Generic placeholder image

Current Cancer Drug Targets

Editor-in-Chief

ISSN (Print): 1568-0096
ISSN (Online): 1873-5576

Review Article

Exploring Proteomic Drug Targets, Therapeutic Strategies and Protein - Protein Interactions in Cancer: Mechanistic View

Author(s): Khalid Bashir Dar, Aashiq Hussain Bhat, Shajrul Amin, Syed Anjum, Bilal Ahmad Reshi, Mohammad Afzal Zargar, Akbar Masood and Showkat Ahmad Ganie*

Volume 19, Issue 6, 2019

Page: [430 - 448] Pages: 19

DOI: 10.2174/1568009618666180803104631

Price: $65

Abstract

Protein-Protein Interactions (PPIs) drive major signalling cascades and play critical role in cell proliferation, apoptosis, angiogenesis and trafficking. Deregulated PPIs are implicated in multiple malignancies and represent the critical targets for treating cancer. Herein, we discuss the key protein-protein interacting domains implicated in cancer notably PDZ, SH2, SH3, LIM, PTB, SAM and PH. These domains are present in numerous enzymes/kinases, growth factors, transcription factors, adaptor proteins, receptors and scaffolding proteins and thus represent essential sites for targeting cancer. This review explores the candidature of various proteins involved in cellular trafficking (small GTPases, molecular motors, matrix-degrading enzymes, integrin), transcription (p53, cMyc), signalling (membrane receptor proteins), angiogenesis (VEGFs) and apoptosis (BCL-2family), which could possibly serve as targets for developing effective anti-cancer regimen. Interactions between Ras/Raf; X-linked inhibitor of apoptosis protein (XIAP)/second mitochondria-derived activator of caspases (Smac/DIABLO); Frizzled (FRZ)/Dishevelled (DVL) protein; beta-catenin/T Cell Factor (TCF) have also been studied as prospective anticancer targets. Efficacy of diverse molecules/ drugs targeting such PPIs although evaluated in various animal models/cell lines, there is an essential need for human-based clinical trials. Therapeutic strategies like the use of biologicals, high throughput screening (HTS) and fragment-based technology could play an imperative role in designing cancer therapeutics. Moreover, bioinformatic/computational strategies based on genome sequence, protein sequence/structure and domain data could serve as competent tools for predicting PPIs. Exploring hot spots in proteomic networks represents another approach for developing targetspecific therapeutics. Overall, this review lays emphasis on a productive amalgamation of proteomics, genomics, biochemistry, and molecular dynamics for successful treatment of cancer.

Keywords: Computational methods, drug targets, proteomics, small molecule inhibitor, protein-protein interactions, T Cell Factor.

Graphical Abstract
[1]
Berggard, T.; Linse, S.; James, P. Methods for the detection and analysis of protein protein interactions. Proteomics, 2007, 7, 2833-2842.
[2]
De-Las-Rivas, J.; Fontanillo, C. Protein-protein interactions essentials: Key concepts to building and analyzing interactome networks. PLOS Comput. Biol., 2010, 6(6)e1000807
[3]
Westermarck, J.; Ivaska, J.; Corthals, G.L. Identification of protein interactions involved in cellular signaling. Mol. Cell. Proteomics, 2013, 12, 1752-1763.
[4]
Sukenik, S.; Ren, P.; Gruebele, M. Weak protein-protein interactions in live cells are quantified by cell-volume modulation. Proc. Natl. Acad. Sci. , 2017, 114(26), 6776-6781.
[5]
Feng, Y.; Wang, Q.; Wang, T. Drug target protein-protein interaction networks: A systematic perspective. BioMed Res. Int., 2017, 20171289259
[6]
Stumpf, M.P.; Thorne, T.; De-Silva, E.; Stewart, R.; An, H.J.; Lappe, M.; Wiuf, C. Estimating the size of the human interactome. Proc. Natl. Acad. Sci. , 2008, 105, 6959-6964.
[7]
Keskin, O.; Tuncbag, N.; Gursoy, A. Predicting protein–protein interactions from the molecular to the proteome level. Chem. Rev., 2016, 116(8), 4884-4909.
[8]
Li, X.H.; Chavali, P.L.; Babu, M.M. Capturing dynamic protein interactions: A method based on heat denaturation reveals how proteins interact in different cells. Science, 2018, 359(6380), 1105-1106.
