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Li C, Yang Q, Zhang L. Identification of putative allosteric inhibitors of BCKDK via virtual screening and biological evaluation. J Enzyme Inhib Med Chem 2024; 39:2290458. [PMID: 38059302 DOI: 10.1080/14756366.2023.2290458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 11/21/2023] [Indexed: 12/08/2023] Open
Abstract
Abnormal accumulation of branched-chain amino acids (BCAAs) can lead to metabolic diseases and cancers. Branched-chain α-keto acid dehydrogenase kinase (BCKDK) is a key negative regulator of BCAA catabolism, and targeting BCKDK provides a promising therapeutic approach for diseases caused by BCAA accumulation. Here, we screened PPHN and POAB as novel putative allosteric inhibitors by integrating allosteric binding site prediction, large-scale ligand database virtual screening, and bioactivity evaluation assays. Both of them showed a high binding affinity to BCKDK, with Kd values of 3.9 μM and 1.86 μM, respectively. In vivo experiments, the inhibitors demonstrated superior kinase inhibitory activity and notable antiproliferative and proapoptotic effects on diverse cancer cells. Finally, bulk RNA-seq analysis revealed that PPHN and POAB suppressed cell growth through a range of signalling pathways. Taken together, our findings highlight two novel BCKDK inhibitors as potent therapeutic candidates for metabolic diseases and cancers associated with BCAA dysfunctional metabolism.
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Affiliation(s)
- Chunqiong Li
- Genomics Center, Chinese Institute for Brain Research, Beijing, China
| | - Quanjun Yang
- Department of Pharmacy, Shanghai Sixth People's Hospital Affiliated Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Zhang
- Genomics Center, Chinese Institute for Brain Research, Beijing, China
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2
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Stuart DD, Van Zant W, Valiulis S, Malinick AS, Hanson V, Cheng Q. Trends in surface plasmon resonance biosensing: materials, methods, and machine learning. Anal Bioanal Chem 2024:10.1007/s00216-024-05367-w. [PMID: 38839686 DOI: 10.1007/s00216-024-05367-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/13/2024] [Accepted: 05/27/2024] [Indexed: 06/07/2024]
Abstract
Surface plasmon resonance (SPR) proves to be one of the most effective methods of label-free detection and has been integral for the study of biomolecular interactions and the development of biosensors. This trend delves into the latest SPR research and progress built upon the Kretschmann configuration, a pivotal platform, and highlights three key developments that have enhanced the capabilities of the technique. We will first cover a range of explorations of novel plasmonic materials that have shaped SPR performance. Innovative signal transduction and collection, which leverages traditional materials and emerging alternatives, will then be discussed. Finally, the evolving landscape of data analysis, including the integration of machine learning algorithms to navigate complex SPR datasets, will be reviewed. We will also discuss the implementation of these improvements that have enabled new biosensing functions. These advancements not only pave the way for enhanced biosensing in general but also open new avenues for the technique to play a more significant role in research concerning human health.
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Affiliation(s)
- Daniel D Stuart
- Department of Chemistry, University of California, Riverside, CA, 92521, USA
| | - Westley Van Zant
- Department of Chemistry, University of California, Riverside, CA, 92521, USA
| | - Santino Valiulis
- Department of Chemistry, University of California, Riverside, CA, 92521, USA
| | | | - Victor Hanson
- Department of Chemistry, University of California, Riverside, CA, 92521, USA
| | - Quan Cheng
- Department of Chemistry, University of California, Riverside, CA, 92521, USA.
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3
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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4
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Chen S, Yang Z, Sun W, Tian K, Sun P, Wu J. TMV-CP based rational design and discovery of α-Amide phosphate derivatives as anti plant viral agents. Bioorg Chem 2024; 147:107415. [PMID: 38701597 DOI: 10.1016/j.bioorg.2024.107415] [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: 12/20/2023] [Revised: 04/17/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024]
Abstract
The tobacco mosaic virus coat protein (TMV-CP) is indispensable for the virus's replication, movement and transmission, as well as for the host plant's immune system to recognize it. It constitutes the outermost layer of the virus particle, and serves as an essential component of the virus structure. TMV-CP is essential for initiating and extending viral assembly, playing a crucial role in the self-assembly process of Tobacco Mosaic Virus (TMV). This research employed TMV-CP as a primary target for virtual screening, from which a library of 43,417 compounds was sourced and SH-05 was chosen as the lead compound. Consequently, a series of α-amide phosphate derivatives were designed and synthesized, exhibiting remarkable anti-TMV efficacy. The synthesized compounds were found to be beneficial in treating TMV, with compound 3g displaying a slightly better curative effect than Ningnanmycin (NNM) (EC50 = 304.54 µg/mL) at an EC50 of 291.9 µg/mL. Additionally, 3g exhibited comparable inactivation activity (EC50 = 63.2 µg/mL) to NNM (EC50 = 67.5 µg/mL) and similar protective activity (EC50 = 228.9 µg/mL) to NNM (EC50 = 219.7 µg/mL). Microscale thermal analysis revealed that the binding of 3g (Kd = 4.5 ± 1.9 µM) to TMV-CP showed the same level with NNM (Kd = 5.5 ± 2.6 µM). Results from transmission electron microscopy indicated that 3g could disrupt the structure of TMV virus particles. The toxicity prediction indicated that 3g was low toxicity. Molecular docking showed that 3g interacted with TMV-CP through hydrogen bond, attractive charge interaction and π-Cation interaction. This research provided a novel α-amide phosphate structure target TMV-CP, which may help the discovery of new anti-TMV agents in the future.
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Affiliation(s)
- Shunhong Chen
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Zhaokai Yang
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Wei Sun
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Kuan Tian
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Ping Sun
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China
| | - Jian Wu
- State Key Laboratory of Green Pesticide; Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Huaxi District, Guiyang 550025, China.
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5
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Zhu J, Gu Z, Pei J, Lai L. DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking. Chem Sci 2024; 15:7926-7942. [PMID: 38817560 PMCID: PMC11134415 DOI: 10.1039/d3sc06803j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/07/2024] [Indexed: 06/01/2024] Open
Abstract
Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.
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Affiliation(s)
- Jintao Zhu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
| | - Zhonghui Gu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University Beijing 100871 China
- BNLMS, College of Chemistry and Molecular Engineering, Peking University Beijing 100871 China
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies Chengdu Sichuan China
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6
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Liu F, Kaplan AL, Levring J, Einsiedel J, Tiedt S, Distler K, Omattage NS, Kondratov IS, Moroz YS, Pietz HL, Irwin JJ, Gmeiner P, Shoichet BK, Chen J. Structure-based discovery of CFTR potentiators and inhibitors. Cell 2024:S0092-8674(24)00472-0. [PMID: 38810646 DOI: 10.1016/j.cell.2024.04.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/19/2024] [Accepted: 04/29/2024] [Indexed: 05/31/2024]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.
