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Zhou R, Fan J, Li S, Zeng W, Chen Y, Zheng X, Chen H, Liao J. LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification. J Cheminform 2024; 16:79. [PMID: 38972994 PMCID: PMC11229186 DOI: 10.1186/s13321-024-00871-8] [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: 12/08/2023] [Accepted: 06/12/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND Previous deep learning methods for predicting protein binding pockets mainly employed 3D convolution, yet an abundance of convolution operations may lead the model to excessively prioritize local information, thus overlooking global information. Moreover, it is essential for us to account for the influence of diverse protein folding structural classes. Because proteins classified differently structurally exhibit varying biological functions, whereas those within the same structural class share similar functional attributes. RESULTS We proposed LVPocket, a novel method that synergistically captures both local and global information of protein structure through the integration of Transformer encoders, which help the model achieve better performance in binding pockets prediction. And then we tailored prediction models for data of four distinct structural classes of proteins using the transfer learning. The four fine-tuned models were trained on the baseline LVPocket model which was trained on the sc-PDB dataset. LVPocket exhibits superior performance on three independent datasets compared to current state-of-the-art methods. Additionally, the fine-tuned model outperforms the baseline model in terms of performance. SCIENTIFIC CONTRIBUTION We present a novel model structure for predicting protein binding pockets that provides a solution for relying on extensive convolutional computation while neglecting global information about protein structures. Furthermore, we tackle the impact of different protein folding structures on binding pocket prediction tasks through the application of transfer learning methods.
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Affiliation(s)
- Ruifeng Zhou
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Jing Fan
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Sishu Li
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Wenjie Zeng
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Yilun Chen
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Xiaoshan Zheng
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China
| | - Hongyang Chen
- Research Center for Graph Computing, Zhejiang Lab, Hangzhou, 311121, Zhejiang, People's Republic of China.
| | - Jun Liao
- School of Science, China Pharmaceutical University, Nanjing, 210009, Jiangsu, People's Republic of China.
- Zhejiang Lab, Hangzhou, 311121, Zhejiang, People's Republic of China.
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2
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He X, Zhao L, Tian Y, Li R, Chu Q, Gu Z, Zheng M, Wang Y, Li S, Jiang H, Jiang Y, Wen L, Wang D, Cheng X. Highly accurate carbohydrate-binding site prediction with DeepGlycanSite. Nat Commun 2024; 15:5163. [PMID: 38886381 PMCID: PMC11183243 DOI: 10.1038/s41467-024-49516-2] [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: 11/27/2023] [Accepted: 06/10/2024] [Indexed: 06/20/2024] Open
Abstract
As the most abundant organic substances in nature, carbohydrates are essential for life. Understanding how carbohydrates regulate proteins in the physiological and pathological processes presents opportunities to address crucial biological problems and develop new therapeutics. However, the diversity and complexity of carbohydrates pose a challenge in experimentally identifying the sites where carbohydrates bind to and act on proteins. Here, we introduce a deep learning model, DeepGlycanSite, capable of accurately predicting carbohydrate-binding sites on a given protein structure. Incorporating geometric and evolutionary features of proteins into a deep equivariant graph neural network with the transformer architecture, DeepGlycanSite remarkably outperforms previous state-of-the-art methods and effectively predicts binding sites for diverse carbohydrates. Integrating with a mutagenesis study, DeepGlycanSite reveals the guanosine-5'-diphosphate-sugar-recognition site of an important G-protein coupled receptor. These findings demonstrate DeepGlycanSite is invaluable for carbohydrate-binding site prediction and could provide insights into molecular mechanisms underlying carbohydrate-regulation of therapeutically important proteins.
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Affiliation(s)
- Xinheng He
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lifen Zhao
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Yinping Tian
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Rui Li
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Zhiyong Gu
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Mingyue Zheng
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
| | - Yusong Wang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Shaoning Li
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China
- Lingang Laboratory, Shanghai, China
| | - Yi Jiang
- Lingang Laboratory, Shanghai, China
| | - Liuqing Wen
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | | | - Xi Cheng
- State Key Laboratory of Drug Research and State Key Laboratory of Chemical Biology, Carbohydrate-Based Drug Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
- University of Chinese Academy of Sciences, Beijing, China.
- School of Pharmaceutical Science and Technology, Hangzhou Institute of Advanced Study, Hangzhou, China.