[9]
Berridge, M.J. Cell Signalling Biology. Module 6, Spatial and Temporal Aspects of Signalling; Portland Press Ltd.: London, 2008, pp. 50-57.
[10]
Morlacchi, P.; Robertson, F.M.; Klostergaard, J.; McMurray, J.S. Targeting SH2 domains in breast cancer. Future Med. Chem., 2014, 6, 1909-1926.
[11]
Smithgall, T.E. SH2 and SH3 domains: Potential targets for anti-cancer drug design. J. Pharmacol. Toxicol. Methods, 1995, 34, 125-132.
[12]
Matthews, J.M.; Lester, K.; Joseph, S.; David, J.C. LIM-domain-only proteins in cancer. Nat. Rev. Cancer, 2013, 13, 111-122.
[13]
Lo, S.H. Tensin. Int. J. Biochem. Cell Biol., 2004, 36, 31-34.
[14]
Yaffe, M. Phosphotyrosine-binding domains in signal transduction. Nat. Rev. Mol. Cell Biol., 2002, 3, 177-186.
[15]
Mercurio, F.A.; Leone, M. The sam domain of EphA2 receptor and its relevance to cancer: A novel challenge for drug discovery? Curr. Med. Chem., 2016, 23, 4718-4734.
[16]
Facciuto, F.; Cavatorta, A.L.; Valdano, M.B.; Marziali, F.; Gardiol, D. Differential expression of PDZ domain proteins in human diseases-challenging topics and novel issues. FEBS J., 2012, 279, 3538-3548.
[17]
Meuillet, E.; Lemos, R.; Moses, S.; Zuohe, S.; Ihle, N.; Zhang, S.; Du-Cuny, L.; Mash, E.; Powis, G. Novel small molecule inhibitors targeting the pleckstrin homology (PH) domain of Akt. Cancer Research 100th AACR Annual Meeting, 2009, pp. 18-22.
[18]
Rainero, E.; Caswell, P.T.; Muller, P.A.; Grindlay, J.; McCaffrey, M.W.; Zhang, Q.; Wakelam, M.J.; Vousden, K.H.; Graziani, A.; Norman, J.C. Diacylglycerol kinase α controls RCP-dependent integrin trafficking to promote invasive migration. J. Cell Biol., 2012, 196, 277-295.
[19]
Caswell, P.T.; Spence, H.J.; Parsons, M.; White, D.P.; Clark, K.; Cheng, K.W.; Mills, G.B.; Humphries, M.J.; Messent, A.J.; Anderson, K.I.; McCaffrey, M.W. Rab25 associates with α5β1 integrin to promote invasive migration in 3D microenvironments. Dev. Cell, 2007, 13, 496-510.
[20]
Kerber, M.L.; Jacobs, D.T.; Campagnola, L.; Dunn, B.D.; Yin, T.; Sousa, A.D.; Quintero, O.A.; Cheney, R.E. A novel form of motility in filopodia revealed by imaging myosin-X at the single-molecule level. Curr. Biol., 2009, 19, 967-973.
[21]
Feng, S.; Knodler, A.; Ren, J.; Zhang, J.; Zhang, X.; Hong, Y.; Huang, S.; Peranen, J.; Guo, W.A. Rab8 guanine nucleotide exchange factor-effector interaction network regulates primary ciliogenesis. J. Biol. Chem., 2012, 287, 15602-15609.
[22]
Beaumont, K.A.; Hamilton, N.A.; Moores, M.T.; Brown, D.L.; Ohbayashi, N.; Cairncross, O.; Cook, A.L.; Smith, A.G.; Misaki, R.; Fukuda, M.; Taguchi, T. The recycling endosome protein Rab17 regulates melanocytic filopodia formation and melanosome trafficking. Traffic, 2011, 12, 627-643.
[23]
Tzeng, H.T.; Wang, Y.C. Rab-mediated vesicle trafficking in cancer. J. Biomed. Sci., 2016, 23(1), 70.
[24]
Mai, A.; Veltel, S.; Pellinen, T.; Padzik, A.; Coffey, E.; Marjomäki, V.; Ivaska, J. Competitive binding of Rab21 and p120 RasGAP to integrins regulates receptor traffic and migration. J. Cell Biol., 2011, 194, 291-306.
[25]
Barbarin, A.; Frade, R. Procathepsin L secretion, which triggers tumor progression, is regulated by Rab4A in human melanoma cells. Biochem. J., 2011, 437, 97-107.
