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Zhang Y, Yao L, Chung CR, Huang Y, Li S, Zhang W, Pang Y, Lee TY. KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data. iScience 2024; 27:109333. [PMID: 38523792 PMCID: PMC10959666 DOI: 10.1016/j.isci.2024.109333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 12/07/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024] Open
Abstract
Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.
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Affiliation(s)
- Yuntian Zhang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320953, Taiwan
| | - Yixian Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenyang Zhang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDSB), National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan
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2
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Mersal KI, Abdel-Maksoud MS, Ali EMH, Ammar UM, Zaraei SO, Haque MM, Das T, Hassan NF, Kim EE, Lee JS, Park H, Lee KH, El-Gamal MI, Kim HK, Ibrahim TM, Oh CH. Evaluation of novel pyrazol-4-yl pyridine derivatives possessing arylsulfonamide tethers as c-Jun N-terminal kinase (JNK) inhibitors in leukemia cells. Eur J Med Chem 2023; 261:115779. [PMID: 37776574 DOI: 10.1016/j.ejmech.2023.115779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/14/2023] [Accepted: 08/29/2023] [Indexed: 10/02/2023]
Abstract
A series of 36 pyrazol-4-yl pyridine derivatives (8a-i, 9a-i, 10a-i, and 11a-i) was designed, synthesized, and evaluated for its antiproliferative activity over NCI-60 cancer cell line panel and inhibitory effect against JNK isoforms (JNK1, JNK2, and JNK3). All the synthesized compounds were tested against the NCI-60 cancer cell line panel. Compounds 11b, 11c, 11g, and 11i were selected to determine their GI50s and exerted a superior potency over the reference standard SP600125 against the tested cell lines. 11c showed a GI50 of 1.28 μM against K562 leukemic cells. Vero cells were used to assess 11c cytotoxicity compared to the tested cancer cells. The target compounds were tested against hJNK isoforms in which compound 11e exhibited the highest potency against JNK isoforms with IC50 values of 1.81, 12.7, and 10.5 nM against JNK1, JNK2, and JNK3, respectively. Kinase profiling of 11e showed higher JNK selectivity in 50 kinase panels. Compounds 11c and 11e showed cell population arrest at the G2/M phase, induced early apoptosis, and slightly inhibited beclin-1 production at higher concentrations in K562 leukemia cells relative to SP600125. NanoBRET assay of 11e showed intracellular JNK1 inhibition with an IC50 of 2.81 μM. Also, it inhibited CYP2D6 and 3A4 with different extent and its hERG activity showed little cardiac toxicity with an IC50 of 4.82 μM. hJNK3 was used as a template to generate the hJNK1 crystal structure to explore the binding mode of 11e (PDB ID: 8ENJ) with a resolution of 2.8 °A and showed a typical type I kinase inhibition against hJNK1. Binding energy scores showed that selectivity of 11e towards JNK1 could be attributed to additional hydrophobic interactions relative to JNK3.
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Affiliation(s)
- Karim I Mersal
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Cairo, 12055, Egypt; University of Science & Technology (UST), Daejeon, Yuseong-gu, 34113, Republic of Korea; Center of Biomaterials, Korea Institute of Science & Technology (KIST School), Seoul, Seongbuk-gu, 02792, Republic of Korea
| | - Mohammed S Abdel-Maksoud
- Medicinal & Pharmaceutical Chemistry Department, Pharmaceutical and Drug Industries Research Institute, National Research Centre NRC (ID: 60014618), Dokki, Giza, 12622, Egypt
| | - Eslam M H Ali
- Pharmaceutical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Cairo, 12055, Egypt; Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, 575 West Stadium Avenue, West Lafayette, IN, 47907, USA
| | - Usama M Ammar
- School of Applied Sciences, Edinburgh Napier University, Sighthill Campus, 9 Sighthill Court, Edinburgh, EH11 4BN, United Kingdom
| | - Seyed-Omar Zaraei
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Md Mamunul Haque
- Department of Neural and Pain Sciences, School of Dentistry, University of Maryland, Baltimore, MD, 21201, USA
| | - Tanuza Das
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Noha F Hassan
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Cairo, 12055, Egypt
| | - Eunice EunKyeong Kim
- Biomedical Research Institute, Korea Institute of Science and Technology, Seoul, 02792, South Korea
| | - Jun-Seok Lee
- Department of Pharmacology, College of Medicine, Korea University, Seoul, 02841, South Korea
| | - HaJeung Park
- The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, USA
| | - Kwan Hyi Lee
- Center for Advanced Biomolecular Recognition, Korea Institute of Science & Technology (KIST School), Seoul, Seongbuk-gu, 02792, Republic of Korea; KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul, 02841, Republic of Korea
| | - Mohammed I El-Gamal
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates; Department of Medicinal Chemistry, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt
| | - Hee-Kwon Kim
- Department of Nuclear Medicine, Molecular Imaging & Therapeutic Medicine Research Center, Jeonbuk National University Medical School and Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, Republic of Korea; Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonji-ro, Deokjin-gu, Jeonju, 54907, Republic of Korea.
