W, Li C, Li C, Li J, Monbo J, Zella D, Tarasov SG, Lu W (2009) Structural basis for high-affinity peptide inhibition of p53 interactions with MDM2 and MDMX. Proc Natl Acad Sci USA 106:4665–4670
Czarna A, Popowicz GM, Pecak A, Wolf S, Dubin G, Holak TA (2009) Hot, hotter, hottest. Cell Cycle 8:1176–1184
Li C, Pazgier M, Liu M, Lu WY, Lu W (2009) Apamin as a template for structure-based rational design of potent peptide activators of p53. Angew.Chem.Int.Ed.Engl 48, 8712–8715
Liu M, Li C, Pazgier M, Li C, Mao Y, Lv Y, Gu B, Wei G, Yuan W, Zhan C, Lu WY, Lu W (2010) D-peptide inhibitors of the p53-MDM2 interaction for targeted molecular therapy of malignant neoplasms. AngewProc.Natl.Acad.Sci.USA, 398, 200–213
Popowicz GM, Czarna A, Wolf S, Wang K, Wang W, Domling A, Holak TA (2010) Structures of low molecular weight inhibitors bound to MDMX and MDM2 reveal new approaches for p53-MDMX/MDM2 antagonist drug discovery. Cell Cycle 9:1104–1111
Liu M, Pazgier M, Li C, Yuan W, Li C, Lu W A (2010) left-handed solution to peptide inhibition of the p53-MDM2 interaction. Angew Chem Int Ed Engl 49, 3649–3652
Nelson BH, Willerford DM (1998) Biology of the Interleukin-2 Receptor. Adv Immunol 70:1–81
Rickert M, Wang X, Boulanger MJ, Goriatcheva N, Garcia KC (2005) The Structure of Interleukin-2 Complexed with Its Alpha Receptor. Science 308:1477–1480
Arkin MA, Randal M, DeLano WL, Hyde J, Luong TN, Oslob JD, Raphael DR, Taylor L, Wang J, McDowell RS, Wells JA, Braisted AC (2003) Binding of small molecules to an adaptive protein–protein interface. Proc Natl Acad Sci USA 100:1603–1608
Thanos CD, Randal M, Wells JA (2003) Potent small-molecule binding to a dynamic hot spot on IL-2. J Am Chem Soc 125:15280–15281
Acknowledgments
The authors would like to thank Jon Mason and Thierry Langer for their involvement in the original FLAP and GBPM methodology.
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Cross, S., Baroni, M., Ortuso, F., Alcaro, S., Cruciani, G. (2013). Disrupting Protein–Protein Interfaces Using GRID Molecular Interaction Fields. In: Mangani, S. (eds) Disruption of Protein-Protein Interfaces. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37999-4_3
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