1
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Backenköhler M, Groß J, Wolf V, Volkamer A. Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases. J Chem Inf Model 2024; 64:4009-4020. [PMID: 38751014 DOI: 10.1021/acs.jcim.4c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2024]
Abstract
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.
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Affiliation(s)
- Michael Backenköhler
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany
| | - Joschka Groß
- Modeling and Simulation, Saarland University, Saarbrücken 66123, Germany
| | - Verena Wolf
- Modeling and Simulation, Saarland University, Saarbrücken 66123, Germany
| | - Andrea Volkamer
- Data Driven Drug Design, Center for Bioinformatics, Saarland University, Saarbrücken 66123, Germany
- Structural Bioinformatics and in Silico Toxicology Institute of Physiology, Universitätsmedizin Berlin, Berlin 10117, Germany
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2
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Xu X, Han W, Ning X, Zang C, Xu C, Zeng C, Pu C, Zhang Y, Chen Y, Liu H. Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions. J Chem Inf Model 2024; 64:1543-1559. [PMID: 38381562 DOI: 10.1021/acs.jcim.3c01689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.
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Affiliation(s)
- Xiaohe Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiangzhen Ning
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengdong Zang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengcheng Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chen Zeng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengtao Pu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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3
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Yoon HR, Park GJ, Balupuri A, Kang NS. TWN-FS method: A novel fragment screening method for drug discovery. Comput Struct Biotechnol J 2023; 21:4683-4696. [PMID: 37841326 PMCID: PMC10568351 DOI: 10.1016/j.csbj.2023.09.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/17/2023] Open
Abstract
Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.
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Affiliation(s)
- Hye Ree Yoon
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea
| | - Gyoung Jin Park
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea
| | - Anand Balupuri
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea
| | - Nam Sook Kang
- Graduate School of New Drug Discovery and Development, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, South Korea
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4
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Luo Y, Wang P, Mou M, Zheng H, Hong J, Tao L, Zhu F. A novel strategy for designing the magic shotguns for distantly related target pairs. Brief Bioinform 2023; 24:6984790. [PMID: 36631399 DOI: 10.1093/bib/bbac621] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/09/2022] [Accepted: 12/17/2022] [Indexed: 01/13/2023] Open
Abstract
Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.
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Affiliation(s)
- Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanqi Zheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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5
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Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J Med Chem 2022; 65:13771-13783. [PMID: 36256484 DOI: 10.1021/acs.jmedchem.2c00931] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Christel Schmitt-Valencia
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Marcel Hibert
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Pascal Villa
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
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6
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Yang JR, Chen Q, Wang H, Hu XY, Guo YM, Chen JZ. Reliable CA-(Q)SAR generation based on entropy weight optimized by grid search and correction factors. Comput Biol Med 2022; 146:105573. [DOI: 10.1016/j.compbiomed.2022.105573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/31/2022] [Accepted: 04/26/2022] [Indexed: 11/03/2022]
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7
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Sydow D, Aßmann E, Kooistra AJ, Rippmann F, Volkamer A. KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome. J Chem Inf Model 2022; 62:2600-2616. [PMID: 35536589 DOI: 10.1021/acs.jcim.2c00050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.
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Affiliation(s)
- Dominique Sydow
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Eva Aßmann
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Albert J Kooistra
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
| | - Friedrich Rippmann
- Computational Chemistry & Biologics, Merck Healthcare KGaA, Frankfurter Str. 250, 64293 Darmstadt, Germany
| | - Andrea Volkamer
- In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
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8
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Humer C, Heberle H, Montanari F, Wolf T, Huber F, Henderson R, Heinrich J, Streit M. ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations. J Cheminform 2022; 14:21. [PMID: 35379315 PMCID: PMC8981840 DOI: 10.1186/s13321-022-00600-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/12/2022] [Indexed: 11/10/2022] Open
Abstract
The introduction of machine learning to small molecule research- an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.
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Affiliation(s)
| | - Henry Heberle
- Division Crop Science, Bayer AG, 40789, Monheim am Rhein, DE, Germany.
| | | | - Thomas Wolf
- Division Crop Science, Bayer AG, 65926, Frankfurt, DE, Germany
| | - Florian Huber
- Division Crop Science, Bayer AG, 65926, Frankfurt, DE, Germany
| | - Ryan Henderson
- Digital Technologies, Bayer AG, 13353, Berlin, DE, Germany
| | - Julian Heinrich
- Division Crop Science, Bayer AG, 40789, Monheim am Rhein, DE, Germany.
| | - Marc Streit
- Johannes Kepler University Linz, Linz, Austria.