| | - Tamer M Ibrahim
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Kafrelsheikh University, Kafrelsheikh, P.O. Box 33516, Egypt
| | - Chang-Hyun Oh
- University of Science & Technology (UST), Daejeon, Yuseong-gu, 34113, Republic of Korea; Center of Biomaterials, Korea Institute of Science & Technology (KIST School), Seoul, Seongbuk-gu, 02792, Republic of Korea.
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3
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Green JR, Mahalingaiah PKS, Gopalakrishnan SM, Liguori MJ, Mittelstadt SW, Blomme EAG, Van Vleet TR. Off-target pharmacological activity at various kinases: Potential functional and pathological side effects. J Pharmacol Toxicol Methods 2023; 123:107468. [PMID: 37553032 DOI: 10.1016/j.vascn.2023.107468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/16/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023]
Abstract
In drug discovery, during the lead optimization and candidate characterization stages, novel small molecules are frequently evaluated in a battery of in vitro pharmacology assays to identify potential unintended, off-target interactions with various receptors, transporters, ion channels, and enzymes, including kinases. Furthermore, these screening panels may also provide utility at later stages of development to provide a mechanistic understanding of unexpected safety findings. Here, we present a compendium of the most likely functional and pathological outcomes associated with interaction(s) to a panel of 95 kinases based on an extensive curation of the scientific literature. This panel of kinases was designed by AbbVie based on safety-related data extracted from the literature, as well as from over 20 years of institutional knowledge generated from discovery efforts. For each kinase, the scientific literature was reviewed using online databases and the most often reported functional and pathological effects were summarized. This work should serve as a practical guide for small molecule drug discovery scientists and clinical investigators to predict and/or interpret adverse effects related to pharmacological interactions with these kinases.
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Affiliation(s)
- Jonathon R Green
- Departments of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States.
| | | | - Sujatha M Gopalakrishnan
- Drug Discovery Science and Technology, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Michael J Liguori
- Departments of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Scott W Mittelstadt
- Departments of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Eric A G Blomme
- Departments of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States
| | - Terry R Van Vleet
- Departments of Preclinical Safety, AbbVie, 1 North Waukegan Road, North Chicago, IL 60064, United States
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Park H, Hong S, Lee M, Kang S, Brahma R, Cho KH, Shin JM. AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors. Sci Rep 2023; 13:10268. [PMID: 37355672 PMCID: PMC10290719 DOI: 10.1038/s41598-023-37456-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/22/2023] [Indexed: 06/26/2023] Open
Abstract
The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.
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Affiliation(s)
- Hyejin Park
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sujeong Hong
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Myeonghun Lee
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sungil Kang
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Min Shin
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.
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5
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Ojha AA, Srivastava A, Votapka LW, Amaro RE. Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations. J Chem Inf Model 2023; 63:2469-2482. [PMID: 37023323 PMCID: PMC10131228 DOI: 10.1021/acs.jcim.2c01589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.