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Affiliation(s)
- Fangyu Liu
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Anat Levit Kaplan
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jesper Levring
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Jürgen Einsiedel
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Stephanie Tiedt
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Katharina Distler
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany
| | - Natalie S Omattage
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - Ivan S Kondratov
- Enamine Ltd., Chervonotkatska Street 78, 02094 Kyïv, Ukraine; V.P. Kukhar Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, 02660 Kyïv, Ukraine
| | - Yurii S Moroz
- Chemspace, Chervonotkatska Street 85, 02094 Kyïv, Ukraine; Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, 01601 Kyïv, Ukraine
| | - Harlan L Pietz
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Medicinal Chemistry, Friedrich-Alexander University Erlangen-Nürnberg, Nikolaus-Fiebiger-Straße 10, D-91058 Erlangen, Germany.
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Jue Chen
- Laboratory of Membrane Biology and Biophysics, The Rockefeller University, New York, NY 10065, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
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7
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures guide prospective ligand discovery. Science 2024:eadn6354. [PMID: 38753765 DOI: 10.1126/science.adn6354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
AlphaFold2 (AF2) models have had wide impact, but they have had mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and 5-HT2A receptors, testing hundreds of new molecules and comparing results to docking against the experimental structures. Hit rates were high and similar for the experimental and the AF2 structures, as were affinities. The success of docking against the AF2 models was achieved despite differences in orthosteric residue conformations versus the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based ligand discovery.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Manish K Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Isabella S Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Tia A Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
- Enamine Ltd., Kyiv 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew C Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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8
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Marques RE, Shimizu JF, Nogueira ML, Vasilakis N. Current challenges in the discovery of treatments against Mayaro fever. Expert Opin Ther Targets 2024:1-12. [PMID: 38714500 DOI: 10.1080/14728222.2024.2351504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 05/01/2024] [Indexed: 05/10/2024]
Abstract
INTRODUCTION Mayaro fever is an emerging viral disease that manifests as an acute febrile illness. The disease is self-limiting, however joint pain can persist for months leading to chronic arthralgia. There is no specific treatment available, which ultimately leads to socioeconomic losses in populations at risk as well as strains to the public health systems. AREAS COVERED We reviewed the candidate treatments proposed for Mayaro virus (MAYV) infection and disease, including antiviral compounds targeting viral or host mechanisms, and pathways involved in disease development and pathogenicity. We assessed compound screening technologies and experimental infection models used in these studies and indicated the advantages and limitations of available technologies and intended therapeutic strategies. EXPERT OPINION Although several compounds have been suggested as candidate treatments against MAYV infection, notably those with antiviral activity, most compounds were assessed only in vitro. Compounds rarely progress toin vivo or preclinical studies, and such difficulty may be associated with limited experimental models. MAYV biology is largely inferred from related alphaviruses and reflected by few studies focusing on target proteins or mechanisms of action for MAYV. Therapeutic strategies targeting pathogenic inflammatory responses have shown potential against MAYV-induced disease in vivo, which might reduce long-term sequelae.
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Affiliation(s)
- Rafael Elias Marques
- Brazilian Biosciences National Laboratory - LNBio, Brazilian Center for Research in Energy and Materials - CNPEM, Campinas, São Paulo, Brazil
| | - Jacqueline Farinha Shimizu
- Brazilian Biosciences National Laboratory - LNBio, Brazilian Center for Research in Energy and Materials - CNPEM, Campinas, São Paulo, Brazil
| | - Maurício Lacerda Nogueira
- Laboratórios de Pesquisas em Virologia, Departamento de Doenças Dermatológicas, Infecciosas e Parasitá-rias, Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, SP, Brazil
- Department of Pathology and Center for Research in Vector-Borne and Zoonotic Diseases, University of Texas Medical Branch, Galveston, TX, USA
| | - Nikos Vasilakis
- Department of Pathology and Center for Research in Vector-Borne and Zoonotic Diseases, University of Texas Medical Branch, Galveston, TX, USA
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Chen Z, Yu J, Wang H, Xu P, Fan L, Sun F, Huang S, Zhang P, Huang H, Gu S, Zhang B, Zhou Y, Wan X, Pei G, Xu HE, Cheng J, Wang S. Flexible scaffold-based cheminformatics approach for polypharmacological drug design. Cell 2024; 187:2194-2208.e22. [PMID: 38552625 DOI: 10.1016/j.cell.2024.02.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 02/04/2024] [Accepted: 02/27/2024] [Indexed: 04/28/2024]
Abstract
Effective treatments for complex central nervous system (CNS) disorders require drugs with polypharmacology and multifunctionality, yet designing such drugs remains a challenge. Here, we present a flexible scaffold-based cheminformatics approach (FSCA) for the rational design of polypharmacological drugs. FSCA involves fitting a flexible scaffold to different receptors using different binding poses, as exemplified by IHCH-7179, which adopted a "bending-down" binding pose at 5-HT2AR to act as an antagonist and a "stretching-up" binding pose at 5-HT1AR to function as an agonist. IHCH-7179 demonstrated promising results in alleviating cognitive deficits and psychoactive symptoms in mice by blocking 5-HT2AR for psychoactive symptoms and activating 5-HT1AR to alleviate cognitive deficits. By analyzing aminergic receptor structures, we identified two featured motifs, the "agonist filter" and "conformation shaper," which determine ligand binding pose and predict activity at aminergic receptors. With these motifs, FSCA can be applied to the design of polypharmacological ligands at other receptors.
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Affiliation(s)
- Zhangcheng Chen
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Jing Yu
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Huan Wang
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China
| | - Peiyu Xu
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Luyu Fan
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Fengxiu Sun
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Sijie Huang
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Pei Zhang
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Shuo Gu
- ComMedX, Beijing 100094, China
| | | | - Yue Zhou
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Gang Pei
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - H Eric Xu
- State Key Laboratory of Drug Research, Center for Structure and Function of Drug Targets, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
| | - Jianjun Cheng
- iHuman Institute, ShanghaiTech University, Shanghai 201210, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Sheng Wang
- Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
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10
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Zhao H. The Science and Art of Structure-Based Virtual Screening. ACS Med Chem Lett 2024; 15:436-440. [PMID: 38628791 PMCID: PMC11017385 DOI: 10.1021/acsmedchemlett.4c00093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 03/11/2024] [Indexed: 04/19/2024] Open
Abstract
Structure-based virtual screening has gained momentum again as the high attrition rate at every stage of drug discovery drives the need to explore a greater chemical space. From the Bayesian perspective, its shortcomings as a viable strategy for sustainable hit discovery are discussed, with regard to the prior hit rates of screening libraries and the performance of computational methods. Lessons are shared in selecting virtual hits for experimental validation learned from a series of eight successful campaigns, one of which impacted the discovery of a drug candidate currently in clinical trials.