[26]
Goldenring, J.R. A central role for vesicle trafficking in epithelial neoplasia: intracellular highways to carcinogenesis. Nat. Rev. Cancer, 2013, 13, 813-820.
[27]
Guda, P.; Chittur, S.V.; Guda, C. Comparative analysis of protein-protein interactions in cancer-associated genes. Genomics Proteomics & Bioinformatics, 2009, 7, 25-36.
[28]
Horwitz, K.B.; Jackson, T.A.; Bain, D.L.; Richer, J.K.; Takimoto, G.S.; Tung, L. Nuclear receptor coactivators and corepressors. Mol. Endocrinol., 1996, 10, 1167-1177.
[29]
Angeles, C. Tecalco-Cruz, Ríos-López D.G.; Vázquez-Victorio G.; Rosales-Alvarez R.; Macías-Silva M. Transcriptional cofactors Ski and SnoN are major regulators of the TGF-β/Smad signaling pathway in health and disease. Signal Trans. Target. Ther, 2018, 3(15)
[30]
Geffroy, N.; Guédin, A.; Dacquet, C.; Lefebvre, P. Cell cycle regulation of breast cancer cells through estrogen-induced activities of ERK and Akt protein kinases. Mol. Cell. Endocrinol., 2005, 237, 11-23.
[31]
Ballare, C.; Uhrig, M.; Bechtold, T.; Sancho, E.; Di, D.M.; Migliaccio, A.; Auricchio, F.; Beato, M. Two domains of the progesterone receptor interact with the estrogen receptor and are required for progesterone activation of the c-Src/Erk pathway in mammalian cells. Mol. Cell. Biol., 2003, 23, 1994-2008.
[32]
Yulia, L.; Konhilas, J.P. The complex nature of estrogen signaling in breast cancer: enemy or ally? Biosci. Rep., 2016, 36(3)e0035
[33]
Steelman, L.S.; Chappell, W.H.; Abrams, S.L.; Kempf, C.R.; Long, J.; Laidler, P.; Mijatovic, S.; Maksimovic-Ivanic, D.; Stivala, F.; Mazzarino, M.C.; Donia, M. Roles of the Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways in controlling growth and sensitivity to therapy-implications for cancer and aging. Aging (Albany NY), 2011, 3, 192-222.
[34]
Serra, V.; Scaltriti, M.; Prudkin, L.; Eichhorn, P.J.; Ibrahim, Y.H.; Chandarlapaty, S.; Markman, B.; Rodriguez, O.; Guzman, M.; Rodriguez, S.; Gili, M. PI3K inhibition results in enhanced HER signaling and acquired ERK dependency in HER2-overexpressing breast cancer. Oncogene, 2011, 30, 2547-2557.
[35]
Kampen, K.R. Membrane proteins: The key players of a cancer cell. J. Membr. Biol., 2011, 242, 69-74.
[36]
Boer, J.M.; Huber, W.K.; Sültmann, H.; Wilmer, F.; Von-Heydebreck, A.; Haas, S.; Korn, B.; Gunawan, B.; Vente, A.; Füzesi, L.; Vingron, M. Identification and classification of differentially expressed genes in renal cell carcinoma by expression profiling on a global human 31,500-element cDNA array. Genome Res., 2001, 11, 1861-1870.
[37]
Siemann, D.W.; Chaplin, D.J.; Horsman, M.R. Vascular-targeting therapies for treatment of malignant disease. Cancer, 2004, 100, 2491-2499.
[38]
Lee, S.H.; Jeong, D.; Han, Y.K.; Baek, M.J. Pivotal role of vascular endothelial growth factor pathway in tumor angiogenesis. Ann. Surg. Treat. Res., 2015, 89, 1-8.
[39]
Bergers, G.; Javaherian, K.; Lo, K.M.; Folkman, J.; Hanahan, D. Effects of angiogenesis inhibitors on multistage carcinogenesis in mice. Science, 1999, 284, 808-812.
[40]
Los, M.; Roodhart, J.M.L.; Voest, E.E. Target practice: Lessons from phase III trials with bevacizumab and vatalanib in the treatment of advanced colorectal cancer. Oncology, 2007, 12, 443-450.
[41]
Al‐Husein, B.; Abdalla, M.; Trepte, M.; DeRemer, D.L.; Somanath, P.R. Antiangiogenic therapy for cancer: An update. Pharmacotherapy: J. Human Pharmacol. Drug Ther, 2012, 32, 095-111.
