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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Xu L, Cai C, Fang J, Wu Q, Zhao J, Wang Z, Guo P, Zheng L, Liu A. Systems pharmacology dissection of pharmacological mechanisms of Xiaochaihu decoction against human coronavirus. BMC Complement Med Ther 2023; 23:252. [PMID: 37475019 PMCID: PMC10357659 DOI: 10.1186/s12906-023-04024-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/03/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Although coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study. METHODS We constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis. RESULTS Based on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay. CONCLUSION Our work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.
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Affiliation(s)
- Lvjie Xu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Beijing Key Laboratory of Drug Target Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Department of Pharmacy, Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
| | - Chuipu Cai
- Division of Data Intelligence, Department of Computer Science, Key Laboratory of Intelligent Manufacturing Technology of Ministry of Education, Shantou University, Shantou, China
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qihui Wu
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jun Zhao
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Beijing Key Laboratory of Drug Target Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Zhe Wang
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Beijing Key Laboratory of Drug Target Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Pengfei Guo
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- Beijing Key Laboratory of Drug Target Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Lishu Zheng
- NHC Key Laboratory of Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, China CDC, Beijing, China.
- Center for Biosafety Mega-Science, Chinese Academy of Sciences, Beijing, China.
| | - Ailin Liu
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
- Beijing Key Laboratory of Drug Target Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
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Yamane H, Ishida T. Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs. FRONTIERS IN BIOINFORMATICS 2023; 3:1193025. [PMID: 37304403 PMCID: PMC10250622 DOI: 10.3389/fbinf.2023.1193025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/15/2023] [Indexed: 06/13/2023] Open
Abstract
Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.
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Wen J, Gan H, Yang Z, Zhou R, Zhao J, Ye Z. Mutual-DTI: A mutual interaction feature-based neural network for drug-target protein interaction prediction. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:10610-10625. [PMID: 37322951 DOI: 10.3934/mbe.2023469] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.
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Affiliation(s)
- Jiahui Wen
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Haitao Gan
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Zhi Yang
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Ran Zhou
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
| | - Jing Zhao
- State Key Laboratory of Biocatalysis and Enzyme Engineering, Hubei University, Wuhan 430062, China
| | - Zhiwei Ye
- School of Computer Science, Hubei University of Technology, Wuhan 430068, China
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5
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SuHAN: Substructural hierarchical attention network for molecular representation. J Mol Graph Model 2023; 119:108401. [PMID: 36584590 DOI: 10.1016/j.jmgm.2022.108401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/16/2022] [Accepted: 12/23/2022] [Indexed: 12/26/2022]
Abstract
Recently, molecular representation and property exploration, with the combination of neural network, play a critical role in the field of drug design and discovery for assisting in drug related research. However, previous research in molecular representation relies heavily on artificial extraction of features based on biological experiments which may result in a manually introduced noise of molecular information with high cost in time and money. In this paper, a novel method named Substructural Hierarchical Attention Network (SuHAN) is proposed to discover inherent characteristics of molecules for representation learning. Specifically, SuHAN is composed of the cascaded layer: atom-level layer and substructure-level layer. Molecule in the SMILES format is divided into several substructural fragments by predefined partition rules, and then they are fed into atom-level layer and substructure-level layer successively to obtain feature representation from different perspective: atomic view and substructural view. In this way, the prominent structural features that may be omitted in global extraction are excavated from a fine-grained viewpoint and fused to reconstruct representative pattern in an overall view. Experiments on biophysics and physiology datasets demonstrate that our model is competitive with a significant improvement of both accuracy and stability in performance. We confirmed that the substructural segments and progressive hierarchical networks lead to an effective molecular representation for downstream tasks. These results provide a novel perspective about reconstructing overall pattern through local prominent structure.
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Hua Y, Song X, Feng Z, Wu X. MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region. Bioinformatics 2023; 39:7008321. [PMID: 36708000 PMCID: PMC9900210 DOI: 10.1093/bioinformatics/btad056] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/31/2022] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. RESULTS We propose a novel Multi-Functional and Robust Drug-Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug-target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION https://github.com/JU-HuaY/MFR.
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Affiliation(s)
- Yang Hua
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Xiaoning Song
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Zhenhua Feng
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK
| | - Xiaojun Wu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
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7
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Hu Z, Liu W, Zhang C, Huang J, Zhang S, Yu H, Xiong Y, Liu H, Ke S, Hong L. SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction. Brief Bioinform 2023; 24:6955272. [PMID: 36545795 DOI: 10.1093/bib/bbac533] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/05/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022] Open
Abstract
Drug-target binding affinity prediction is a fundamental task for drug discovery and has been studied for decades. Most methods follow the canonical paradigm that processes the inputs of the protein (target) and the ligand (drug) separately and then combines them together. In this study we demonstrate, surprisingly, that a model is able to achieve even superior performance without access to any protein-sequence-related information. Instead, a protein is characterized completely by the ligands that it interacts. Specifically, we treat different proteins separately, which are jointly trained in a multi-head manner, so as to learn a robust and universal representation of ligands that is generalizable across proteins. Empirical evidences show that the novel paradigm outperforms its competitive sequence-based counterpart, with the Mean Squared Error (MSE) of 0.4261 versus 0.7612 and the R-Square of 0.7984 versus 0.6570 compared with DeepAffinity. We also investigate the transfer learning scenario where unseen proteins are encountered after the initial training, and the cross-dataset evaluation for prospective studies. The results reveals the robustness of the proposed model in generalizing to unseen proteins as well as in predicting future data. Source codes and data are available at https://github.com/huzqatpku/SAM-DTA.