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Affiliation(s)
- Hongtao Zhao
- Medicinal Chemistry, Research and Early
Development, Respiratory and Immunology (R&I), BioPharmaceuticals
R&D, AstraZeneca, Gothenburg 43183, Sweden
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11
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Kim H, Lee K, Kim C, Lim J, Kim WY. DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening. J Chem Inf Model 2024; 64:2432-2444. [PMID: 37651152 DOI: 10.1021/acs.jcim.3c01134] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.
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Affiliation(s)
- Hyeongwoo Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Kyunghoon Lee
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Chansu Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - Jaechang Lim
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- HITS Incorporation, 124 Teheran-ro, Gangnam-gu, Seoul 06234, Republic of Korea
- AI Institute, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
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12
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Marin E, Kovaleva M, Kadukova M, Mustafin K, Khorn P, Rogachev A, Mishin A, Guskov A, Borshchevskiy V. Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking. J Chem Inf Model 2024; 64:2612-2623. [PMID: 38157481 PMCID: PMC11005039 DOI: 10.1021/acs.jcim.3c01661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 01/03/2024]
Abstract
Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.
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Affiliation(s)
- Egor Marin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Margarita Kovaleva
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Maria Kadukova
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- University
Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
| | - Khalid Mustafin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Polina Khorn
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Andrey Rogachev
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
| | - Alexey Mishin
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
| | - Albert Guskov
- Groningen
Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Valentin Borshchevskiy
- Research
Center for Molecular Mechanisms of Aging and Age-related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia
- Joint
Institute for Nuclear Research, Dubna 141980, Russian
Federation
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13
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Zhang X, Gao H, Wang H, Chen Z, Zhang Z, Chen X, Li Y, Qi Y, Wang R. PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2205-2220. [PMID: 37319418 DOI: 10.1021/acs.jcim.3c00253] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.
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Affiliation(s)
- Xiangying Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haotian Gao
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haojie Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhihang Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhe Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Xinchong Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yifei Qi
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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14
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Dou X, Huo T, Liu Y, Pang Z, Su L, Zhao X, Peng X, Liu Z, Zhang L, Jiao N. Discovery of novel and selective farnesoid X receptor antagonists through structure-based virtual screening, preliminary structure-activity relationship study, and biological evaluation. Eur J Med Chem 2024; 269:116323. [PMID: 38547735 DOI: 10.1016/j.ejmech.2024.116323] [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: 12/26/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/07/2024]
Abstract
Farnesoid X receptor (FXR) is a bile acids receptor and plays a crucial role in regulating bile acids, lipids, and glucose metabolism. Previous research suggests that inhibiting FXR activation can be beneficial in reducing cholesterol and low-density lipoprotein cholesterol (LDL-C) levels, offering potential treatment options for metabolic syndrome with lipid disorders. Herein, we report p-acetylaminobenzene sulfonate derivatives as a novel scaffold of FXR antagonists by multistage screening. Among these derivatives, compound F44-A13 exhibited a half-maximal inhibitory concentration of 1.1 μM. Furthermore, compound F44-A13 demonstrated effective inhibition of FXR activation in cellular assays and exhibited high selectivity over eleven other nuclear receptors. Besides, compound F44-A13 significantly suppressed the regulation of FXR target genes Shp, Besp, and Cyp7a1, while reducing cholesterol levels in human hepatoma HepG2 cells. Pharmacological studies conducted on C57BL/6 mice further confirmed that compound F44-A13 had beneficial effects in reducing cholesterol, triglycerides, and LDL-C levels. These findings highlight that F44-A13 is a highly selective FXR antagonist that might serve as a useful molecule for further FXR studies as well as the development of FXR antagonists for the potential treatment of metabolic diseases with lipid disorders.
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Affiliation(s)
- Xiaodong Dou
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Tongyu Huo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Yameng Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China
| | - Zichen Pang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Lingyu Su
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Xinyi Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Xing Peng
- Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Ning Jiao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China; Changping Laboratory, Yard 28, Science Park Road, Changping District, Beijing, China.
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15
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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16
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Trepte P, Secker C, Olivet J, Blavier J, Kostova S, Maseko SB, Minia I, Silva Ramos E, Cassonnet P, Golusik S, Zenkner M, Beetz S, Liebich MJ, Scharek N, Schütz A, Sperling M, Lisurek M, Wang Y, Spirohn K, Hao T, Calderwood MA, Hill DE, Landthaler M, Choi SG, Twizere JC, Vidal M, Wanker EE. AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor. Mol Syst Biol 2024; 20:428-457. [PMID: 38467836 PMCID: PMC10987651 DOI: 10.1038/s44320-024-00019-8] [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: 02/09/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/13/2024] Open
Abstract
Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Affiliation(s)
- Philipp Trepte
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Brain Development and Disease, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, 1030, Vienna, Austria.
| | - Christopher Secker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
- Zuse Institute Berlin, Berlin, Germany.
| | - Julien Olivet
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Structural Biology Unit, Laboratory of Virology and Chemotherapy, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Katholieke Universiteit Leuven, 3000, Leuven, Belgium
| | - Jeremy Blavier
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Simona Kostova
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Sibusiso B Maseko
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium
| | - Igor Minia
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
| | - Eduardo Silva Ramos
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Patricia Cassonnet
- Département de Virologie, Unité de Génétique Moléculaire des Virus à ARN (GMVR), Institut Pasteur, Centre National de la Recherche Scientifique (CNRS), Université de Paris, Paris, France
| | - Sabrina Golusik
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Martina Zenkner
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Stephanie Beetz
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Mara J Liebich
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Nadine Scharek
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Anja Schütz
- Protein Production & Characterization, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany
| | - Marcel Sperling
- Multifunctional Colloids and Coating, Fraunhofer Institute for Applied Polymer Research (IAP), 14476, Potsdam-Golm, Germany
| | - Michael Lisurek
- Structural Chemistry and Computational Biophysics, Leibniz-Institut für Molekulare Pharmakologie (FMP), 13125, Berlin, Germany
| | - Yang Wang
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Markus Landthaler
- RNA Biology and Posttranscriptional Regulation, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, 13125, Berlin, Germany
- Institute of Biology, Humboldt-Universität zu Berlin, 13125, Berlin, Germany
| | - Soon Gang Choi
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
| | - Jean-Claude Twizere
- Laboratory of Viral Interactomes, Interdisciplinary Cluster for Applied Genoproteomics (GIGA)-Molecular Biology of Diseases, University of Liège, 4000, Liège, Belgium.