[42]
Feller, S.M.; Lewitzky, M. Potential disease targets for drugs that disrupt protein-protein interactions of Grb2 and Crk family adaptors. Curr. Pharm. Des., 2006, 12, 529-548.
[43]
Furet, P.; Gay, B.; Caravatti, G.; García-Echeverria, C.; Rahuel, J.; Schoepfer, J.; Fretz, H. Structure-based design and synthesis of high affinity tripeptide ligands of the Grb2-SH2 domain. J. Med. Chem., 1998, 41, 3442-3449.
[44]
Gao, Y.; Luo, J.; Yao, Z.J.; Guo, R.; Zou, H.; Kelley, J.; Voigt, J.H.; Yang, D.; Burke, T.R. Inhibition of Grb2 SH2 domain binding by non-phosphate-containing ligands. 2. 4-(2-Malonyl) phenylalanine as a potent phosphotyrosyl mimetic. J. Med. Chem., 2000, 43, 911-920.
[45]
Jesus-Perez-de-Vega, M.; Martin-Martinez, M.; Gonzalez-Muniz, R. Modulation of protein-protein interactions by stabilizing/mimicking protein secondary structure elements. Curr. Top. Med. Chem., 2007, 7, 33-62.
[46]
Sosman, J.A.; Puzanov, I.; Atkins, M.B. Opportunities and obstacles to combination targeted therapy in renal cell cancer. Clin. Cancer Res., 2007, 13, 764s-769s.
[47]
Kampen, K.R. Membrane proteins: The key players of a cancer cell. J. Membr. Biol., 2011, 242, 69-74.
[48]
Maeng, J.H.; Lee, D.H.; Jung, K.H.; Bae, Y.H.; Park, I.S.; Jeong, S.; Jeon, Y.S.; Shim, C.K.; Kim, W.; Kim, J.; Lee, J. Multifunctional doxorubicin loaded superparamagnetic iron oxide nanoparticles for chemotherapy and magnetic resonance imaging in liver cancer. Biomaterials, 2010, 31, 4995-5006.
[49]
Milane, L.; Duan, Z.; Amiji, M. Development of EGFR-targeted polymer blend nanocarriers for combination paclitaxel/lonidamine delivery to treat multi-drug resistance in human breast and ovarian tumor cells. Mol. Pharm., 2011, 8, 185-203.
[50]
Lee, T.I.; Young, R.A. Transcriptional regulation and its misregulation in disease. Cell, 2013, 152, 1237-1251.
[51]
Matthews, C.P.; Colburn, N.H.; Young, M.R. AP-1 a target for cancer prevention. Curr. Cancer Drug Targets, 2007, 7, 317-324.
[52]
Darnell, J.E. Transcription factors as targets for cancer therapy. Nat. Rev. Cancer, 2002, 2, 740-749.
[53]
Lee, E.Y.; Muller, W.J. Oncogenes and tumor suppressor genes. Cold Spring Harb. Perspect. Biol., 2010, 2a003236
[54]
Bretones, G.; Delgado, M.D.; León, J. Myc and cell cycle control. Biochimica et Biophysica Acta (BBA)-. Gene Regulatory Mech., 2015, 1849, 506-516.
[55]
Garraway, L.A.; Lander, E.S. Lessons from the cancer genome. Cell, 2013, 153, 17-37.
[56]
Ouyang, X.; Jessen, W.J.; Al-Ahmadie, H.; Serio, A.M.; Lin, Y.; Shih, W.J.; Reuter, V.E.; Scardino, P.T.; Shen, M.M.; Aronow, B.J. Vickers. A.J. Activator protein-1 transcription factors are associated with progression and recurrence of prostate cancer. Cancer Res., 2008, 68, 2132-2144.
[57]
Semenza, G.L. Involvement of hypoxia-inducible factor 1 in human cancer. Intern. Med., 2002, 41, 79-83.
[58]
Van-Delft, M.F.; Huang, D.C.S. How the Bcl-2 family of proteins interacts to regulate apoptosis. Cell Res., 2006, 16, 203-213.
[59]
Sattler, M.; Liang, H.; Nettesheim, D.; Meadows, R.P.; Harlan, J.E.; Eberstadt, M. Yoon.; H.S.; Shuker, S.B.; Chang, B.S.; Minn, A.J.; Thompson, C.B. Structure of Bcl-x L-Bak peptide complex: recognition between regulators of apoptosis. Science, 1997, 275, 983-986.
