Structure based design of selective SHP2 inhibitors by De novo design, synthesis and biological evaluation

SHP2 phosphatase, encoded by the PTPN11 gene, is a non-receptor PTP, which plays an important role in growth factor, cytokine, integrin, hormone signaling pathways, and regulates cellular responses, such as proliferation, differentiation, adhesion migration and apoptosis. Many studies have reported that upregulation of SHP2 expression is closely related to human cancer, such as breast cancer, liver cancer and gastric cancer. Hence, SHP2 has become a promising target for cancer immunotherapy. In this paper, we reported the identification of compound 1 as SHP2 inhibitor. Fragment-based ligand design, De novo design, ADMET and Molecular docking were performed to explore potential selective SHP2 allosteric inhibitors based on SHP836. The results of docking studies indicated that the selected compounds had higher selective SHP2 inhibition than existing inhibitors. Compound 1 was found to have a novel selectivity against SHP2 with an in vitro enzyme activity IC50 value of 9.97 μM. Fluorescence titration experiment confirmed that compound 1 directly bound to SHP2. Furthermore, the results of binding free energies demonstrated that electrostatic energy was the primary factor in elucidating the mechanism of SHP2 inhibition. Dynamic cross correlation studies also supported the results of docking and molecular dynamics simulation. This series of analyses provided important structural features for designing new selective SHP2 inhibitors as potential drugs and promising candidates for pre-clinical pharmacological investigations.

[1]  Jun O. Liu,et al.  Exploring the Existing Drug Space for Novel pTyr Mimetic and SHP2 Inhibitors. , 2015, ACS medicinal chemistry letters.

[2]  H Verli,et al.  GROMOS96 43a1 performance in predicting oligosaccharide conformational ensembles within glycoproteins. , 2010, Carbohydrate research.

[3]  Jörg Weiser,et al.  Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO) , 1999, J. Comput. Chem..

[4]  W. Guida,et al.  Inhibitors of Src homology-2 domain containing protein tyrosine phosphatase-2 (Shp2) based on oxindole scaffolds. , 2008, Journal of medicinal chemistry.

[5]  Andreas Prlic,et al.  Integrating genomic information with protein sequence and 3D atomic level structure at the RCSB protein data bank , 2016, Bioinform..

[6]  Lisa Yan,et al.  Fully Automated Molecular Mechanics Based Induced Fit Protein-Ligand Docking Method , 2008, J. Chem. Inf. Model..

[7]  M. Murcko,et al.  Guiding molecules towards drug-likeness. , 2002, Current opinion in drug discovery & development.

[8]  Robin Taylor,et al.  Comparing protein–ligand docking programs is difficult , 2005, Proteins.

[9]  Siyu Zhu,et al.  Discovery of a Novel Inhibitor of the Protein Tyrosine Phosphatase Shp2 , 2015, Scientific Reports.

[10]  John D. Minna,et al.  Activating Mutations of the Noonan Syndrome-Associated SHP2/PTPN11 Gene in Human Solid Tumors and Adult Acute Myelogenous Leukemia , 2004, Cancer Research.

[11]  Tingjun Hou,et al.  Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking , 2011, J. Comput. Chem..

[12]  R. Chan,et al.  Salicylic acid based small molecule inhibitor for the oncogenic Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2). , 2010, Journal of medicinal chemistry.

[13]  Collin M. Stultz,et al.  The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design , 2009, J. Comput. Aided Mol. Des..

[14]  Wei Wang,et al.  Crystal structure of human protein tyrosine phosphatase SHP‐1 in the open conformation , 2011, Journal of cellular biochemistry.

[15]  Xiaoyang Xia,et al.  Classification of kinase inhibitors using a Bayesian model. , 2004, Journal of medicinal chemistry.

[16]  Lisa Yan,et al.  The dominant role of side‐chain backbone interactions in structural realization of amino acid code. ChiRotor: A side‐chain prediction algorithm based on side‐chain backbone interactions , 2007, Protein science : a publication of the Protein Society.