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3
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Xia Y, Pan X, Shen HB. A comprehensive survey on protein-ligand binding site prediction. Curr Opin Struct Biol 2024; 86:102793. [PMID: 38447285 DOI: 10.1016/j.sbi.2024.102793] [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: 12/20/2023] [Revised: 02/18/2024] [Accepted: 02/18/2024] [Indexed: 03/08/2024]
Abstract
Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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4
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Takahashi M, Chong HB, Zhang S, Yang TY, Lazarov MJ, Harry S, Maynard M, Hilbert B, White RD, Murrey HE, Tsou CC, Vordermark K, Assaad J, Gohar M, Dürr BR, Richter M, Patel H, Kryukov G, Brooijmans N, Alghali ASO, Rubio K, Villanueva A, Zhang J, Ge M, Makram F, Griesshaber H, Harrison D, Koglin AS, Ojeda S, Karakyriakou B, Healy A, Popoola G, Rachmin I, Khandelwal N, Neil JR, Tien PC, Chen N, Hosp T, van den Ouweland S, Hara T, Bussema L, Dong R, Shi L, Rasmussen MQ, Domingues AC, Lawless A, Fang J, Yoda S, Nguyen LP, Reeves SM, Wakefield FN, Acker A, Clark SE, Dubash T, Kastanos J, Oh E, Fisher DE, Maheswaran S, Haber DA, Boland GM, Sade-Feldman M, Jenkins RW, Hata AN, Bardeesy NM, Suvà ML, Martin BR, Liau BB, Ott CJ, Rivera MN, Lawrence MS, Bar-Peled L. DrugMap: A quantitative pan-cancer analysis of cysteine ligandability. Cell 2024; 187:2536-2556.e30. [PMID: 38653237 PMCID: PMC11143475 DOI: 10.1016/j.cell.2024.03.027] [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: 10/01/2023] [Revised: 01/15/2024] [Accepted: 03/19/2024] [Indexed: 04/25/2024]
Abstract
Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.
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Affiliation(s)
- Mariko Takahashi
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA.
| | - Harrison B Chong
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Siwen Zhang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Tzu-Yi Yang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Matthew J Lazarov
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Stefan Harry
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | | | | | | | | | | | - Kira Vordermark
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Jonathan Assaad
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Magdy Gohar
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Benedikt R Dürr
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Marianne Richter
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Himani Patel
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | | | | | | | - Karla Rubio
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Antonio Villanueva
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Junbing Zhang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Maolin Ge
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Farah Makram
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Hanna Griesshaber
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Drew Harrison
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Ann-Sophie Koglin
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Samuel Ojeda
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Barbara Karakyriakou
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Alexander Healy
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - George Popoola
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Inbal Rachmin
- Cutaneous Biology Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Neha Khandelwal
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | | | - Pei-Chieh Tien
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Nicholas Chen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | - Tobias Hosp
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sanne van den Ouweland
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Toshiro Hara
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lillian Bussema
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Rui Dong
- Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lei Shi
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Martin Q Rasmussen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Ana Carolina Domingues
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Aleigha Lawless
- Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jacy Fang
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Satoshi Yoda
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Linh Phuong Nguyen
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sarah Marie Reeves
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Farrah Nicole Wakefield
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Adam Acker
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Sarah Elizabeth Clark
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Taronish Dubash
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - John Kastanos
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA
| | - Eugene Oh
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - David E Fisher
- Cutaneous Biology Research Center, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Shyamala Maheswaran
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Daniel A Haber
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Genevieve M Boland
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Surgery, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Surgery, Harvard Medical School, Boston, MA 02114, USA
| | - Moshe Sade-Feldman
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Russell W Jenkins
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Aaron N Hata
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Nabeel M Bardeesy
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Mario L Suvà
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | | | - Brian B Liau
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Christopher J Ott
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Miguel N Rivera
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA
| | - Michael S Lawrence
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pathology, Harvard Medical School, Boston, MA 02114, USA.
| | - Liron Bar-Peled
- Krantz Family Center for Cancer Research, Massachusetts General Hospital Cancer Center, Charlestown, MA 02129, USA; Department of Medicine, Harvard Medical School, Boston, MA 02114, USA.
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Yang K, Xie Z, Li Z, Qian X, Sun N, He T, Xu Z, Jiang J, Mei Q, Wang J, Qu S, Xu X, Chen C, Ju B. MolProphet: A One-Stop, General Purpose, and AI-Based Platform for the Early Stages of Drug Discovery. J Chem Inf Model 2024; 64:2941-2947. [PMID: 38563534 DOI: 10.1021/acs.jcim.3c01979] [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: 04/04/2024]
Abstract
Artificial intelligence (AI) is an effective tool to accelerate drug discovery and cut costs in discovery processes. Many successful AI applications are reported in the early stages of small molecule drug discovery. However, most of those applications require a deep understanding of software and hardware, and focus on a single field that implies data normalization and transfer between those applications is still a challenge for normal users. It usually limits the application of AI in drug discovery. Here, based on a series of robust models, we formed a one-stop, general purpose, and AI-based drug discovery platform, MolProphet, to provide complete functionalities in the early stages of small molecule drug discovery, including AI-based target pocket prediction, hit discovery and lead optimization, and compound targeting, as well as abundant analyzing tools to check the results. MolProphet is an accessible and user-friendly web-based platform that is fully designed according to the practices in the drug discovery industry. The molecule screened, generated, or optimized by the MolProphet is purchasable and synthesizable at low cost but with good drug-likeness. More than 400 users from industry and academia have used MolProphet in their work. We hope this platform can provide a powerful solution to assist each normal researcher in drug design and related research areas. It is available for everyone at https://www.molprophet.com/.