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030, Gembloux, Belgium.
- Laboratory of Algal Synthetic and Systems Biology, Division of Science and Math, New York University Abu Dhabi, Abu Dhabi, UAE.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
| | - Erich E Wanker
- Proteomics and Molecular Mechanisms of Neurodegenerative Diseases, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, 13125, Berlin, Germany.
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17
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Guo L, Wang J. GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking. Brief Bioinform 2024; 25:bbae201. [PMID: 38706316 PMCID: PMC11070652 DOI: 10.1093/bib/bbae201] [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: 07/08/2023] [Revised: 02/05/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024] Open
Abstract
Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.
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Affiliation(s)
- Linyuan Guo
- School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Rd. Lu Shan Nan, 410083, Changsha, P.R. China
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18
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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19
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Cao Z, Sciabola S, Wang Y. Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening. J Chem Inf Model 2024; 64:1882-1891. [PMID: 38442000 DOI: 10.1021/acs.jcim.3c01938] [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: 03/07/2024]
Abstract
Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.
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Affiliation(s)
- Zhonglin Cao
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Simone Sciabola
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
| | - Ye Wang
- Medicinal Chemistry, Biogen, Cambridge, Massachusetts 02142, United States
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20
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Dodds M, Guo J, Löhr T, Tibo A, Engkvist O, Janet JP. Sample efficient reinforcement learning with active learning for molecular design. Chem Sci 2024; 15:4146-4160. [PMID: 38487235 PMCID: PMC10935729 DOI: 10.1039/d3sc04653b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024] Open
Abstract
Reinforcement learning (RL) is a powerful and flexible paradigm for searching for solutions in high-dimensional action spaces. However, bridging the gap between playing computer games with thousands of simulated episodes and solving real scientific problems with complex and involved environments (up to actual laboratory experiments) requires improvements in terms of sample efficiency to make the most of expensive information. The discovery of new drugs is a major commercial application of RL, motivated by the very large nature of the chemical space and the need to perform multiparameter optimization (MPO) across different properties. In silico methods, such as virtual library screening (VS) and de novo molecular generation with RL, show great promise in accelerating this search. However, incorporation of increasingly complex computational models in these workflows requires increasing sample efficiency. Here, we introduce an active learning system linked with an RL model (RL-AL) for molecular design, which aims to improve the sample-efficiency of the optimization process. We identity and characterize unique challenges combining RL and AL, investigate the interplay between the systems, and develop a novel AL approach to solve the MPO problem. Our approach greatly expedites the search for novel solutions relative to baseline-RL for simple ligand- and structure-based oracle functions, with a 5-66-fold increase in hits generated for a fixed oracle budget and a 4-64-fold reduction in computational time to find a specific number of hits. Furthermore, compounds discovered through RL-AL display substantial enrichment of a multi-parameter scoring objective, indicating superior efficacy in curating high-scoring compounds, without a reduction in output diversity. This significant acceleration improves the feasibility of oracle functions that have largely been overlooked in RL due to high computational costs, for example free energy perturbation methods, and in principle is applicable to any RL domain.
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Affiliation(s)
- Michael Dodds
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jeff Guo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Thomas Löhr
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Alessandro Tibo
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca 431 50 Gothenburg Sweden
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21
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Liu F, Kaplan AL, Levring J, Einsiedel J, Tiedt S, Distler K, Omattage NS, Kondratov IS, Moroz YS, Pietz HL, Irwin JJ, Gmeiner P, Shoichet BK, Chen J. Structure-based discovery of CFTR potentiators and inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.09.557002. [PMID: 37745391 PMCID: PMC10515777 DOI: 10.1101/2023.09.09.557002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, while its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify novel CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered novel mid-nanomolar potentiators as well as inhibitors that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.
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22
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Liu F, Wu CG, Tu CL, Glenn I, Meyerowitz J, Levit Kaplan A, Lyu J, Cheng Z, Tarkhanova OO, Moroz YS, Irwin JJ, Chang W, Shoichet BK, Skiniotis G. Small vs. Large Library Docking for Positive Allosteric Modulators of the Calcium Sensing Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.27.573448. [PMID: 38234749 PMCID: PMC10793424 DOI: 10.1101/2023.12.27.573448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Drugs acting as positive allosteric modulators (PAMs) to enhance the activation of the calcium sensing receptor (CaSR) and to suppress parathyroid hormone (PTH) secretion can treat hyperparathyroidism but suffer from side effects including hypocalcemia and arrhythmias. Seeking new CaSR modulators, we docked libraries of 2.7 million and 1.2 billion molecules against transforming pockets in the active-state receptor dimer structure. Consistent with simulations suggesting that docking improves with library size, billion-molecule docking found new PAMs with a hit rate that was 2.7-fold higher than the million-molecule library and with hits up to 37-fold more potent. Structure-based optimization of ligands from both campaigns led to nanomolar leads, one of which was advanced to animal testing. This PAM displays 100-fold the potency of the standard of care, cinacalcet, in ex vivo organ assays, and reduces serum PTH levels in mice by up to 80% without the hypocalcemia typical of CaSR drugs. Cryo-EM structures with the new PAMs show that they induce residue rearrangements in the binding pockets and promote CaSR dimer conformations that are closer to the G-protein coupled state compared to established drugs. These findings highlight the promise of large library docking for therapeutic leads, especially when combined with experimental structure determination and mechanism.
