Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

[1]  Joo Chuan Tong,et al.  Recent advances in computer-aided drug design , 2009, Briefings Bioinform..

[2]  Yang Zhang,et al.  Deep‐learning contact‐map guided protein structure prediction in CASP13 , 2019, Proteins.

[3]  K. Shokat,et al.  Drugging the 'undruggable' cancer targets , 2017, Nature Reviews Cancer.

[4]  Brian K. Shoichet,et al.  Molecular Docking and High-Throughput Screening for Novel Inhibitors of Protein Tyrosine Phosphatase-1 B , 2022 .

[5]  Zaheer Ul-Haq,et al.  3D Structure Prediction of Human β1-Adrenergic Receptor via Threading-Based Homology Modeling for Implications in Structure-Based Drug Designing , 2015, PloS one.

[6]  M J Sternberg,et al.  Supersites within superfolds. Binding site similarity in the absence of homology. , 1998, Journal of molecular biology.

[7]  Yang Zhang,et al.  BSP‐SLIM: A blind low‐resolution ligand‐protein docking approach using predicted protein structures , 2012, Proteins.

[8]  J. Fernandez-Banet,et al.  The KRASG12C Inhibitor, MRTX849, Provides Insight Toward Therapeutic Susceptibility of KRAS Mutant Cancers in Mouse Models and Patients. , 2019, Cancer discovery.

[9]  Wenkang Huang,et al.  A novel allosteric site in casein kinase 2α discovered using combining bioinformatics and biochemistry methods , 2017, Acta Pharmacologica Sinica.

[10]  Julio C Facelli,et al.  Effects of the enlargement of polyglutamine segments on the structure and folding of ataxin-2 and ataxin-3 proteins , 2017, Journal of biomolecular structure & dynamics.

[11]  Maruti J. Dhanavade,et al.  Homology modeling, molecular docking and MD simulation studies to investigate role of cysteine protease from Xanthomonas campestris in degradation of Aβ peptide , 2013, Comput. Biol. Medicine.

[12]  Haruki Nakamura,et al.  Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive. , 2017, Methods in molecular biology.

[13]  Frank McCormick,et al.  KRAS as a Therapeutic Target , 2015, Clinical Cancer Research.

[14]  Yu Luo,et al.  Allosite: a method for predicting allosteric sites , 2013, Bioinform..

[15]  Shaoyong Lu,et al.  Discovery of hidden allosteric sites as novel targets for allosteric drug design. , 2017, Drug discovery today.

[16]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[17]  Yi-Ping Phoebe Chen,et al.  Structure-based drug design to augment hit discovery. , 2011, Drug discovery today.

[18]  Albert C. Pan,et al.  Pathway and mechanism of drug binding to G-protein-coupled receptors , 2011, Proceedings of the National Academy of Sciences.

[19]  S. Fesik,et al.  Drugging the undruggable RAS: Mission Possible? , 2014, Nature Reviews Drug Discovery.

[20]  Simon Mitternacht,et al.  SPACER: server for predicting allosteric communication and effects of regulation , 2013, Nucleic Acids Res..

[21]  Atul J. Butte,et al.  Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health , 2018, Pharmacological Reviews.

[22]  Burkhard Rost,et al.  The PredictProtein server , 2003, Nucleic Acids Res..

[23]  J. Skolnick,et al.  A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation , 2008, Proceedings of the National Academy of Sciences.

[24]  Khozirah Shaari,et al.  Andrographolide derivatives inhibit guanine nucleotide exchange and abrogate oncogenic Ras function , 2013, Proceedings of the National Academy of Sciences.

[25]  Alejandro Panjkovich,et al.  PARS: a web server for the prediction of Protein Allosteric and Regulatory Sites , 2014, Bioinform..

[26]  Dusanka Janezic,et al.  ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment , 2010, Bioinform..

[27]  Hongyi Zhou,et al.  FINDSITEcomb: A Threading/Structure-Based, Proteomic-Scale Virtual Ligand Screening Approach , 2013, J. Chem. Inf. Model..

[28]  B. Shoichet,et al.  Molecular docking and high-throughput screening for novel inhibitors of protein tyrosine phosphatase-1B. , 2002, Journal of medicinal chemistry.

[29]  Theodora Katsila,et al.  Computational approaches in target identification and drug discovery , 2016, Computational and structural biotechnology journal.

[30]  Xiaoqin Zou,et al.  Docking-based inverse virtual screening: methods, applications, and challenges , 2018, Biophysics reports.

[31]  L. Lai,et al.  Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery. , 2012, Journal of chemical theory and computation.

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

[33]  David S. Goodsell,et al.  The RCSB protein data bank: integrative view of protein, gene and 3D structural information , 2016, Nucleic Acids Res..

[34]  Yang Zhang,et al.  I-TASSER server: new development for protein structure and function predictions , 2015, Nucleic Acids Res..

[35]  Chao-yie Yang,et al.  Identification of Potential Small Molecule Allosteric Modulator Sites on IL-1R1 Ectodomain Using Accelerated Conformational Sampling Method , 2015, PloS one.

[36]  M. Duffy,et al.  Mutant p53 as a target for cancer treatment. , 2017, European journal of cancer.

[37]  Kathryn M Hart,et al.  Discovery of multiple hidden allosteric sites by combining Markov state models and experiments , 2015, Proceedings of the National Academy of Sciences.

[38]  Mohammed AlQuraishi,et al.  AlphaFold at CASP13 , 2019, Bioinform..

[39]  Hiroyuki Ogata,et al.  Metagrowth: a new resource for the building of metabolic hypotheses in microbiology , 2004, Nucleic Acids Res..

[40]  A. Rehemtulla,et al.  Inducing Oncoprotein Degradation to Improve Targeted Cancer Therapy1 , 2015, Neoplasia.

[41]  Edward W. Lowe,et al.  Computational Methods in Drug Discovery , 2014, Pharmacological Reviews.

[42]  R. Hamid,et al.  Modern Computational Strategies for Designing Drugs to Curb Human Diseases: A Prospect. , 2019, Current topics in medicinal chemistry.