[60]
Fesik, S.W. Promoting apoptosis as a strategy for cancer drug discovery. Nat. Rev. Cancer, 2005, 5, 876-885.
[61]
Oltersdorf, T.; Elmore, S.W.; Shoemaker, A.R.; Armstrong, R.C.; Augeri, D.J.; Belli, B.A.; Bruncko, M.; Deckwerth, T.L.; Dinges, J.; Hajduk, P.J.; Joseph, M.K. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature, 2005, 435, 677-681.
[62]
Barker, N.; Clevers, H. Mining the Wnt pathway for cancer therapeutics. Nat. Rev. Drug Discov., 2006, 5, 997-1014.
[63]
Fujii, N.; You, L.; Xu, Z.; Uematsu, K.; Shan, J.; He, B.; Mikami, I.; Edmondson, L.R.; Neale, G.; Zheng, J.; Guy, R.K. An antagonist of dishevelled protein-protein interaction suppresses β-catenin-dependent tumor cell growth. Cancer Res., 2007, 67, 573-579.
[64]
White, A.W.; Westwell, A.D.; Brahemi, G. Protein protein interactions as targets for small-molecule therapeutics in cancer. Expert Rev. Mol. Med., 2008, 10, e8.
[65]
Emami, K.H.; Nguyen, C.; Ma, H.; Kim, D.H.; Jeong, K.W.; Eguchi, M.; Moon, R.T.; Teo, J.L.; Oh, S.W.; Kim, H.Y.; Moon, S.H. A small molecule inhibitor of β-catenin/cyclic AMP response element-binding protein transcription. Proc. Natl. Acad. Sci. , 2004, 101, 12682-12687.
[66]
Walker, K.; Olson, M.F. Targeting Ras and Rho GTPases as opportunities for cancer therapeutics. Curr. Opin. Genet. Dev., 2005, 15, 62-68.
[67]
Warne, P.H.; Viciana, P.R.; Downward, J. Direct interaction of Ras and the amino terminal region of Raf-1 in-vitro. Nature, 1993, 364, 352-355.
[68]
Yang, S.; Liu, G. Targeting the Ras/Raf/MEK/ERK pathway in hepatocellular carcinoma. ([)Review). Oncol. Lett., 2017, 13, 1041-1047.
[69]
Wecksler, A.T.; Hwang, S.H.; Liu, J.Y.; Wettersten, H.I.; Morisseau, C.; Wu, J.; Weiss, R.H.; Hammock, B.D. Biological evaluation of a novel sorafenib analogue, t-CUPM. Cancer Chemother. Pharmacol., 2015, 75, 161-171.
[70]
Fucile, C.; Marenco, S.; Bazzica, M.; Zuccoli, M.L.; Lantieri, F.; Robbiano, L.; Marini, V.; Di-Gion, P.; Pieri, G.; Stura, P. Martelli. A Measurement of sorafenib plasma concentration by high-performance liquid chromatography in patients with advanced hepatocellular carcinoma: Is it useful the application in clinical practice? A pilot study. Med. Oncol., 2015, 32, 335.
[71]
Schimmer, A.D. Inhibitor of apoptosis proteins: translating basic knowledge into clinical practice. Cancer Res., 2004, 64, 7183-7190.
[72]
Deveraux, Q.L.; Roy, N.; Stennicke, H.R.; Van-Arsdale, T.; Zhou, Q.; Srinivasula, S.M.; Alnemri, E.S.; Salvesen, G.S.; Reed, J.C. IAPs block apoptotic events induced by caspase‐8 and cytochrome c by direct inhibition of distinct caspases. EMBO J., 1998, 17, 2215-2223.
[73]
Oost, T.K.; Sun, C.; Armstrong, R.C.; Al-Assaad, A.S.; Betz, S.F.; Deckwerth, T.L.; Ding, H.; Elmore, S.W.; Meadows, R.P.; Olejniczak, E.T. Oleksijew. A Discovery of potent antagonists of the antiapoptotic protein XIAP for the treatment of cancer. J. Med. Chem., 2004, 47, 4417-4426.
[74]
Stein, A.; Aloy, P. Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures. PLOS Comput. Biol., 2010, 6(5)e1000789
[75]
Verdine, G.L.; Walensky, L.D. The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin. Cancer Res., 2007, 13, 7264-7270.