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Affiliation(s)
- Fangyu Liu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Cheng-Guo Wu
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chia-Ling Tu
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | - Isabella Glenn
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Justin Meyerowitz
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Anat Levit Kaplan
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Jiankun Lyu
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
- Current address: The Rockefeller University, New York, NY, 10065
| | - Zhiqiang Cheng
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | | | - Yurii S. Moroz
- Chemspace LLC, Kyiv, 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv, 01601, Ukraine
- Enamine Ltd., Kyiv, 02094, Ukraine
| | - John J. Irwin
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Wenhan Chang
- San Francisco VA Medical Center, Dept. of Medicine, University of California, San Francisco, San Francisco CA 94158, USA
| | - Brian K. Shoichet
- Dept. of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco CA 94143, USA
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA
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23
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Corso G, Deng A, Fry B, Polizzi N, Barzilay R, Jaakkola T. Deep Confident Steps to New Pockets: Strategies for Docking Generalization. ARXIV 2024:arXiv:2402.18396v1. [PMID: 38463508 PMCID: PMC10925391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Accurate blind docking has the potential to lead to new biological breakthroughs, but for this promise to be realized, docking methods must generalize well across the proteome. Existing benchmarks, however, fail to rigorously assess generalizability. Therefore, we develop DockGen, a new benchmark based on the ligand-binding domains of proteins, and we show that existing machine learning-based docking models have very weak generalization abilities. We carefully analyze the scaling laws of ML-based docking and show that, by scaling data and model size, as well as integrating synthetic data strategies, we are able to significantly increase the generalization capacity and set new state-of-the-art performance across benchmarks. Further, we propose Confidence Bootstrapping, a new training paradigm that solely relies on the interaction between diffusion and confidence models and exploits the multi-resolution generation process of diffusion models. We demonstrate that Confidence Bootstrapping significantly improves the ability of ML-based docking methods to dock to unseen protein classes, edging closer to accurate and generalizable blind docking methods.
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Affiliation(s)
| | | | - Benjamin Fry
- Dana-Farber Cancer Institute and Harvard Medical School
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24
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Tummino TA, Iliopoulos-Tsoutsouvas C, Braz JM, O'Brien ES, Stein RM, Craik V, Tran NK, Ganapathy S, Liu F, Shiimura Y, Tong F, Ho TC, Radchenko DS, Moroz YS, Rosado SR, Bhardwaj K, Benitez J, Liu Y, Kandasamy H, Normand C, Semache M, Sabbagh L, Glenn I, Irwin JJ, Kumar KK, Makriyannis A, Basbaum AI, Shoichet BK. Large library docking for cannabinoid-1 receptor agonists with reduced side effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.27.530254. [PMID: 38328157 PMCID: PMC10849508 DOI: 10.1101/2023.02.27.530254] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.
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25
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Cheng C, Beroza P. Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces. J Chem Inf Model 2024; 64:1251-1260. [PMID: 38335044 DOI: 10.1021/acs.jcim.3c01865] [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: 02/12/2024]
Abstract
Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.
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Affiliation(s)
- Chen Cheng
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
| | - Paul Beroza
- Discovery Chemistry, Genentech, South San Francisco, California 94080, United States
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26
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Yao H, Wang X, Chi J, Chen H, Liu Y, Yang J, Yu J, Ruan Y, Xiang X, Pi J, Xu JF. Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures. Molecules 2024; 29:964. [PMID: 38474476 DOI: 10.3390/molecules29050964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.
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Affiliation(s)
- Hanbo Yao
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Xiaodong Wang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaxin Chi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Haorong Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yilin Liu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiayi Yang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jiaqi Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Yongdui Ruan
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
| | - Xufu Xiang
- The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
| | - Jun-Fa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan 523808, China
- Institute of Laboratory Medicine, School of Medical Technology, Guangdong Medical University, Dongguan 523808, China
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27
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Knight IS, Mailhot O, Tang KG, Irwin JJ. DockOpt: A Tool for Automatic Optimization of Docking Models. J Chem Inf Model 2024; 64:1004-1016. [PMID: 38206771 PMCID: PMC10865354 DOI: 10.1021/acs.jcim.3c01406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 12/17/2023] [Accepted: 12/26/2023] [Indexed: 01/13/2024]
Abstract
Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.
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Affiliation(s)
- Ian S. Knight
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Olivier Mailhot
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - Khanh G. Tang
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, UCSF, 1700 Fourth Street, San Francisco, California 94158-2330, United States
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28
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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29
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Tandi M, Tripathi N, Gaur A, Gopal B, Sundriyal S. Curation and cheminformatics analysis of a Ugi-reaction derived library (URDL) of synthetically tractable small molecules for virtual screening application. Mol Divers 2024; 28:37-50. [PMID: 36574164 DOI: 10.1007/s11030-022-10588-1] [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: 10/11/2022] [Accepted: 12/17/2022] [Indexed: 12/28/2022]
Abstract
Virtual screening (VS) is an important approach in drug discovery and relies on the availability of a virtual library of synthetically tractable molecules. Ugi reaction (UR) represents an important multi-component reaction (MCR) that reliably produces a peptidomimetic scaffold. Recent literature shows that a tactically assembled Ugi adduct can be subjected to further chemical modifications to yield a variety of rings and scaffolds, thus, renewing the interest in this old reaction. Given the reliability and efficiency of UR, we collated an UR derived library (URDL) of small molecules (total = 5773) for VS. The synthesis of the majority of URDL molecules may be carried out in 1-2 pots in a time and cost-effective manner. The detailed analysis of the average property and chemical space of URDL was also carried out using the open-source Datawarrior program. The comparison with FDA-approved oral drugs and inhibitors of protein-protein interactions (iPPIs) suggests URDL molecules are 'clean', drug-like, and conform to a structurally distinct space from the other two categories. The average physicochemical properties of compounds in the URDL library lie closer to iPPI molecules than oral drugs thus suggesting that the URDL resource can be applied to discover novel iPPI molecules. The URDL molecules consist of diverse ring systems, many of which have not been exploited yet for drug design. Thus, URDL represents a small virtual library of drug-like molecules with unexplored chemical space designed for VS. The structures of all molecules of URDL, oral drugs, and iPPI compounds are being made freely accessible as supplementary information for broader application.
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Affiliation(s)
- Mukesh Tandi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Nancy Tripathi
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | - Animesh Gaur
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India
| | | | - Sandeep Sundriyal
- Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Rajasthan, 333031, India.
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30
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Sulimov AV, Ilin IS, Tashchilova AS, Kondakova OA, Kutov DC, Sulimov VB. Docking and other computing tools in drug design against SARS-CoV-2. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:91-136. [PMID: 38353209 DOI: 10.1080/1062936x.2024.2306336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.
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Affiliation(s)
- A V Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - I S Ilin
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - A S Tashchilova
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - O A Kondakova
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - D C Kutov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
| | - V B Sulimov
- Dimonta Ltd., Moscow, Russia
- Research Computing Center, Lomonosov Moscow State University, Moscow, Russia
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31
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Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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32
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Smith M, Knight IS, Kormos RC, Pepe JG, Kunach P, Diamond MI, Shahmoradian SH, Irwin JJ, DeGrado WF, Shoichet BK. Docking for Molecules That Bind in a Symmetric Stack with SymDOCK. J Chem Inf Model 2024; 64:425-434. [PMID: 38191997 PMCID: PMC10806807 DOI: 10.1021/acs.jcim.3c01749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 01/10/2024]
Abstract
Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of great current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures, the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the protein's symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligand's docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural-network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study, SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.