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9
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Chen Y, Wang ZZ, Hao GF, Song BA. Web support for the more efficient discovery of kinase inhibitors. Drug Discov Today 2022; 27:2216-2225. [DOI: 10.1016/j.drudis.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/16/2022] [Accepted: 04/01/2022] [Indexed: 11/24/2022]
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10
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de Esch IJP, Erlanson DA, Jahnke W, Johnson CN, Walsh L. Fragment-to-Lead Medicinal Chemistry Publications in 2020. J Med Chem 2022; 65:84-99. [PMID: 34928151 PMCID: PMC8762670 DOI: 10.1021/acs.jmedchem.1c01803] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Indexed: 12/28/2022]
Abstract
Fragment-based drug discovery (FBDD) continues to evolve and make an impact in the pharmaceutical sciences. We summarize successful fragment-to-lead studies that were published in 2020. Having systematically analyzed annual scientific outputs since 2015, we discuss trends and best practices in terms of fragment libraries, target proteins, screening technologies, hit-optimization strategies, and the properties of hit fragments and the leads resulting from them. As well as the tabulated Fragment-to-Lead (F2L) programs, our 2020 literature review identifies several trends and innovations that promise to further increase the success of FBDD. These include developing structurally novel screening fragments, improving fragment-screening technologies, using new computer-aided design and virtual screening approaches, and combining FBDD with other innovative drug-discovery technologies.
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Affiliation(s)
- Iwan J. P. de Esch
- Division
of Medicinal Chemistry, Amsterdam Institute of Molecular and Life
Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Daniel A. Erlanson
- Frontier
Medicines, 151 Oyster
Point Blvd., South San Francisco, California 94080, United States
| | - Wolfgang Jahnke
- Novartis
Institutes for Biomedical Research, Chemical
Biology and Therapeutics, 4002 Basel, Switzerland
| | - Christopher N. Johnson
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Louise Walsh
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
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11
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Andrianov GV, Ong WJG, Serebriiskii I, Karanicolas J. Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging. J Chem Inf Model 2021; 61:5967-5987. [PMID: 34762402 PMCID: PMC8865965 DOI: 10.1021/acs.jcim.1c00630] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions─or billions─of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFRT790M, and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.
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Affiliation(s)
- Grigorii V. Andrianov
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia, 420008
| | - Wern Juin Gabriel Ong
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Bowdoin College, Brunswick, ME 04011
| | - Ilya Serebriiskii
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia, 420008
| | - John Karanicolas
- Program in Molecular Therapeutics, Fox Chase Cancer Center, Philadelphia, PA 19111-2497,To whom correspondence should be addressed. , 215-728-7067
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12
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Hu L, Yang Y, Zheng S, Xu J, Ran T, Chen H. Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches. J Chem Inf Model 2021; 61:4900-4912. [PMID: 34586824 DOI: 10.1021/acs.jcim.1c00608] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets.
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Affiliation(s)
- Lizhao Hu
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Yuyao Yang
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Shuangjia Zheng
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Jun Xu
- School of Biotechnology and Health Sciences, Wuyi University, Jiangmen 529020, China.,Research Center for Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-Sen University, 132 East Circle at University City, Guangzhou 510006, China
| | - Ting Ran
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China
| | - Hongming Chen
- Center of Cell Lineage and Atlas, Bioland Laboratory (Guangzhou Regenerative Medicine and Health-Guangdong Laboratory), Guangzhou 510530, China
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13
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Kanev GK, de Graaf C, Westerman BA, de Esch IJP, Kooistra AJ. KLIFS: an overhaul after the first 5 years of supporting kinase research. Nucleic Acids Res 2021; 49:D562-D569. [PMID: 33084889 PMCID: PMC7778968 DOI: 10.1093/nar/gkaa895] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated that keeps on growing. KLIFS-Kinase-Ligand Interaction Fingerprints and Structures-is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.
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Affiliation(s)
- Georgi K Kanev
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- Sosei Heptares, Steinmetz Building, Granta Park, Great Abington, Cambridge, CB21 6DG, UK
| | - Bart A Westerman
- Department of Neurosurgery, Amsterdam University Medical Centers, Cancer Center Amsterdam, Brain Tumor Center Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Albert J Kooistra
- Department of Drug Design and Pharmacology, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark
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