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Affiliation(s)
- Anupam Anand Ojha
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Ambuj Srivastava
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Lane William Votapka
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, United States
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6
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Computational Approaches to Enzyme Inhibition by Marine Natural Products in the Search for New Drugs. Mar Drugs 2023; 21:md21020100. [PMID: 36827141 PMCID: PMC9961086 DOI: 10.3390/md21020100] [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: 12/21/2022] [Revised: 01/26/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
The exploration of biologically relevant chemical space for the discovery of small bioactive molecules present in marine organisms has led not only to important advances in certain therapeutic areas, but also to a better understanding of many life processes. The still largely untapped reservoir of countless metabolites that play biological roles in marine invertebrates and microorganisms opens new avenues and poses new challenges for research. Computational technologies provide the means to (i) organize chemical and biological information in easily searchable and hyperlinked databases and knowledgebases; (ii) carry out cheminformatic analyses on natural products; (iii) mine microbial genomes for known and cryptic biosynthetic pathways; (iv) explore global networks that connect active compounds to their targets (often including enzymes); (v) solve structures of ligands, targets, and their respective complexes using X-ray crystallography and NMR techniques, thus enabling virtual screening and structure-based drug design; and (vi) build molecular models to simulate ligand binding and understand mechanisms of action in atomic detail. Marine natural products are viewed today not only as potential drugs, but also as an invaluable source of chemical inspiration for the development of novel chemotypes to be used in chemical biology and medicinal chemistry research.
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7
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Wang T, Pulkkinen OI, Aittokallio T. Target-specific compound selectivity for multi-target drug discovery and repurposing. Front Pharmacol 2022; 13:1003480. [PMID: 36225560 PMCID: PMC9549418 DOI: 10.3389/fphar.2022.1003480] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.
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Affiliation(s)
- Tianduanyi Wang
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Otto I. Pulkkinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics and InFLAMES Research Flagship, University of Turku, Turku, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland
- Department of Mathematics and Statistics and InFLAMES Research Flagship, University of Turku, Turku, Finland
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
- *Correspondence: Tero Aittokallio,
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8
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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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Wang B, Wu H, Hu C, Wang H, Liu J, Wang W, Liu Q. An overview of kinase downregulators and recent advances in discovery approaches. Signal Transduct Target Ther 2021; 6:423. [PMID: 34924565 PMCID: PMC8685278 DOI: 10.1038/s41392-021-00826-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 10/28/2021] [Accepted: 11/05/2021] [Indexed: 12/17/2022] Open
Abstract
Since the clinical approval of imatinib, the discovery of protein kinase downregulators entered a prosperous age. However, challenges still exist in the discovery of kinase downregulator drugs, such as the high failure rate during development, side effects, and drug-resistance problems. With the progress made through multidisciplinary efforts, an increasing number of new approaches have been applied to solve the above problems during the discovery process of kinase downregulators. In terms of in vitro and in vivo drug evaluation, progress was also made in cellular and animal model platforms for better and more clinically relevant drug assessment. Here, we review the advances in drug design strategies, drug property evaluation technologies, and efficacy evaluation models and technologies. Finally, we discuss the challenges and perspectives in the development of kinase downregulator drugs.
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Affiliation(s)
- Beilei Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Hong Wu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Chen Hu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Haizhen Wang
- Hefei PreceDo pharmaceuticals Co., Ltd, Hefei, Anhui, 230088, People's Republic of China
| | - Jing Liu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Wenchao Wang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China
| | - Qingsong Liu
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.
- Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People's Republic of China.