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Affiliation(s)
- Matthew
S. Smith
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Ian S. Knight
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
| | - Rian C. Kormos
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Joseph G. Pepe
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Program
in Biophysics, University of California, UCSF Genentech Hall MC2240, 600
16th St Rm N474D,San Francisco, California 94143, United States
| | - Peter Kunach
- McGill
Research Centre for Studies in Aging, McGill
University, 6875 Boulevard LaSalle, Montreal, Quebec H4H 1R3, Canada
- Department
of Neurology and Neurosurgery, McGill University, 1033 Pine Avenue West, Room 310, Montreal, Quebec H3A 1A1, Canada
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Neurology, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., G2.222, Dallas, Texas 75390-9368, United States
- Department
of Neuroscience, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-9111, United States
| | - Marc I. Diamond
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Neurology, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., G2.222, Dallas, Texas 75390-9368, United States
- Department
of Neuroscience, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-9111, United States
| | - Sarah H. Shahmoradian
- Center
for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell
Jr. Brain Institute, University of Texas
Southwestern Medical Center, 6124 Harry Hines Blvd. Suite NS03.200, Dallas, Texas 75390, United States
- Department
of Biophysics, University of Texas Southwestern
Medical Center, 5323 Harry Hines Blvd., Dallas, Texas 75390-8816, United States
| | - John J. Irwin
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
| | - William F. DeGrado
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
- Cardiovascular
Research Institute, University of California, 555 Mission Bay Blvd South, PO Box 589001, San Francisco, California 94158-9001, United
States
| | - Brian K. Shoichet
- Department
of Pharmaceutical Chemistry, University
of California, UCSF Genentech
Hall Box 2280, 600 16th St Rm 518,San Francisco, California 94158, United States
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33
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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [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: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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34
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Balius TE, Tan YS, Chakrabarti M. DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking. J Comput Chem 2024; 45:47-63. [PMID: 37743732 DOI: 10.1002/jcc.27218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/17/2023] [Indexed: 09/26/2023]
Abstract
To allow DOCK 6 access to unprecedented chemical space for screening billions of small molecules, we have implemented features from DOCK 3.7 into DOCK 6, including a search routine that traverses precomputed ligand conformations stored in a hierarchical database. We tested them on the DUDE-Z and SB2012 test sets. The hierarchical database search routine is 16 times faster than anchor-and-grow. However, the ability of hierarchical database search to reproduce the experimental pose is 16% worse than that of anchor-and-grow. The enrichment performance is on average similar, but DOCK 3.7 has better enrichment than DOCK 6, and DOCK 6 is on average 1.7 times slower. However, with post-docking torsion minimization, DOCK 6 surpasses DOCK 3.7. A large-scale virtual screen is performed with DOCK 6 on 23 million fragment molecules. We use current features in DOCK 6 to complement hierarchical database calculations, including torsion minimization, which is not available in DOCK 3.7.
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Affiliation(s)
- Trent E Balius
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Y Stanley Tan
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
| | - Mayukh Chakrabarti
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, Maryland, USA
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35
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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36
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Mohebbinia Z, Firouzi R, Karimi-Jafari MH. Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set. J Biomol Struct Dyn 2024:1-11. [PMID: 38165642 DOI: 10.1080/07391102.2023.2299742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 12/20/2023] [Indexed: 01/04/2024]
Abstract
Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Zainab Mohebbinia
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
| | - Rohoullah Firouzi
- Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran, Iran
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37
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Singh K, Bhushan B, Singh B. Advances in Drug Discovery and Design using Computer-aided Molecular Modeling. Curr Comput Aided Drug Des 2024; 20:697-710. [PMID: 37711101 DOI: 10.2174/1573409920666230914123005] [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: 07/04/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023]
Abstract
Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.
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Affiliation(s)
- Kuldeep Singh
- Department of Pharmacology, Rajiv Academy for Pharmacy, Mathura Uttar Pradesh, India
| | - Bharat Bhushan
- Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura Uttar Pradesh, India
| | - Bhoopendra Singh
- Department of Pharmacy, B.S.A. College of Engineering & Technology, Mathura Uttar Pradesh India
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38
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Popov KI, Wellnitz J, Maxfield T, Tropsha A. HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries. Mol Inform 2024; 43:e202300207. [PMID: 37802967 PMCID: PMC11156482 DOI: 10.1002/minf.202300207] [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: 08/16/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/08/2023]
Abstract
Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.
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Affiliation(s)
- Konstantin I. Popov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - James Wellnitz
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Travis Maxfield
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
- These authors contributed equally: Konstantin I. Popov, James Wellnitz, Travis Maxfield
| | - Alexander Tropsha
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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39
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Bonde B. Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery. Methods Mol Biol 2024; 2716:181-202. [PMID: 37702940 DOI: 10.1007/978-1-0716-3449-3_8] [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] [Indexed: 09/14/2023]
Abstract
The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (ML) and artificial intelligence (AI)-based tools for drug discovery, such as BERT, BARD, AlphaFold2, and GPT. This chapter attempts to overview types of software architectures for developing scientific software or application with deployment agnostic (on-prem to cloud and hybrid) use cases. Furthermore, the chapter aims to outline how the innovation is disrupting the orthodox mindset of monolithic software running on on-prem HPC and provide the paradigm shift landscape to microservices driven application programming (API) and message parsing interface (MPI)-based scientific computing across the distributed, high-available infrastructure. This is coupled with agile DevOps, and good coding practices, low code and no-code application development frameworks for cost-efficient, secure, automated, and robust scientific application life cycle management.
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Affiliation(s)
- Bhushan Bonde
- Evotec (UK) Ltd., Dorothy Crowfoot Hodgkin Campus, Abingdon, Oxfordshire, UK.
- Digital Futures Institute, University of Suffolk, Ipswich, United Kingdom.