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

[18]  Rajendra Kumar,et al.  g_mmpbsa - A GROMACS Tool for High-Throughput MM-PBSA Calculations , 2014, J. Chem. Inf. Model..

[19]  Walters Wp,et al.  Guiding molecules towards drug-likeness. , 2002 .

[20]  S. Shoelson,et al.  Crystal Structure of the Tyrosine Phosphatase SHP-2 , 1998, Cell.

[21]  W. Guida,et al.  Discovery of a Novel Shp2 Protein Tyrosine Phosphatase Inhibitor , 2006, Molecular Pharmacology.

[22]  D. Moore,et al.  Corrigendum: Ptpn11 deletion in a novel progenitor causes metachondromatosis by inducing hedgehog signalling , 2014, Nature.

[23]  Gisbert Schneider,et al.  Computer-based de novo design of drug-like molecules , 2005, Nature Reviews Drug Discovery.

[24]  Charles L. Brooks,et al.  Detailed analysis of grid‐based molecular docking: A case study of CDOCKER—A CHARMm‐based MD docking algorithm , 2003, J. Comput. Chem..

[25]  Ping Zhu,et al.  Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases , 2016, Nature.

[26]  Xiaobo Li,et al.  SAHA-based novel HDAC inhibitor design by core hopping method. , 2014, Journal of molecular graphics & modelling.

[27]  Y. Sanejouand,et al.  Conformational change of proteins arising from normal mode calculations. , 2001, Protein engineering.

[28]  Duqiang Luo,et al.  Identification of demethylincisterol A3 as a selective inhibitor of protein tyrosine phosphatase Shp2 , 2017, European journal of pharmacology.

[29]  G. Feng,et al.  PTPN11 is the first identified proto-oncogene that encodes a tyrosine phosphatase. , 2007, Blood.

[30]  D. Case,et al.  Exploring protein native states and large‐scale conformational changes with a modified generalized born model , 2004, Proteins.

[31]  A S Ivanov,et al.  Strategy of computer-aided drug design. , 2003, Current drug targets. Infectious disorders.

[32]  D. Eisenberg,et al.  Atomic solvation parameters applied to molecular dynamics of proteins in solution , 1992, Protein science : a publication of the Protein Society.

[33]  Hans-Joachim Böhm,et al.  The computer program LUDI: A new method for the de novo design of enzyme inhibitors , 1992, J. Comput. Aided Mol. Des..

[34]  Jahan B. Ghasemi,et al.  Computer‐aided drug design to explore cyclodextrin therapeutics and biomedical applications , 2017, Chemical biology & drug design.

[35]  Kenneth M Merz,et al.  Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. , 2003, Journal of medicinal chemistry.

[36]  Bowen Tang,et al.  The molecular mechanism of hPPARα activation , 2017 .

[37]  Haruki Nakamura,et al.  A Novel Approach of Dynamic Cross Correlation Analysis on Molecular Dynamics Simulations and Its Application to Ets1 Dimer–DNA Complex , 2014, PloS one.

[38]  Wei-Ren Xu,et al.  The Discovery of a Novel and Selective Inhibitor of PTP1B Over TCPTP: 3D QSAR Pharmacophore Modeling, Virtual Screening, Synthesis, and Biological Evaluation , 2014, Chemical biology & drug design.

[39]  A. Barr.  Protein tyrosine phosphatases as drug targets: strategies and challenges of inhibitor development. , 2010, Future medicinal chemistry.

[40]  Sarah L. Williams,et al.  Allosteric Inhibition of SHP2: Identification of a Potent, Selective, and Orally Efficacious Phosphatase Inhibitor. , 2016, Journal of medicinal chemistry.

[41]  Berk Hess,et al.  P-LINCS:  A Parallel Linear Constraint Solver for Molecular Simulation. , 2008, Journal of chemical theory and computation.