[76]
Parrondo, R.; De-las-Pozas, A.; Reiner, T.; Perez-Stable, C. ABT-737, a small molecule Bcl-2/Bcl-xL antagonist, increases anti-mitotic-mediated apoptosis in human prostate cancer cells. Peer J., 2013, 1e144
[77]
Rayburn, E.; Zhang, R.; He, J.; Wang, H. MDM2 and human malignancies: expression, clinical pathology, prognostic markers, and implications for chemotherapy. Curr. Cancer Drug Targets, 2005, 5, 27-41.
[78]
Zhao, Y.; Yu, S.; Sun, W.; Liu, L.; Lu, J.; McEachern, D.; Shargary, S.; Bernard, D.; Li, X.; Zhao, T.; Zou, P.; Sun, D.; Wang, S. A potent small-molecule inhibitor of the MDM2-p53 interaction (MI-888) achieved complete and durable tumor regression in mice. J. Med. Chem., 2013, 56, 5553-5561.
[79]
Werner, L.; Huang, S.; Armstrong, A.; Francis, D.; Osgood, T.; Canon, J.; Harari, P.M. Abstract 2610: AMG 232, a small molecular inhibitor of MDM2 augments radiation response in human tumors harboring wild-type p53. Cancer Res., 2014, 74, 2610-2610.
[80]
Bernard, D.; Zhao, Y.; Wang, S. AM-8553: A novel MDM2 inhibitor with a promising outlook for potential clinical development. J. Med. Chem., 2012, 55, 4934-4935.
[81]
Galatin, P.S.; Abraham, D.J. A nonpeptidic sulfonamide inhibits the p53 - mdm2 interaction and activates p53-dependent transcription in mdm2 over expressing cells. J. Med. Chem., 2004, 47, 4163-4165.
[82]
Khoo, K.H.; Hoe, K.K.; Verma, C.S.; Lane, D.P. Drugging the p53 pathway: Understanding the route to clinical efficacy. Nat. Rev. Drug Discov., 2014, 13, 217-236.
[83]
Youle, R.J.; Strasser, A. The BCL-2 protein family: opposing activities that mediate cell death. Nat. Rev. Mol. Cell Biol., 2008, 9, 47-59.
[84]
Cory, S.; Adams, J.M. The Bcl2 family: Regulators of the cellular life-or-death switch. Nat. Rev. Cancer, 2002, 2, 647-656.
[85]
Sattler, M. Liang. H.; Nettesheim, D.; Meadows, R.P.; Harlan, J.E.; Eberstadt, M.; Yoon, H.S.; Shuker, S.B.; Chang, B.S.; Minn, A.J.; Thompson, C.B. Structure of Bcl-x L-Bak peptide complex: recognition between regulators of apoptosis. Science, 1997, 275, 983-986.
[86]
Corbi-Verge, C.; Kim, P.M. Motif mediated protein-protein interactions as drug targets. Cell Commun. Signal., 2016, 14, 8.
[87]
Parrondo, R.; De-las-Pozas, A.; Reiner, T.; Perez-Stable, C. ABT-737, a small molecule Bcl-2/Bcl-xL antagonist, increases antimitotic-mediated apoptosis in human prostate cancer cells. Peer J, 2013, 1e144
[88]
Leisle, L.; Valiyaveetil, F.; Mehl, R.A.; Ahern, C.A. Incorporation of non-canonical amino acids. Adv. Exp. Med. Biol., 2015, 869, 119-151.
[89]
Hamase, K. Recent advances on D-amino acid research. J. Pharmaceut. Biomed. Anal., 2015, 116, 1.
[90]
Spokoyny, A.M.; Zou, Y.; Ling, J.J.; Yu, H. Lin.; Y.S.; Pentelute, B.L. A perfluoroaryl-cysteine SNAr chemistry approach to unprotected peptide stapling. J. Am. Chem. Soc., 2013, 135, 5946-5949.
[91]
Schafmeister, C.E.; Po, J.; Verdine, G.L. An all-hydrocarbon cross-linking system for enhancing the helicity and metabolic stability of peptides. J. Am. Chem. Soc., 2000, 122, 5891-5892.
[92]
Zhang, C.; Dai, P.; Spokoyny, A.M.; Pentelute, B.L. Enzyme-catalyzed macrocyclization of long unprotected peptides. Org. Lett., 2014, 16, 3652-3655.
[93]
Shi, Y.; Wu, G.; Chai, J.; Suber, T.L.; Wu, J.W.; Du, C.; Wang, X. Structural basis for binding of Smac/DIABLO to the XIAP BIR3 domain. Nature, 2000, 408, 1008-1012.