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10
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Born J, Huynh T, Stroobants A, Cornell WD, Manica M. Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model. J Chem Inf Model 2021; 62:240-257. [PMID: 34905358 DOI: 10.1021/acs.jcim.1c00889] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Recent advances in deep learning have enabled the development of large-scale multimodal models for virtual screening and de novo molecular design. The human kinome with its abundant sequence and inhibitor data presents an attractive opportunity to develop proteochemometric models that exploit the size and internal diversity of this family of targets. Here, we challenge a standard practice in sequence-based affinity prediction models: instead of leveraging the full primary structure of proteins, each target is represented by a sequence of 29 discontiguous residues defining the ATP binding site. In kinase-ligand binding affinity prediction, our results show that the reduced active site sequence representation is not only computationally more efficient but consistently yields significantly higher performance than the full primary structure. This trend persists across different models, data sets, and performance metrics and holds true when predicting pIC50 for both unseen ligands and kinases. Our interpretability analysis reveals a potential explanation for the superiority of the active site models: whereas only mild statistical effects about the extraction of three-dimensional (3D) interaction sites take place in the full sequence models, the active site models are equipped with an implicit but strong inductive bias about the 3D structure stemming from the discontiguity of the active sites. Moreover, in direct comparisons, our models perform similarly or better than previous state-of-the-art approaches in affinity prediction. We then investigate a de novo molecular design task and find that the active site provides benefits in the computational efficiency, but otherwise, both kinase representations yield similar optimized affinities (for both SMILES- and SELFIES-based molecular generators). Our work challenges the assumption that the full primary structure is indispensable for modeling human kinases.
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Affiliation(s)
- Jannis Born
- IBM Research Europe, 8804 Rüschlikon, Switzerland.,Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Tien Huynh
- IBM Research, Yorktown Heights, New York 10598, United States
| | - Astrid Stroobants
- Department of Chemistry, Imperial College London, SW7 2AZ London, United Kingdom
| | - Wendy D Cornell
- IBM Research, Yorktown Heights, New York 10598, United States
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11
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Shen Z, Yan YH, Yang S, Zhu S, Yuan Y, Qiu Z, Jia H, Wang R, Li GB, Li H. ProfKin: A comprehensive web server for structure-based kinase profiling. Eur J Med Chem 2021; 225:113772. [PMID: 34411891 DOI: 10.1016/j.ejmech.2021.113772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/30/2021] [Accepted: 08/09/2021] [Indexed: 11/16/2022]
Abstract
Protein kinases are central mediators of signal-transduction cascades and attractive drug targets for therapeutic intervention. Since kinases are structurally and mechanistically related to each other, kinase inhibitor selectivity is often investigated by kinase profiling and considered as an important index for drug discovery. We here describe a versatile web server termed ProfKin for structure-based kinase profiling, which is based on a kinase-ligand focused database (KinLigDB). It provides all ready-to-use 3D structure coordinates of 4219 kinase-ligand complex structures covering 297 human kinases and the associated information, particularly including binding site type, binding ligand type, interaction fingerprints, downstream molecules and related human diseases. The web server works via predicting possible binding modes for the query molecule, prioritizing the binding modes guided by an interaction fingerprint analysis method, and giving a list of ranked kinases by a comprehensive index. Users can freely select entire or part of the KinLigDB database, e.g. via subfamily and binding site type, to customize the profiling contents. The superimpositions of the predicted binding poses of the query molecule with reference binding modes can be visually inspected on the website. The additional classification attributes and phylogenetic tree are also given for each top-ranked kinase.
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Affiliation(s)
- Zihao Shen
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China
| | - Yu-Hang Yan
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Shuo Yang
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China
| | - Sang Zhu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Yuan Yuan
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Zhiqiang Qiu
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Huan Jia
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Ruiqiong Wang
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug Targeting and Drug Delivery System of Ministry of Education, West China School of Pharmacy, Sichuan University, Sichuan, 610041, China.
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai, 200237, China.
