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Dorogan M, Namballa HK, Harding WW. Natural Product-Inspired Dopamine Receptor Ligands. J Med Chem 2024; 67:12463-12484. [PMID: 39038276 PMCID: PMC11320586 DOI: 10.1021/acs.jmedchem.4c00537] [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: 03/03/2024] [Revised: 06/30/2024] [Accepted: 07/11/2024] [Indexed: 07/24/2024]
Abstract
Due to their evolutionary bias as ligands for biologically relevant drug targets, natural products offer a unique opportunity as lead compounds in drug discovery. Given the involvement of dopamine receptors in various physiological and behavioral functions, they are linked to numerous diseases and disorders such as Parkinson's disease, schizophrenia, and substance use disorders. Consequently, ligands targeting dopamine receptors hold considerable therapeutic and investigative promise. As this perspective will highlight, dopamine receptor targeting natural products play a pivotal role as scaffolds with unique and beneficial pharmacological properties, allowing for natural product-inspired drug design and lead optimization. As such, dopamine receptor targeting natural products still have untapped potential to aid in the treatment of disorders and diseases related to central nervous system (CNS) and peripheral nervous system (PNS) dysfunction.
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Affiliation(s)
- Michael Dorogan
- Department
of Chemistry, Hunter College, City University
of New York, 695 Park
Avenue, New York, New York 10065, United States
| | - Hari K. Namballa
- Department
of Chemistry, Hunter College, City University
of New York, 695 Park
Avenue, New York, New York 10065, United States
| | - Wayne W. Harding
- Department
of Chemistry, Hunter College, City University
of New York, 695 Park
Avenue, New York, New York 10065, United States
- Program
in Biochemistry, CUNY Graduate Center, 365 Fifth Avenue, New York, New York 10016, United States
- Program
in Chemistry, CUNY Graduate Center, 365 Fifth Avenue, New York, New York 10016, United
States
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2
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Wang M, Wang J, Rong Z, Wang L, Xu Z, Zhang L, He J, Li S, Cao L, Hou Y, Li K. A bidirectional interpretable compound-protein interaction prediction framework based on cross attention. Comput Biol Med 2024; 172:108239. [PMID: 38460309 DOI: 10.1016/j.compbiomed.2024.108239] [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: 08/31/2023] [Revised: 02/25/2024] [Accepted: 02/26/2024] [Indexed: 03/11/2024]
Abstract
The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.
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Affiliation(s)
- Meng Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jianmin Wang
- School of Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon, 21983, Republic of Korea
| | - Zhiwei Rong
- School of Public Health, Peking University, Beijing, 100871, China
| | - Liuying Wang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Zhenyi Xu
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Liuchao Zhang
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Jia He
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Shuang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Lei Cao
- School of Public Health, Harbin Medical University, Harbin, 150081, China
| | - Yan Hou
- School of Public Health, Peking University, Beijing, 100871, China
| | - Kang Li
- School of Public Health, Harbin Medical University, Harbin, 150081, China.
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Ren Q, Qu N, Sun J, Zhou J, Liu J, Ni L, Tong X, Zhang Z, Kong X, Wen Y, Wang Y, Wang D, Luo X, Zhang S, Zheng M, Li X. KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling. Brief Bioinform 2023; 25:bbad461. [PMID: 38113075 PMCID: PMC10729787 DOI: 10.1093/bib/bbad461] [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: 08/27/2023] [Revised: 11/08/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.
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Affiliation(s)
- Qun Ren
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Jingjing Sun
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Jingyi Zhou
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Lingang Laboratory, Shanghai 200031, China
| | - Jin Liu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Lin Ni
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Zimei Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Yiming Wen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Yitian Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Dingyan Wang
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou 330106, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Sulin Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
- Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing 210023, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
- University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049, China
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4
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Feifei W, Wenrou S, Sining K, Siyu Z, Xiaolei F, Junxiang L, Congfen H, Xuhui L. A novel functional peptide, named EQ-9 (ESETRILLQ), identified by virtual screening from regenerative cell secretome and its potential anti-aging and restoration effects in topical applications. Peptides 2023; 169:171078. [PMID: 37579838 DOI: 10.1016/j.peptides.2023.171078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023]
Abstract
Skin aging refers to a degenerative process that can be affected and regulated by intrinsic and extrinsic factors. The mesenchymal stem cell secretome covers a considerable number of regenerative molecules with anti-aging effects in a wide variety of circumstances. However, it is complex, time-consuming, and costly to identify specific compounds from thousands of natural molecules using conventional methods. With the development of computational biology and machine learning, an efficient workflow was generated to identify novel peptides with anti-aging and skin restoration potential. One of the candidate peptides was discovered and subsequently truncated to a novel peptide named EQ-9, with promising anti-aging effects for topical applications at a concentration of 10 ppm validated by experimental validation. The above-described paradigm is expected to be further applied to the virtual screening of novel peptide molecules targeting specific biological functions from a wide variety of natural resources.
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Affiliation(s)
- Wang Feifei
- Yunnan Botanee Bio-technology Group Co., Ltd., Yunnan, China; Yunnan Yunke Characteristic Plant Extraction Laboratory Co., Ltd., Yunnan, China
| | - Su Wenrou
- Yunnan Botanee Bio-technology Group Co., Ltd., Yunnan, China; Yunnan Yunke Characteristic Plant Extraction Laboratory Co., Ltd., Yunnan, China
| | - Kang Sining
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China; Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Zhu Siyu
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China; Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Fu Xiaolei
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China; Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - Li Junxiang
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China; Harvest Biotech (Zhejiang) Co., Ltd., Zhejiang, China
| | - He Congfen
- Beijing Technology and Business University, Beijing Key Lab of Plant Resources Research and Development, Beijing, China
| | - Li Xuhui
- AGECODE R&D Center, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China; Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, Zhejiang, China.
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