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Affiliation(s)
- Keda Yang
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Zewen Xie
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Zhen Li
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Xiaoliang Qian
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Nannan Sun
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Tao He
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Zuodong Xu
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Jing Jiang
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Qi Mei
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Jie Wang
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Shugang Qu
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
| | - Xiaoling Xu
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Chaoxiang Chen
- Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Shulan International Medical College, Zhejiang Shuren University, Hangzhou 310015, P. R. China
| | - Bin Ju
- Hangzhou SanOmics Information Technology Co., Ltd., Hangzhou 310015, P. R. China
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6
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Smith Z, Strobel M, Vani BP, Tiwary P. Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention. J Chem Inf Model 2024; 64:2637-2644. [PMID: 38453912 PMCID: PMC11182664 DOI: 10.1021/acs.jcim.3c01698] [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: 03/09/2024]
Abstract
Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.
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Affiliation(s)
- Zachary Smith
- Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
- Biophysics Program, University of Maryland, College Park 20742, USA
| | - Michael Strobel
- Department of Computer Science, University of Maryland, College Park 20742, USA
| | - Bodhi P. Vani
- Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
| | - Pratyush Tiwary
- Institute for Physical Science and Technology, University of Maryland, College Park 20742, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park 20742, USA
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7
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Kumar N, Srivastava R. Deep learning in structural bioinformatics: current applications and future perspectives. Brief Bioinform 2024; 25:bbae042. [PMID: 38701422 PMCID: PMC11066934 DOI: 10.1093/bib/bbae042] [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: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 05/05/2024] Open
Abstract
In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.
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Affiliation(s)
- Niranjan Kumar
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
| | - Rakesh Srivastava
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India
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8
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Carbery A, Buttenschoen M, Skyner R, von Delft F, Deane CM. Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. J Cheminform 2024; 16:32. [PMID: 38486231 PMCID: PMC10941399 DOI: 10.1186/s13321-024-00821-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/01/2024] [Indexed: 03/17/2024] Open
Abstract
Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.
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Affiliation(s)
- Anna Carbery
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
| | - Martin Buttenschoen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK
| | - Rachael Skyner
- OMass Therapeutics, Building 4000, Chancellor Court, John Smith Drive, ARC Oxford, OX4 2GX, UK
| | - Frank von Delft
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, OX11 0DE, UK
- Centre for Medicines Discovery, University of Oxford, Oxford, OX3 7DQ, UK
- Research Complex at Harwell, Harwell Science and Innovation Campus, Didcot, OX11 0FA, United Kingdom
- Department of Biochemistry, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, OX1 3LB, UK.
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9
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Korlepara DB, C S V, Srivastava R, Pal PK, Raza SH, Kumar V, Pandit S, Nair AG, Pandey S, Sharma S, Jeurkar S, Thakran K, Jaglan R, Verma S, Ramachandran I, Chatterjee P, Nayar D, Priyakumar UD. PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications. Sci Data 2024; 11:180. [PMID: 38336857 PMCID: PMC10858175 DOI: 10.1038/s41597-023-02872-y] [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: 07/20/2023] [Accepted: 12/21/2023] [Indexed: 02/12/2024] Open
Abstract
Computing binding affinities is of great importance in drug discovery pipeline and its prediction using advanced machine learning methods still remains a major challenge as the existing datasets and models do not consider the dynamic features of protein-ligand interactions. To this end, we have developed PLAS-20k dataset, an extension of previously developed PLAS-5k, with 97,500 independent simulations on a total of 19,500 different protein-ligand complexes. Our results show good correlation with the available experimental values, performing better than docking scores. This holds true even for a subset of ligands that follows Lipinski's rule, and for diverse clusters of complex structures, thereby highlighting the importance of PLAS-20k dataset in developing new ML models. Along with this, our dataset is also beneficial in classifying strong and weak binders compared to docking. Further, OnionNet model has been retrained on PLAS-20k dataset and is provided as a baseline for the prediction of binding affinities. We believe that large-scale MD-based datasets along with trajectories will form new synergy, paving the way for accelerating drug discovery.