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40
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Schimunek J, Seidl P, Elez K, Hempel T, Le T, Noé F, Olsson S, Raich L, Winter R, Gokcan H, Gusev F, Gutkin EM, Isayev O, Kurnikova MG, Narangoda CH, Zubatyuk R, Bosko IP, Furs KV, Karpenko AD, Kornoushenko YV, Shuldau M, Yushkevich A, Benabderrahmane MB, Bousquet-Melou P, Bureau R, Charton B, Cirou BC, Gil G, Allen WJ, Sirimulla S, Watowich S, Antonopoulos N, Epitropakis N, Krasoulis A, Itsikalis V, Theodorakis S, Kozlovskii I, Maliutin A, Medvedev A, Popov P, Zaretckii M, Eghbal-Zadeh H, Halmich C, Hochreiter S, Mayr A, Ruch P, Widrich M, Berenger F, Kumar A, Yamanishi Y, Zhang KYJ, Bengio E, Bengio Y, Jain MJ, Korablyov M, Liu CH, Marcou G, Glaab E, Barnsley K, Iyengar SM, Ondrechen MJ, Haupt VJ, Kaiser F, Schroeder M, Pugliese L, Albani S, Athanasiou C, Beccari A, Carloni P, D'Arrigo G, Gianquinto E, Goßen J, Hanke A, Joseph BP, Kokh DB, Kovachka S, Manelfi C, Mukherjee G, Muñiz-Chicharro A, Musiani F, Nunes-Alves A, Paiardi G, Rossetti G, Sadiq SK, Spyrakis F, Talarico C, Tsengenes A, Wade RC, Copeland C, Gaiser J, Olson DR, Roy A, Venkatraman V, Wheeler TJ, Arthanari H, Blaschitz K, Cespugli M, Durmaz V, Fackeldey K, Fischer PD, Gorgulla C, Gruber C, Gruber K, Hetmann M, Kinney JE, Padmanabha Das KM, Pandita S, Singh A, Steinkellner G, Tesseyre G, Wagner G, Wang ZF, Yust RJ, Druzhilovskiy DS, Filimonov DA, Pogodin PV, Poroikov V, Rudik AV, Stolbov LA, Veselovsky AV, De Rosa M, De Simone G, Gulotta MR, Lombino J, Mekni N, Perricone U, Casini A, Embree A, Gordon DB, Lei D, Pratt K, Voigt CA, Chen KY, Jacob Y, Krischuns T, Lafaye P, Zettor A, Rodríguez ML, White KM, Fearon D, Von Delft F, Walsh MA, Horvath D, Brooks CL, Falsafi B, Ford B, García-Sastre A, Yup Lee S, Naffakh N, Varnek A, Klambauer G, Hermans TM. A community effort in SARS-CoV-2 drug discovery. Mol Inform 2024; 43:e202300262. [PMID: 37833243 DOI: 10.1002/minf.202300262] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/13/2023] [Accepted: 10/13/2023] [Indexed: 10/15/2023]
Abstract
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
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41
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Panwar U, Murali A, Khan MA, Selvaraj C, Singh SK. Virtual Screening Process: A Guide in Modern Drug Designing. Methods Mol Biol 2024; 2714:21-31. [PMID: 37676591 DOI: 10.1007/978-1-0716-3441-7_2] [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] [Indexed: 09/08/2023]
Abstract
Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.
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Affiliation(s)
- Umesh Panwar
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Aarthy Murali
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Mohammad Aqueel Khan
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
| | - Chandrabose Selvaraj
- Center for Transdisciplinary Research, Department of Pharmacology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
| | - Sanjeev Kumar Singh
- Computer Aided Drug Design and Molecular Modelling Lab, Department of Bioinformatics, Science Block, Alagappa University, Karaikudi, Tamil Nadu, India
- Department of Data Sciences, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, Uttar Pradesh, India
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42
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Talevi A. Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects. Methods Mol Biol 2024; 2714:1-20. [PMID: 37676590 DOI: 10.1007/978-1-0716-3441-7_1] [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] [Indexed: 09/08/2023]
Abstract
Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.
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Affiliation(s)
- Alan Talevi
- Laboratory of Bioactive Compound Research and Development (LIDeB), Faculty of Exact Sciences, National University of La Plata (UNLP), La Plata, Argentina.
- Argentinean National Council of Scientific and Technical Research (CONICET), La Plata, Argentina.
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43
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [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: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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44
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Michino M, Beautrait A, Boyles NA, Nadupalli A, Dementiev A, Sun S, Ginn J, Baxt L, Suto R, Bryk R, Jerome SV, Huggins DJ, Vendome J. Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors. ACS BIO & MED CHEM AU 2023; 3:507-515. [PMID: 38144256 PMCID: PMC10739260 DOI: 10.1021/acsbiomedchemau.3c00046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 12/26/2023]
Abstract
Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.
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Affiliation(s)
- Mayako Michino
- Sanders
Tri-Institutional Therapeutics Discovery Institute, 1230 York Avenue, Box 122, New York, New York 10065, United States
| | - Alexandre Beautrait
- Schrödinger,
Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Nicholas A. Boyles
- Schrödinger,
Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Aparna Nadupalli
- Schrödinger,
Inc., 12 Michigan Dr., Natick, Massachusetts 01760, United States
| | - Alexey Dementiev
- Schrödinger,
Inc., 12 Michigan Dr., Natick, Massachusetts 01760, United States
| | - Shan Sun
- Sanders
Tri-Institutional Therapeutics Discovery Institute, 1230 York Avenue, Box 122, New York, New York 10065, United States
| | - John Ginn
- Sanders
Tri-Institutional Therapeutics Discovery Institute, 1230 York Avenue, Box 122, New York, New York 10065, United States
| | - Leigh Baxt
- Sanders
Tri-Institutional Therapeutics Discovery Institute, 1230 York Avenue, Box 122, New York, New York 10065, United States
| | - Robert Suto
- Schrödinger,
Inc., 12 Michigan Dr., Natick, Massachusetts 01760, United States
| | - Ruslana Bryk
- Department
of Microbiology and Immunology, Weill Cornell
Medicine, New York, New York 10065, United States
| | - Steven V. Jerome
- Schrödinger,
Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - David J. Huggins
- Sanders
Tri-Institutional Therapeutics Discovery Institute, 1230 York Avenue, Box 122, New York, New York 10065, United States
- Department
of Physiology and Biophysics, Weill Cornell
Medicine, New York, New York 10021, United States
| | - Jeremie Vendome
- Schrödinger,
Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
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45
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Wu Z, Zhang T, Ma X, Guo S, Zhou Q, Zahoor A, Deng G. Recent advances in anti-inflammatory active components and action mechanisms of natural medicines. Inflammopharmacology 2023; 31:2901-2937. [PMID: 37947913 DOI: 10.1007/s10787-023-01369-9] [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: 04/12/2023] [Accepted: 09/16/2023] [Indexed: 11/12/2023]
Abstract
Inflammation is a series of reactions caused by the body's resistance to external biological stimuli. Inflammation affects the occurrence and development of many diseases. Anti-inflammatory drugs have been used widely to treat inflammatory diseases, but long-term use can cause toxic side-effects and affect human functions. As immunomodulators with long-term conditioning effects and no drug residues, natural products are being investigated increasingly for the treatment of inflammatory diseases. In this review, we focus on the inflammatory process and cellular mechanisms in the development of diseases such as inflammatory bowel disease, atherosclerosis, and coronavirus disease-2019. Also, we focus on three signaling pathways (Nuclear factor-kappa B, p38 mitogen-activated protein kinase, Janus kinase/signal transducer and activator of transcription-3) to explain the anti-inflammatory effect of natural products. In addition, we also classified common natural products based on secondary metabolites and explained the association between current bidirectional prediction progress of natural product targets and inflammatory diseases.