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12
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Neault N, O’Reilly S, Baig AT, Plaza-Diaz J, Azimi M, Farooq F, Baird SD, MacKenzie A. High-throughput kinome-RNAi screen identifies protein kinase R activator (PACT) as a novel genetic modifier of CUG foci integrity in myotonic dystrophy type 1 (DM1). PLoS One 2021; 16:e0256276. [PMID: 34520479 PMCID: PMC8439471 DOI: 10.1371/journal.pone.0256276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 08/03/2021] [Indexed: 11/24/2022] Open
Abstract
Myotonic Dystrophy Type 1 (DM1) is the most common form of adult muscular dystrophy (~1:8000). In DM1, expansion of CTG trinucleotide repeats in the 3' untranslated region of the dystrophia myotonica protein kinase (DMPK) gene results in DMPK mRNA hairpin structures which aggregate as insoluble ribonuclear foci and sequester several RNA-binding proteins. The resulting sequestration and misregulation of important splicing factors, such as muscleblind-like 1 (MBNL1), causes the aberrant expression of fetal transcripts for several genes that contribute to the disease phenotype. Previous work has shown that antisense oligonucleotide-mediated disaggregation of the intranuclear foci has the potential to reverse downstream anomalies. To explore whether the nuclear foci are, to some extent, controlled by cell signalling pathways, we have performed a screen using a small interfering RNA (siRNA) library targeting 518 protein kinases to look at kinomic modulation of foci integrity. RNA foci were visualized by in situ hybridization of a fluorescent-tagged (CAG)10 probe directed towards the expanded DMPK mRNA and the cross-sectional area and number of foci per nuclei were recorded. From our screen, we have identified PACT (protein kinase R (PKR) activator) as a novel modulator of foci integrity and have shown that PACT knockdown can both increase MBNL1 protein levels; however, these changes are not suffcient for significant correction of downstream spliceopathies.
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Affiliation(s)
- Nafisa Neault
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Sean O’Reilly
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Aiman Tariq Baig
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Julio Plaza-Diaz
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Mehrdad Azimi
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Faraz Farooq
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Stephen D. Baird
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
| | - Alex MacKenzie
- Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
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13
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Rensi S, Keys A, Lo YC, Derry A, McInnes G, Liu T, Altman R. Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12009582. [PMID: 32511288 PMCID: PMC7263764 DOI: 10.26434/chemrxiv.12009582] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 03/20/2020] [Indexed: 04/16/2023]
Abstract
The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.
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Affiliation(s)
- Stefano Rensi
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Allison Keys
- Department of Computer Science, Stanford University, CA, USA
| | - Yu-Chen Lo
- Pediatrics, Bass Center for Childhood Cancer, Stanford School of Medicine, Stanford, CA, USA
| | - Alexander Derry
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Greg McInnes
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Tianyun Liu
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Russ Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Departments of Genetics, Stanford University, Stanford, CA, USA
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14
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Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies. Cell Chem Biol 2019; 26:1608-1622.e6. [DOI: 10.1016/j.chembiol.2019.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 07/18/2019] [Accepted: 08/21/2019] [Indexed: 02/06/2023]
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15
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Torng W, Altman RB. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. J Chem Inf Model 2019; 59:4131-4149. [DOI: 10.1021/acs.jcim.9b00628] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Wen Torng
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Russ B. Altman
- Deparment of Bioengineering, Stanford University, Stanford, California 94305, United States
- Department of Genetics, Stanford University, Stanford, California 94305, United States
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16
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Pocket similarity identifies selective estrogen receptor modulators as microtubule modulators at the taxane site. Nat Commun 2019; 10:1033. [PMID: 30833575 PMCID: PMC6399299 DOI: 10.1038/s41467-019-08965-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 01/19/2019] [Indexed: 02/01/2023] Open
Abstract
Taxanes are a family of natural products with a broad spectrum of anticancer activity. This activity is mediated by interaction with the taxane site of beta-tubulin, leading to microtubule stabilization and cell death. Although widely used in the treatment of breast cancer and other malignancies, existing taxane-based therapies including paclitaxel and the second-generation docetaxel are currently limited by severe adverse effects and dose-limiting toxicity. To discover taxane site modulators, we employ a computational binding site similarity screen of > 14,000 drug-like pockets from PDB, revealing an unexpected similarity between the estrogen receptor and the beta-tubulin taxane binding pocket. Evaluation of nine selective estrogen receptor modulators (SERMs) via cellular and biochemical assays confirms taxane site interaction, microtubule stabilization, and cell proliferation inhibition. Our study demonstrates that SERMs can modulate microtubule assembly and raises the possibility of an estrogen receptor-independent mechanism for inhibiting cell proliferation. Taxanes are natural products which bind beta-tubulin, stabilize microtubules and have a broad spectrum of anticancer activity. Here authors employ a computational binding site similarity screen and cell-based assays to reveal a SERM cross-reactivity between the estrogen receptor and the beta-tubulin taxane binding pocket.
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