[94]
Nikolovska-Coleska, Z.; Meagher, J.L.; Jiang, S.; Yang, C.Y.; Qiu, S.; Roller, P.P.; Stuckey, J.A.; Wang, S. Interaction of a cyclic, bivalent Smac mimetic with the x-linked inhibitor of apoptosis protein. Biochemistry, 2008, 16, 9811-9824.
[95]
Flygare, J.A.; Beresini, M.; Budha, N.; Chan, H.; Chan, I.T.; Cheeti, S.; Cohen, F.; Deshayes, K.; Doerner, K.; Eckhardt, S.G.; Elliott, L.O. Discovery of a potent small-molecule antagonist of inhibitor of apoptosis (IAP) proteins and clinical candidate for the treatment of cancer (GDC-0152). J. Med. Chem., 2012, 55, 4101-4113.
[96]
Wang, S.; Bai, L.; Lu, J.; Liu, L.; Yang, C.Y. Targeting inhibitors of apoptosis proteins (IAPs) for new breast cancer therapeutics. J. Mammary Gland Biol. Neoplasia, 2012, 17, 217-228.
[97]
Blackwell, H.E.; Grubbs, R.H. Highly efficient synthesis of covalently cross‐linked peptide helices by ring‐closing metathesis. Angew. Chem. Int. Ed., 1998, 37, 3281-3284.
[98]
Walensky, L.D.; Bird, G.H. Hydrocarbon-stapled peptides: Principles, practice, and progress. J. Med. Chem., 2014, 57, 6275-6288.
[99]
Chang, Y.S.; Graves, B.; Guerlavais, V.; Tovar, C.; Packman, K.; To, K.H.; Olson, K.A.; Kesavan, K.; Gangurde, P.; Mukherjee, A.; Baker, T. Stapled α-helical peptide drug development: A potent dual inhibitor of MDM2 and MDMX for p53-dependent cancer therapy. Proc. Natl. Acad. Sci. , 2013, 110, 3445-E3454.
[100]
Zinzalla, G.; Thurston, D.E. Targeting protein-protein interactions for therapeutic intervention: a challenge for the future. Future Med. Chem., 2009, 1, 65-93.
[101]
Dandekar, T.; Snel, B.; Huynen, M.; Bork, P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci., 1998, 23, 324-328.
[102]
Overbeek, R.; Fonstein, M.; D’Souza, M.; Pusch, G.D.; Maltsev, N. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. , 1999, 96, 2896-2901.
[103]
Enright, A.J.; Iliopoulos, I.; Kyrpides, N.C.; Ouzounis, C.A. Protein interaction maps for complete genomes based on gene fusion events. Nature, 1999, 402, 86-90.
[104]
Marcotte, E.M.; Pellegrini, M.; Ng, H.L.; Rice, D.W. Yeates, Eisenberg D. Detecting protein function and protein-protein interactions from genome sequences. Science, 1999, 285, 751-753.
[105]
Ouzounis, C.; Kyrpides, N. The emergence of major cellular processes in evolution. FEBS Lett., 1996, 390, 119-123.
[106]
Pellegrini, M.; Marcotte, E.M.; Thompson, M.J.; Eisenberg, D.; Yeates, T.O. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. , 1999, 96, 4285-4288.
[107]
Najafabadi, H.S.; Salavati, R. Sequence-based prediction of protein-protein interactions by means of codon usage. Genome Biol., 2008, 9, R87.
[108]
Bock, J.R.; Gough, D.A. Whole-proteome interaction mining. Bioinformatics, 2003, 19, 125-134.
[109]
Aziz, M.M.; Maleki, M.; Rueda, L.; Raza, M.; Banerjee, S. Prediction of biological protein-protein interactions using atom-type and amino acid properties. Proteomics, 2011, 11, 3802-3810.
[110]
Pitre, S.; Dehne, F.; Chan, A.; Cheetham, J.; Duong, A.; Emili, A.; Gebbia, M.; Greenblatt, J.; Jessulat, M.; Krogan, N.; Luo, X. Golshani. A PIPE: A protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. BMC Bioinformatics, 2006, 7, 365.
[111]
Guo, Y.; Yu, L.; Wen, Z.; Li, M. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res., 2008, 36, 3025-3030.