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Affiliation(s)
- Zhimin Wu
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Tao Zhang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Xiaofei Ma
- College of Veterinary Medicine, Gansu Agriculture University, Lanzhou, China
| | - Shuai Guo
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Qingqing Zhou
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Arshad Zahoor
- College of Veterinary Sciences, The University of Agriculture Peshawar, Peshawar, Pakistan
| | - Ganzhen Deng
- Department of Clinical Veterinary Medicine, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
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46
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Parvatikar PP, Patil S, Khaparkhuntikar K, Patil S, Singh PK, Sahana R, Kulkarni RV, Raghu AV. Artificial intelligence: Machine learning approach for screening large database and drug discovery. Antiviral Res 2023; 220:105740. [PMID: 37935248 DOI: 10.1016/j.antiviral.2023.105740] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/17/2023] [Accepted: 10/26/2023] [Indexed: 11/09/2023]
Abstract
Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.
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Affiliation(s)
- Prachi P Parvatikar
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
| | - Sudha Patil
- Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Kedar Khaparkhuntikar
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - Shruti Patil
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India
| | - Pankaj K Singh
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, 500037, India
| | - R Sahana
- Department of Computer Science and Engineering, RV Institute of Technology and Management, 560076, Bengaluru, India
| | - Raghavendra V Kulkarni
- Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India; Department of Pharmaceutics, BLDEA's SSM College of Pharmacy and Research Centre, Vijayapur 586 103, Karnataka, India
| | - Anjanapura V Raghu
- Department of Science and Technology, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.
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47
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Reis J, Gorgulla C, Massari M, Marchese S, Valente S, Noce B, Basile L, Törner R, Cox H, Viennet T, Yang MH, Ronan MM, Rees MG, Roth JA, Capasso L, Nebbioso A, Altucci L, Mai A, Arthanari H, Mattevi A. Targeting ROS production through inhibition of NADPH oxidases. Nat Chem Biol 2023; 19:1540-1550. [PMID: 37884805 DOI: 10.1038/s41589-023-01457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023]
Abstract
NADPH oxidases (NOXs) are transmembrane enzymes that are devoted to the production of reactive oxygen species (ROS). In cancers, dysregulation of NOX enzymes affects ROS production, leading to redox unbalance and tumor progression. Consequently, NOXs are a drug target for cancer therapeutics, although current therapies have off-target effects: there is a need for isoenzyme-selective inhibitors. Here, we describe fully validated human NOX inhibitors, obtained from an in silico screen, targeting the active site of Cylindrospermum stagnale NOX5 (csNOX5). The hits are validated by in vitro and in cellulo enzymatic and binding assays, and their binding modes to the dehydrogenase domain of csNOX5 studied via high-resolution crystal structures. A high-throughput screen in a panel of cancer cells shows activity in selected cancer cell lines and synergistic effects with KRAS modulators. Our work lays the foundation for the development of inhibitor-based methods for controlling the tightly regulated and highly localized ROS sources.
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Affiliation(s)
- Joana Reis
- Department of Biology and Biotechnology Lazzaro Spallanzani, University of Pavia, Pavia, Italy
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christoph Gorgulla
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Physics, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Marta Massari
- Department of Biology and Biotechnology Lazzaro Spallanzani, University of Pavia, Pavia, Italy
| | - Sara Marchese
- Department of Biology and Biotechnology Lazzaro Spallanzani, University of Pavia, Pavia, Italy
| | - Sergio Valente
- Department of Drug Chemistry and Technologies, Sapienza University of Rome, Rome, Italy
| | - Beatrice Noce
- Department of Drug Chemistry and Technologies, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Basile
- Department of Biology and Biotechnology Lazzaro Spallanzani, University of Pavia, Pavia, Italy
| | - Ricarda Törner
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Huel Cox
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thibault Viennet
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Moon Hee Yang
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | | | - Lucia Capasso
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Angela Nebbioso
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Lucia Altucci
- Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Antonello Mai
- Department of Drug Chemistry and Technologies, Sapienza University of Rome, Rome, Italy.
| | - Haribabu Arthanari
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Andrea Mattevi
- Department of Biology and Biotechnology Lazzaro Spallanzani, University of Pavia, Pavia, Italy.
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48
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Ahmed F, Brooks CL. FASTDock: A Pipeline for Allosteric Drug Discovery. J Chem Inf Model 2023; 63:7219-7227. [PMID: 37939386 PMCID: PMC10773972 DOI: 10.1021/acs.jcim.3c00895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.
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Affiliation(s)
- Furyal Ahmed
- Biophysics Program, University of Michigan, Ann Arbor, MI 48103
| | - Charles L. Brooks
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, MI 48103
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49
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Wang J, Xiao Y, Shang X, Peng J. Predicting drug-target binding affinity with cross-scale graph contrastive learning. Brief Bioinform 2023; 25:bbad516. [PMID: 38221904 PMCID: PMC10788681 DOI: 10.1093/bib/bbad516] [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: 07/03/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 01/16/2024] Open
Abstract
Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.
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Affiliation(s)
- Jingru Wang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
| | - Yihang Xiao
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
- Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518000, China
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50
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Xia S, Chen E, Zhang Y. Integrated Molecular Modeling and Machine Learning for Drug Design. J Chem Theory Comput 2023; 19:7478-7495. [PMID: 37883810 PMCID: PMC10653122 DOI: 10.1021/acs.jctc.3c00814] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.
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Affiliation(s)
- Song Xia
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Eric Chen
- Department
of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department
of Chemistry, New York University, New York, New York 10003, United States
- Simons
Center for Computational Physical Chemistry at New York University, New York, New York 10003, United States
- NYU-ECNU
Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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