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Affiliation(s)
- Divya B Korlepara
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Divison of Physics, School of Advanced Sciences, Vellore Institute of Technology, Chennai, 600127, India
| | - Vasavi C S
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
- Department of Artificial Intelligence, School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, 560035, India
| | - Rakesh Srivastava
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Pradeep Kumar Pal
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Saalim H Raza
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Vishal Kumar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivam Pandit
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Aathira G Nair
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Sanjana Pandey
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shubham Sharma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shruti Jeurkar
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Kavita Thakran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Reena Jaglan
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Shivangi Verma
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Indhu Ramachandran
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Prathit Chatterjee
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India
| | - Divya Nayar
- Department of Materials Science and Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - U Deva Priyakumar
- IHub-Data, International Institute of Information Technology, Hyderabad, 500032, India.
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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10
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Popov P, Kalinin R, Buslaev P, Kozlovskii I, Zaretckii M, Karlov D, Gabibov A, Stepanov A. Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites. Brief Bioinform 2023; 25:bbad459. [PMID: 38113077 PMCID: PMC10783863 DOI: 10.1093/bib/bbad459] [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: 08/07/2023] [Revised: 11/10/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promising solution by efficiently reducing the number of candidates. In this study, we propose a structure- and deep learning-based approach that identifies vulnerable regions in viral proteins corresponding to drug binding sites. Our approach takes into account the protein dynamics, accessibility and mutability of the binding site and the putative mechanism of action of the drug. We applied this technique to validate drug targeting toward severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein S. Our findings reveal a conformation- and oligomer-specific glycan-free binding site proximal to the receptor binding domain. This site comprises topologically important amino acid residues. Molecular dynamics simulations of Spike in complex with candidate drug molecules bound to the potential binding sites indicate an equilibrium shifted toward the inactive conformation compared with drug-free simulations. Small molecules targeting this binding site have the potential to prevent the closed-to-open conformational transition of Spike, thereby allosterically inhibiting its interaction with human angiotensin-converting enzyme 2 receptor. Using a pseudotyped virus-based assay with a SARS-CoV-2 neutralizing antibody, we identified a set of hit compounds that exhibited inhibition at micromolar concentrations.
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Affiliation(s)
- Petr Popov
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Roman Kalinin
- M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997, Russia
| | - Pavel Buslaev
- Nanoscience Center and Department of Chemistry, University of Jyväskylä, 40014, Jyväskylä, Finland
| | - Igor Kozlovskii
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Mark Zaretckii
- Tetra-d, Rheinweg 9, Schaffhausen, 8200, Switzerland
- School of Science, Constructor University Bremen gGmbH, 28759, Bremen, Germany
| | - Dmitry Karlov
- School of Pharmacy, Medical Biology Centre, Queen’s University Belfast, Street, Belfast, BT9 7BL Northern Ireland, U.K
| | - Alexander Gabibov
- M.M. Shemyakin and Yu.A. Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, 117997, Russia
| | - Alexey Stepanov
- Department of Chemistry, The Scripps Research Institute, 10550 North Torrey Pines Road MB-10, La Jolla, 92037, CA, USA
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11
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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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12
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Takahashi M, Chong HB, Zhang S, Lazarov MJ, Harry S, Maynard M, White R, Murrey HE, Hilbert B, Neil JR, Gohar M, Ge M, Zhang J, Durr BR, Kryukov G, Tsou CC, Brooijmans N, Alghali ASO, Rubio K, Vilanueva A, Harrison D, Koglin AS, Ojeda S, Karakyriakou B, Healy A, Assaad J, Makram F, Rachman I, Khandelwal N, Tien PC, Popoola G, Chen N, Vordermark K, Richter M, Patel H, Yang TY, Griesshaber H, Hosp T, van den Ouweland S, Hara T, Bussema L, Dong R, Shi L, Rasmussen MQ, Domingues AC, Lawless A, Fang J, Yoda S, Nguyen LP, Reeves SM, Wakefield FN, Acker A, Clark SE, Dubash T, Fisher DE, Maheswaran S, Haber DA, Boland G, Sade-Feldman M, Jenkins R, Hata A, Bardeesy N, Suva ML, Martin B, Liau B, Ott C, Rivera MN, Lawrence MS, Bar-Peled L. DrugMap: A quantitative pan-cancer analysis of cysteine ligandability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563287. [PMID: 37961514 PMCID: PMC10634688 DOI: 10.1101/2023.10.20.563287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors of a wide-range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed DrugMap , an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NFκB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NFκB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription factor activity.
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13
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Liu Y, Li P, Tu S, Xu L. RefinePocket: An Attention-Enhanced and Mask-Guided Deep Learning Approach for Protein Binding Site Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3314-3321. [PMID: 37040253 DOI: 10.1109/tcbb.2023.3265640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Protein binding site prediction is an important prerequisite task of drug discovery and design. While binding sites are very small, irregular and varied in shape, making the prediction very challenging. Standard 3D U-Net has been adopted to predict binding sites but got stuck with unsatisfactory prediction results, incomplete, out-of-bounds, or even failed. The reason is that this scheme is less capable of extracting the chemical interactions of the entire region and hardly takes into account the difficulty of segmenting complex shapes. In this paper, we propose a refined U-Net architecture, called RefinePocket, consisting of an attention-enhanced encoder and a mask-guided decoder. During encoding, taking binding site proposal as input, we employ Dual Attention Block (DAB) hierarchically to capture rich global information, exploring residue relationship and chemical correlations in spatial and channel dimensions respectively. Then, based on the enhanced representation extracted by the encoder, we devise Refine Block (RB) in the decoder to enable self-guided refinement of uncertain regions gradually, resulting in more precise segmentation. Experiments show that DAB and RB complement and promote each other, making RefinePocket has an average improvement of 10.02% on DCC and 4.26% on DVO compared with the state-of-the-art method on four test sets.
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14
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Lei C, Lu Z, Wang M, Li M. StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features. Comput Biol Med 2023; 164:107131. [PMID: 37494820 DOI: 10.1016/j.compbiomed.2023.107131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/10/2023] [Accepted: 06/01/2023] [Indexed: 07/28/2023]
Abstract
Accurately predicting compound-protein binding affinity is a crucial task in drug discovery. Computational models offer the advantages of short time, low cost and safety compared to traditional drug development. Pocket is the key binding region of the protein, which provides invaluable information for drug repositioning and drug design. In this study, we propose an ensemble learning model, called StackCPA, to predict the compound-protein binding affinity. The model integrates multi-scale features of protein pocket and compound through a transfer learning strategy. The protein pocket is described in a fine-grained way by atomic level, residue level and subdomain level. The proposed model StackCPA is evaluated on three binding affinity benchmark datasets. The experiment results show that StackCPA achieves the best performance on all the three datasets in comparison with other state-of-the-art deep learning models. The ablation study shows that the protein pocket can provide sufficient information for affinity prediction and its multi-scale features enable the model to further improve the prediction performance. In addition, the case study for epidermal growth factor receptor erbB1 (EGFR) indicates that StackCPA could serve as an effective tool for drug repurposing. Source codes and data of StackCPA are available at https://github.com/CSUBioGroup/StackCPA.
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Affiliation(s)
- Chuqi Lei
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Zhangli Lu
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Meng Wang
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China.
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15
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Hagg A, Kirschner KN. Open-Source Machine Learning in Computational Chemistry. J Chem Inf Model 2023; 63:4505-4532. [PMID: 37466636 PMCID: PMC10430767 DOI: 10.1021/acs.jcim.3c00643] [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: 05/04/2023] [Indexed: 07/20/2023]
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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Affiliation(s)
- Alexander Hagg
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Electrical Engineering, Mechanical Engineering and Technical Journalism, University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
| | - Karl N. Kirschner
- Institute
of Technology, Resource and Energy-Efficient Engineering (TREE), University of Applied Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
- Department
of Computer Science, University of Applied
Sciences Bonn-Rhein-Sieg, 53757 Sankt Augustin, Germany
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16
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Xia Y, Pan X, Shen HB. LigBind: identifying binding residues for over 1000 ligands with relation-aware graph neural networks. J Mol Biol 2023; 435:168091. [PMID: 37054909 DOI: 10.1016/j.jmb.2023.168091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/22/2023] [Accepted: 04/05/2023] [Indexed: 04/15/2023]
Abstract
Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind's efficacy on the large-scale ligand-specific benchmark datasets, and generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The webserver and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
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17
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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18
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Petrovski ŽH, Hribar-Lee B, Bosnić Z. CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network. Pharmaceutics 2022; 15:pharmaceutics15010119. [PMID: 36678749 PMCID: PMC9862895 DOI: 10.3390/pharmaceutics15010119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/18/2022] [Accepted: 12/22/2022] [Indexed: 01/01/2023] Open
Abstract
Identifying binding sites on the protein surface is an important part of computer-assisted drug design processes. Reliable prediction of binding sites not only assists with docking algorithms, but it can also explain the possible side-effects of a potential drug as well as its efficiency. In this work, we propose a novel workflow for predicting possible binding sites of a ligand on a protein surface. We use proteins from the PDBbind and sc-PDB databases, from which we combine available ligand information for similar proteins using all the possible ligands rather than only a special sub-selection to generalize the work of existing research. After performing protein clustering and merging of ligands of similar proteins, we use a three-dimensional convolutional neural network that takes into account the spatial structure of a protein. Lastly, we combine ligandability predictions for points on protein surfaces into joint binding sites. Analysis of our model's performance shows that its achieved sensitivity is 0.829, specificity is 0.98, and F1 score is 0.517, and that for 54% of larger and pharmacologically relevant binding sites, the distance between their real and predicted centers amounts to less than 4 Å.