[112]
Rajasekaran, S.; Balla, S.; Gradie, P.; Gryk, M.R.; Kadaveru, K.; Kundeti, V.; Maciejewski, M.W.; Mi, T.; Rubino, N.; Vyas, J.; Schiller, M.R. Minimotif miner 2nd release: A database and web system for motif search. Nucleic Acids Res., 2009, 37, D185-D190.
[113]
Knisley, T.J.; Ariyasena, T.C.; Sajavaara, T.; Saly, M.J.; Winter, C.H. Low temperature growth of high purity, low resistivity copper films by atomic layer deposition. Chem. Mater., 2011, 23, 4417-4419.
[114]
Harris, B.Z.; Lim, W.A. Mechanisms and role of PDZ domains in signalling complex assembly. J. Cell Sci., 2001, 114, 3219-3231.
[115]
Hue, M.; Riffle, M.; Vert, J.P.; Noble, W.S. Large-scale prediction of protein-protein interactions from structures. BMC Bioinformatics, 2010, 11, 144.
[116]
Shoemaker, B.A.; Zhang, D.; Tyagi, M.; Thangudu, R.R.; Fong, J.H.; Marchler-Bauer, A.; Bryant, S.H.; Madej, T.; Panchenko, A.R. IBIS (Inferred Biomolecular Interaction Server) reports, predicts and integrates multiple types of conserved interactions for proteins. Nucleic Acids Res., 2011, 40, D834-D840.
[117]
Krissinel, E.; Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol., 2007, 372, 774-797.
[118]
Gibrat, J.F.; Madej, T.; Bryant, S.H. Surprising similarities in structure comparison. Curr. Opin. Struct. Biol., 1996, 6, 377-385.
[119]
Lu, L.; Lu, H.; Skolnick, J. Multiprospector: An algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins, 2002, 49, 350-364.
[120]
Rodriguez-Soca, Y.; Munteanu, C.R.; Dorado, J.; Rabunal, J.; Pazos, A.; González-Díaz, H. Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein-protein interactions. Polymer , 2010, 51, 264-273.
[121]
Deng, M.; Mehta, S.; Sun, F.; Chen, T. Inferring domain-domain interactions from protein-protein interactions. Genome Res., 2002, 12, 1540-1548.
[122]
Punta, M.; Coggill, P.C.; Eberhardt, R.Y.; Mistry, J.; Tate, J.; Boursnell, C.; Pang, N.; Forslund, K.; Ceric, G.; Clements, J.; Heger, A.; Holm, L.; Sonnhammer, E.L.L.; Eddy, S.R.; Bateman, A.; Finn, R.D. The Pfam protein families database. Nucleic Acid Res., 2012, 40, D290-D301.
[123]
Pagel, P.; Kovac, S.; Oesterheld, M.; Brauner, B.; Dunger-Kaltenbach, I.; Frishman, G.; Montrone, C.; Mark, P.; Stumpflen, V.; Mewes, H.W.; Ruepp, A.; Frishman, D. The MIPS mammalian protein-protein interaction database. Bioinformatics, 2005, 21, 832-834.
[124]
Huang, C.; Morcos, F.; Kanaan, S.P.; Wuchty, S.; Chen, D.Z.; Izaguirre, J.A. Predicting protein-protein interactions from protein domains using a set cover approach. TCBB, 2007, 4, 78-87.
[125]
Chen, X.W.; Liu, M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics, 2005, 21, 4394-4400.
[126]
Wang, R.S.; Wang, Y.; Wu, L.Y.; Zhang, X.S.; Chen, L. Analysis on multi-domain cooperation for predicting protein-protein interactions. BMC Bioinformatics, 2007, 8, 391.
[127]
Conte, L.L.; Chothia, C.; Janin, J. The atomic structure of protein-protein recognition sites. J. Mol. Biol., 1999, 285, 2177-2198.
[128]
Rudolph, J. Inhibiting transient protein protein interactions: lessons from the Cdc25 protein tyrosine phosphatases. Nat. Rev. Cancer, 2007, 7, 202-211.
[129]
DeLano, W.L. Unraveling hot spots in binding interfaces: Progress and challenges. Curr. Opin. Struct. Biol., 2002, 12, 14-20.
[130]
Sidhu, S.S.; Fairbrother, W.J.; Deshayes, K. Exploring protein-protein interactions with phage display. ChemBioChem, 2003, 4, 14-25.
[131]
Bogan, A.A.; Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol., 1998, 280, 1-9.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy