In silico identification of small molecule modulators for disruption of Hsp90–Cdc37 protein–protein interaction interface for cancer therapeutic application

Abstract The protein–protein interactions (PPIs) in the biological systems are important to maintain a number of cellular processes. Several disorders including cancer may be developed due to dysfunction in the assembly of PPI networks. Hence, targeting intracellular PPIs can be considered as a crucial drug target for cancer therapy. Among the enormous and diverse group of cancer-enabling PPIs, the Hsp90–Cdc37 is prominent for cancer therapeutic development. The successful inhibition of Hsp90–Cdc37 PPI interface can be an important therapeutic option for cancer management. In the current study, a set of more than sixty thousand compounds belong to four databases were screened through a multi-steps molecular docking study in Glide against the Hsp90–Cdc37 interaction interface. The Glide-score and Prime-MM-GBSA based binding free energy of DCZ3112, standard Hsp90–Cdc37 inhibitor were found to be −6.96 and −40.46 kcal/mol, respectively. The above two parameters were used as cut-off score to reduce the chemical space from all successfully docked molecules. Furthermore, the in-silico pharmacokinetics parameters, common-feature pharmacophore analyses and the molecular binding interactions were used to wipe out the inactive molecules. Finally, four molecules were found to be important to modulate the Hsp90–Cdc37 interface. The potentiality of the final four molecules was checked through several drug-likeness characteristics. The molecular dynamics (MD) simulation study explained that all four molecules retained inside the interface of Hsp90–Cdc37. The binding free energy of each molecule obtained from the MD simulation trajectory was clearly explained the strong affection towards the Hsp90–Cdc37. Hence, the proposed molecule might be crucial for successful inhibition of the Hsp90–Cdc37 interface. Communicated by Ramaswamy H. Sarma

[1]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings , 1997 .

[2]  A. Ghose,et al.  A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. , 1999, Journal of combinatorial chemistry.

[3]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[4]  J J Baldwin,et al.  Prediction of drug absorption using multivariate statistics. , 2000, Journal of medicinal chemistry.

[5]  I. Muegge,et al.  Simple selection criteria for drug-like chemical matter. , 2001, Journal of medicinal chemistry.

[6]  Stephen R. Johnson,et al.  Molecular properties that influence the oral bioavailability of drug candidates. , 2002, Journal of medicinal chemistry.

[7]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[8]  L. Pearl,et al.  The Mechanism of Hsp90 Regulation by the Protein Kinase-Specific Cochaperone p50cdc37 , 2004, Cell.

[9]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[10]  P. Chène,et al.  Drugs Targeting Protein–Protein Interactions , 2006, ChemMedChem.

[11]  J. Gerrard,et al.  Inhibiting protein-protein interactions as an emerging paradigm for drug discovery. , 2007, Mini reviews in medicinal chemistry.

[12]  Jeremy R. Greenwood,et al.  Epik: a software program for pKa prediction and protonation state generation for drug-like molecules , 2007, J. Comput. Aided Mol. Des..

[13]  Burkhard Rost,et al.  Protein–Protein Interaction Hotspots Carved into Sequences , 2007, PLoS Comput. Biol..

[14]  Tao Zhang,et al.  A novel Hsp90 inhibitor to disrupt Hsp90/Cdc37 complex against pancreatic cancer cells , 2008, Molecular Cancer Therapeutics.

[15]  Jan H. Jensen,et al.  Very fast prediction and rationalization of pKa values for protein–ligand complexes , 2008, Proteins.

[16]  Julie C. Mitchell,et al.  KFC Server: interactive forecasting of protein interaction hot spots , 2008, Nucleic Acids Res..

[17]  Solène Grosdidier,et al.  Identification of hot-spot residues in protein-protein interactions by computational docking , 2008, BMC Bioinformatics.

[18]  Giovanna Zinzalla,et al.  Targeting protein-protein interactions for therapeutic intervention: a challenge for the future. , 2009, Future medicinal chemistry.

[19]  Tao Zhang,et al.  Characterization of Celastrol to Inhibit Hsp90 and Cdc37 Interaction* , 2009, The Journal of Biological Chemistry.

[20]  Harald Schwalbe,et al.  The Human Cdc37·Hsp90 Complex Studied by Heteronuclear NMR Spectroscopy* , 2009, Journal of Biological Chemistry.

[21]  Tao Zhang,et al.  New developments in Hsp90 inhibitors as anti-cancer therapeutics: mechanisms, clinical perspective and more potential. , 2009, Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy.

[22]  Ozlem Keskin,et al.  HotPoint: hot spot prediction server for protein interfaces , 2010, Nucleic Acids Res..

[23]  Tao Zhang,et al.  Withaferin A targets heat shock protein 90 in pancreatic cancer cells. , 2010, Biochemical pharmacology.

[24]  Roman A. Laskowski,et al.  LigPlot+: Multiple Ligand-Protein Interaction Diagrams for Drug Discovery , 2011, J. Chem. Inf. Model..

[25]  Abhinav Grover,et al.  Hsp90/Cdc37 Chaperone/co-chaperone complex, a novel junction anticancer target elucidated by the mode of action of herbal drug Withaferin A , 2011, BMC Bioinformatics.

[26]  Kate S. Carroll,et al.  Sulforaphane inhibits pancreatic cancer through disrupting Hsp90-p50(Cdc37) complex and direct interactions with amino acids residues of Hsp90. , 2012, The Journal of nutritional biochemistry.

[27]  F. Khuri,et al.  Targeting protein-protein interactions as an anticancer strategy. , 2013, Trends in pharmacological sciences.

[28]  Mancang Gu,et al.  Structure-activity relationship (SAR) of withanolides to inhibit Hsp90 for its activity in pancreatic cancer cells , 2014, Investigational New Drugs.

[29]  David S. Wishart,et al.  DrugBank 4.0: shedding new light on drug metabolism , 2013, Nucleic Acids Res..

[30]  Min Zhang,et al.  FW-04-806 inhibits proliferation and induces apoptosis in human breast cancer cells by binding to N-terminus of Hsp90 and disrupting Hsp90-Cdc37 complex formation , 2014, Molecular Cancer.

[31]  Jolanta Grembecka,et al.  Targeting protein–protein interactions in hematologic malignancies: still a challenge or a great opportunity for future therapies? , 2015, Immunological reviews.

[32]  A. Mapp,et al.  Direct and Propagated Effects of Small Molecules on Protein–Protein Interaction Networks , 2015, Front. Bioeng. Biotechnol..

[33]  Michael Schroeder,et al.  PLIP: fully automated protein–ligand interaction profiler , 2015, Nucleic Acids Res..

[34]  PCOS in 2015: New insights into the genetics of polycystic ovary syndrome , 2016, Nature Reviews Endocrinology.

[35]  D. Scott,et al.  Small molecules, big targets: drug discovery faces the protein–protein interaction challenge , 2016, Nature Reviews Drug Discovery.

[36]  Erich E. Wanker,et al.  Current Approaches Toward Quantitative Mapping of the Interactome , 2016, Front. Genet..

[37]  C. Behre,et al.  Epigenetic and Transcriptional Alterations in Human Adipose Tissue of Polycystic Ovary Syndrome , 2016, Scientific Reports.

[38]  J. Silke,et al.  The importance of being chaperoned: HSP90 and necroptosis. , 2016, Cell chemical biology.

[39]  Jin-jian Lu,et al.  Platycodin D potentiates proliferation inhibition and apoptosis induction upon AKT inhibition via feedback blockade in non-small cell lung cancer cells , 2016, Scientific Reports.

[40]  C. Li,et al.  Natural Product Kongensin A is a Non-Canonical HSP90 Inhibitor that Blocks RIP3-dependent Necroptosis. , 2016, Cell chemical biology.

[41]  R. Azziz PCOS in 2015: New insights into the genetics of polycystic ovary syndrome , 2016, Nature Reviews Endocrinology.

[42]  O. Keskin,et al.  Predicting Protein-Protein Interactions from the Molecular to the Proteome Level. , 2016, Chemical reviews.

[43]  Jennifer L. Knight,et al.  OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins. , 2016, Journal of chemical theory and computation.

[44]  Jin-jian Lu,et al.  Novel Hsp90 inhibitor platycodin D disrupts Hsp90/Cdc37 complex and enhances the anticancer effect of mTOR inhibitor , 2017, Toxicology and applied pharmacology.

[45]  Daisuke Kihara,et al.  In silico structure-based approaches to discover protein-protein interaction-targeting drugs. , 2017, Methods.

[46]  Olivier Michielin,et al.  SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules , 2017, Scientific Reports.

[47]  Weiliang Zhu,et al.  DCZ3112, a novel Hsp90 inhibitor, exerts potent antitumor activity against HER2-positive breast cancer through disruption of Hsp90-Cdc37 interaction. , 2018, Cancer letters.

[48]  Lei Deng,et al.  Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment , 2018, Molecules.

[49]  Yi Xiong,et al.  Protein-protein interface hot spots prediction based on a hybrid feature selection strategy , 2018, BMC Bioinformatics.

[50]  Stephani Joy Y Macalino,et al.  Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery , 2018, Molecules.

[51]  J. Fernández-Recio,et al.  Hot-spot analysis for drug discovery targeting protein-protein interactions , 2018, Expert opinion on drug discovery.

[52]  J. Reynisson,et al.  Identification of Isoform-Selective Ligands for the Middle Domain of Heat Shock Protein 90 (Hsp90) , 2019, International journal of molecular sciences.

[53]  L. Mabonga,et al.  Protein-protein interaction modulators: advances, successes and remaining challenges , 2019, Biophysical Reviews.

[54]  S. Dutta Gupta,et al.  Inhibiting protein-protein interactions of Hsp90 as a novel approach for targeting cancer. , 2019, European journal of medicinal chemistry.

[55]  Jian Zhang,et al.  Small-molecule inhibitor targeting the Hsp90-Cdc37 protein-protein interaction in colorectal cancer , 2019, Science Advances.

[56]  Rong Tang,et al.  Kisspeptin and Polycystic Ovary Syndrome , 2019, Front. Endocrinol..

[57]  A. Saha,et al.  Insight into the screening of potential beta-lactamase inhibitors as anti-bacterial chemical agents through pharmacoinformatics study , 2020, Journal of biomolecular structure & dynamics.