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Affiliation(s)
- Žan Hafner Petrovski
- University of Ljubljana, Faculty of Computer and Information Science, SI-1000 Ljubljana, Slovenia
| | - Barbara Hribar-Lee
- University of Ljubljana, Faculty of Chemistry and Chemical Technology, SI-1000 Ljubljana, Slovenia
- Correspondence: (B.-H.L.); (Z.B.)
| | - Zoran Bosnić
- University of Ljubljana, Faculty of Computer and Information Science, SI-1000 Ljubljana, Slovenia
- Correspondence: (B.-H.L.); (Z.B.)
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19
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Xia Y, Xia C, Pan X, Shen H. BindWeb: A web server for ligand binding residue and pocket prediction from protein structures. Protein Sci 2022; 31:e4462. [PMID: 36190332 PMCID: PMC9667820 DOI: 10.1002/pro.4462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/27/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022]
Abstract
Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains challenging. In this study, we introduce an easy-to-use web server BindWeb for ligand-specific and ligand-general binding residue and pocket prediction from protein structures. BindWeb integrates a graph neural network GraphBind with a hybrid convolutional neural network and bidirectional long short-term memory network DELIA to identify binding residues. Furthermore, BindWeb clusters the predicted binding residues to binding pockets with mean shift clustering. The experimental results and case study demonstrate that BindWeb benefits from the complementarity of two base methods. BindWeb is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/BindWeb/.
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Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of ChinaShanghaiChina
| | - Chunqiu Xia
- Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of ChinaShanghaiChina
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of ChinaShanghaiChina
| | - Hong‐Bin Shen
- Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of ChinaShanghaiChina
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20
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Zhang J, Li H, Zhao X, Wu Q, Huang SY. Holo Protein Conformation Generation from Apo Structures by Ligand Binding Site Refinement. J Chem Inf Model 2022; 62:5806-5820. [PMID: 36342197 DOI: 10.1021/acs.jcim.2c00895] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
An important part in structure-based drug design is the selection of an appropriate protein structure. It has been revealed that a holo protein structure that contains a well-defined binding site is a much better choice than an apo structure in structure-based drug discovery. Therefore, it is valuable to obtain a holo-like protein conformation from apo structures in the case where no holo structure is available. Meeting the need, we present a robust approach to generate reliable holo-like structures from apo structures by ligand binding site refinement with restraints derived from holo templates with low homology. Our method was tested on a test set of 32 proteins from the DUD-E data set and compared with other approaches. It was shown that our method successfully refined the apo structures toward the corresponding holo conformations for 23 of 32 proteins, reducing the average all-heavy-atom RMSD of binding site residues by 0.48 Å. In addition, when evaluated against all the holo structures in the protein data bank, our method can improve the binding site RMSD for 14 of 19 cases that experience significant conformational changes. Furthermore, our refined structures also demonstrate their advantages over the apo structures in ligand binding mode predictions by both rigid docking and flexible docking and in virtual screening on the database of active and decoy ligands from the DUD-E. These results indicate that our method is effective in recovering holo-like conformations and will be valuable in structure-based drug discovery.
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Affiliation(s)
- Jinze Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Hao Li
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Xuejun Zhao
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan430074, Hubei, P. R. China
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21
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Wei GW, Soares TA, Wahab H, Zhu F. Computational Chemistry in Asia. J Chem Inf Model 2022; 62:5035-5037. [DOI: 10.1021/acs.jcim.2c01050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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22
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Zhou Y, Li M, Shen T, Yang T, Shi G, Wei Y, Chen C, Wang D, Wang Y, Zhang T. Celastrol Targets Cullin-Associated and Neddylation-Dissociated 1 to Prevent Fibroblast-Myofibroblast Transformation against Pulmonary Fibrosis. ACS Chem Biol 2022; 17:2734-2743. [PMID: 36076154 DOI: 10.1021/acschembio.2c00099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Celastrol (CEL), a pentacyclic triterpene compound, has been proven to have a definite antipulmonary fibrosis effect. However, its direct targets for antipulmonary fibrosis remain unknown. In this study, we designed and synthesized a series of celastrol-based probes to identify the direct targets in human pulmonary fibroblasts using an activity-based protein profiling strategy. Among many fished targets, we identified a key protein, cullin-associated and neddylation-dissociated 1 (CAND1), which was involved in fibroblast-myofibroblast transformation (FMT). More importantly, we found that the inhibitory effect of celastrol on FMT is dependent on CAND1, through improving the interactions between CAND1 and Cullin1 to promote the activity of Skp1/Cullin1/F-box ubiquitin ligases. In silico studies and cysteine mutation experiments further demonstrated that Cys264 of CAND1 is the site for conjugation of celastrol. This reveals a new mechanism of celastrol against pulmonary fibrosis and may provide a novel therapeutic option for antipulmonary fibrosis.
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Affiliation(s)
- Yu Zhou
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Manru Li
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Tao Shen
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Tianming Yang
- Tianjin Key Laboratory of Molecular Design and Drug Discovery, Tianjin Institute of Pharmaceutical Research, Tianjin 300301, China.,State Key Laboratory of Drug Delivery Technology and Pharmacokinetics, Tianjin Institute of Pharmaceutical Research, Tianjin 300301, China
| | - Gaona Shi
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Yazi Wei
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Chengjuan Chen
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Yanan Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
| | - Tiantai Zhang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China
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23
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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24
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Radoux CJ, Vianello F, McGreig J, Desai N, Bradley AR. The druggable genome: Twenty years later. FRONTIERS IN BIOINFORMATICS 2022; 2:958378. [PMID: 36304325 PMCID: PMC9580872 DOI: 10.3389/fbinf.2022.958378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.
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25
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Wang C, Chen Y, Zhang Y, Li K, Lin M, Pan F, Wu W, Zhang J. A reinforcement learning approach for protein-ligand binding pose prediction. BMC Bioinformatics 2022; 23:368. [PMID: 36076158 PMCID: PMC9454149 DOI: 10.1186/s12859-022-04912-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu2+), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO42-), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.
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Affiliation(s)
- Chenran Wang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Yang Chen
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Yuan Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Keqiao Li
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Menghan Lin
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Feng Pan
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA
| | - Wei Wu
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA.
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, 32306-4330, USA.
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26
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Aguti R, Gardini E, Bertazzo M, Decherchi S, Cavalli A. Probabilistic Pocket Druggability Prediction via One-Class Learning. Front Pharmacol 2022; 13:870479. [PMID: 35847005 PMCID: PMC9278401 DOI: 10.3389/fphar.2022.870479] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 03/24/2022] [Indexed: 12/31/2022] Open
Abstract
The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by in silico druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed via NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling.
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Affiliation(s)
- Riccardo Aguti
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Erika Gardini
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Martina Bertazzo
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Sergio Decherchi
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy
| | - Andrea Cavalli
- Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.,Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
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27
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Ning S, Wang H, Zeng C, Zhao Y. Prediction of allosteric druggable pockets of cyclin-dependent kinases. Brief Bioinform 2022; 23:6643454. [PMID: 35830869 DOI: 10.1093/bib/bbac290] [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: 05/04/2022] [Revised: 06/07/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.
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Affiliation(s)
- Shangbo Ning
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC 20052, USA
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
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28
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Meli R, Morris GM, Biggin PC. Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review. FRONTIERS IN BIOINFORMATICS 2022; 2:885983. [PMID: 36187180 PMCID: PMC7613667 DOI: 10.3389/fbinf.2022.885983] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/11/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.
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Affiliation(s)
- Rocco Meli
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
| | - Garrett M. Morris
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Philip C. Biggin
- Department of Biochemistry, University of Oxford, Oxford, United Kingdom
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29
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Chelur VR, Priyakumar UD. BiRDS - Binding Residue Detection from Protein Sequences Using Deep ResNets. J Chem Inf Model 2022; 62:1809-1818. [PMID: 35414182 DOI: 10.1021/acs.jcim.1c00972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein-drug interactions play important roles in many biological processes and therapeutics. Predicting the binding sites of a protein helps to discover such interactions. New drugs can be designed to optimize these interactions, improving protein function. The tertiary structure of a protein decides the binding sites available to the drug molecule, but the determination of the 3D structure is slow and expensive. Conversely, the determination of the amino acid sequence is swift and economical. Although quick and accurate prediction of the binding site using just the sequence is challenging, the application of Deep Learning, which has been hugely successful in several biochemical tasks, makes it feasible. BiRDS is a Residual Neural Network that predicts the protein's most active binding site using sequence information. SC-PDB, an annotated database of druggable binding sites, is used for training the network. Multiple Sequence Alignments of the proteins in the database are generated using DeepMSA, and features such as Position-Specific Scoring Matrix, Secondary Structure, and Relative Solvent Accessibility are extracted. During training, a weighted binary cross-entropy loss function is used to counter the substantial imbalance in the two classes of binding and nonbinding residues. A novel test set SC6K is introduced to compare binding-site prediction methods. BiRDS achieves an AUROC score of 0.87, and the center of 25% of its predicted binding sites lie within 4 Å of the center of the actual binding site.
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Affiliation(s)
- Vineeth R Chelur
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences & Bioinformatics International Institute of Information Technology Hyderabad 500032, India
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30
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McGreig JE, Uri H, Antczak M, Sternberg MJE, Michaelis M, Wass MN. 3DLigandSite: structure-based prediction of protein-ligand binding sites. Nucleic Acids Res 2022; 50:W13-W20. [PMID: 35412635 PMCID: PMC9252821 DOI: 10.1093/nar/gkac250] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/13/2022] [Accepted: 04/03/2022] [Indexed: 01/13/2023] Open
Abstract
3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.
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Affiliation(s)
- Jake E McGreig
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Hannah Uri
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Magdalena Antczak
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Martin Michaelis
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
| | - Mark N Wass
- School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury, Kent CT2 7NJ, UK
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31
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Sridharan B, Goel M, Priyakumar UD. Modern Machine Learning for Tackling Inverse Problems in Chemistry: Molecular Design to Realization. Chem Commun (Camb) 2022; 58:5316-5331. [DOI: 10.1039/d1cc07035e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The discovery of new molecules and materials helps expand the horizons of novel and innovative real-life applications. In the pursuit of finding molecules with desired properties, chemists have traditionally relied...
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32
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Goel M, Raghunathan S, Laghuvarapu S, Priyakumar UD. MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards. J Chem Inf Model 2021; 61:5815-5826. [PMID: 34866384 DOI: 10.1021/acs.jcim.1c01341] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches such as high-throughput virtual screening require extensive combing through existing data sets in the hope of finding possible matches. In this study, we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target and other desirable properties for druglike molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate druglike molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, quantitative estimate of drug likeliness, topological polar surface area, and hydration free energy along with the binding affinity. For multiobjective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows an enhanced ability to generate a significantly higher number of molecules with desirable properties.
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Affiliation(s)
- Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.,École Centrale School of Engineering, Mahindra University, Hyderabad 500 043, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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33
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Karthikeyan A, Priyakumar UD. Artificial intelligence: machine learning for chemical sciences. J CHEM SCI 2021; 134:2. [PMID: 34955617 PMCID: PMC8691161 DOI: 10.1007/s12039-021-01995-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/08/2021] [Accepted: 09/14/2021] [Indexed: 12/05/2022]
Abstract
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences. Synopsis Recent surge in the application of machine learning (ML) methods in fundamental sciences has led to a perspective that these methods may become important tools in chemical science. This perspective provides an overview of the modern ML methods and their successful applications in chemistry during the last few years.
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Affiliation(s)
- Akshaya Karthikeyan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500 032 India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500 032 India
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34
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Samaga YBL, Raghunathan S, Priyakumar UD. SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation. J Phys Chem B 2021; 125:10657-10671. [PMID: 34546056 DOI: 10.1021/acs.jpcb.1c04913] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new Stransitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.
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Affiliation(s)
- Yashas B L Samaga
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - Shampa Raghunathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
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35
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Mehta S, Laghuvarapu S, Pathak Y, Sethi A, Alvala M, Priyakumar UD. MEMES: Machine learning framework for Enhanced MolEcular Screening. Chem Sci 2021; 12:11710-11721. [PMID: 34659706 PMCID: PMC8442698 DOI: 10.1039/d1sc02783b] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 07/24/2021] [Indexed: 01/20/2023] Open
Abstract
In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.
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Affiliation(s)
- Sarvesh Mehta
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Yashaswi Pathak
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research Hyderabad 500 037 India
| | - Mallika Alvala
- School of Pharmacy and Technology Management, Narsee Monjee Institute of Management Sciences Hyderabad India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology Hyderabad 500 032 India +91 40 6653 1413 +91 40 6653 1161
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Kandel J, Tayara H, Chong KT. PUResNet: prediction of protein-ligand binding sites using deep residual neural network. J Cheminform 2021; 13:65. [PMID: 34496970 PMCID: PMC8424938 DOI: 10.1186/s13321-021-00547-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/28/2021] [Indexed: 11/10/2022] Open
Abstract
Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00547-7.
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Affiliation(s)
- Jeevan Kandel
- Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea. .,Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, 54896, South Korea.
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