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Affiliation(s)
| | - Wenfeng Liu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | | | - Jiawen Huang
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Shaoting Zhang
- SenseTime Research, Shanghai, 201103, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
| | - Huiqun Yu
- Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hao Liu
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Song Ke
- Shanghai Matwings Technology Co., Ltd., Shanghai, 200240, China
| | - Liang Hong
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
- School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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8
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Hua Y, Song X, Feng Z, Wu XJ, Kittler J, Yu DJ. CPInformer for Efficient and Robust Compound-Protein Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:285-296. [PMID: 35044921 DOI: 10.1109/tcbb.2022.3144008] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Recently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches.
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Maghsoudi S, Taghavi Shahraki B, Rameh F, Nazarabi M, Fatahi Y, Akhavan O, Rabiee M, Mostafavi E, Lima EC, Saeb MR, Rabiee N. A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery. Chem Biol Drug Des 2022; 100:699-721. [PMID: 36002440 PMCID: PMC9539342 DOI: 10.1111/cbdd.14136] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/07/2022] [Accepted: 08/21/2022] [Indexed: 11/29/2022]
Abstract
Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.
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Affiliation(s)
- Saeid Maghsoudi
- Faculty of Medicine, Department of Physiology and PathophysiologyUniversity of ManitobaWinnipegManitobaCanada
- Biology of Breathing Group, Children's Hospital Research Institute of Manitoba (CHRIM), University of ManitobaWinnipegManitobaCanada
| | | | | | - Masoomeh Nazarabi
- Faculty of Organic Chemistry, Department of ChemistryUniversity of KashanKashanIran
| | - Yousef Fatahi
- Department of Pharmaceutical Nanotechnology, Faculty of PharmacyTehran University of Medical SciencesTehranIran
- Nanotechnology Research Center, Faculty of PharmacyTehran University of Medical SciencesTehranIran
| | - Omid Akhavan
- Department of PhysicsSharif University of TechnologyTehranIran
| | - Mohammad Rabiee
- Biomaterials Group, Department of Biomedical EngineeringAmirkabir University of TechnologyTehranIran
| | - Ebrahim Mostafavi
- Stanford Cardiovascular Institute, Stanford University School of MedicineStanfordCaliforniaUSA
- Department of MedicineStanford University School of MedicineStanfordCaliforniaUSA
| | - Eder C. Lima
- Institute of Chemistry, Federal University of Rio Grande Do Sul (UFRGS)Porto AlegreBrazil
| | - Mohammad Reza Saeb
- Department of Polymer Technology, Faculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Navid Rabiee
- Department of PhysicsSharif University of TechnologyTehranIran
- School of EngineeringMacquarie UniversitySydneyNew South WalesAustralia
- Department of Materials Science and EngineeringPohang University of Science and Technology (POSTECH)PohangSouth Korea
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Sohrab SS, Kamal MA. Screening, Docking, and Molecular Dynamics Study of Natural Compounds as an Anti-HER2 for the Management of Breast Cancer. Life (Basel) 2022; 12:1729. [PMID: 36362883 PMCID: PMC9693058 DOI: 10.3390/life12111729] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/19/2022] [Accepted: 10/25/2022] [Indexed: 08/27/2023] Open
Abstract
Breast cancer (BC) is one of the most frequent types of cancer that affect women. Human epidermal growth factor receptor-2 (HER2) is responsible for 20% of all BC cases. The use of anti-HER2 natural compounds in the cure of BC that is HER2-positive patients has resulted in significant increases in survival in both early and advanced stages. The findings of in-silico research support the use of ligands as possible HER2 inhibitors, and molecules with high free energy of binding may have considerable anti-BC action, making them candidates for future drug development. The inhibitory activity of selected ligands like ZINC43069427 and ZINC95918662 against HER2 was found to be -11.0 and -8.50 kcal/mol, respectively. The amino acid residues Leu726, Val734, Ala751, Lys753, Thr798, Gly804, Arg849, Leu852, Thr862, and Asp863 were found in common interaction as compared to the control compound Lapatinib. Molecular dynamics study calculations of these selected potent inhibitors were conducted and found to be stable over the 50 ns simulation time in terms of root mean square deviation (RMSD), root-mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA). In addition, there are several parameters such as absorption, distribution, metabolism, and excretion toxicity (ADMET), physicochemical, and drug-likeness that were checked and found in good range to be potential lead-like molecules. Several drug-likeness rules like Lipinski, Ghose, Veber, Egan, and Muegge were checked and found to be positive for these rules. Based on these calculations and different parameters, these top two selected natural compounds can be used as potential candidates for anti-HER2 for the management of BC.
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Affiliation(s)
- Sayed Sartaj Sohrab
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22254, Saudi Arabia
| | - Mohammad Amjad Kamal
- Special Infectious Agents Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 22254, Saudi Arabia
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh
- Enzymoics, 7 Peterlee Place, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
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11
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Sun G, Dong D, Dong Z, Zhang Q, Fang H, Wang C, Zhang S, Wu S, Dong Y, Wan Y. Drug repositioning: A bibliometric analysis. Front Pharmacol 2022; 13:974849. [PMID: 36225586 PMCID: PMC9549161 DOI: 10.3389/fphar.2022.974849] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 08/12/2022] [Indexed: 11/14/2022] Open
Abstract
Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.
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Affiliation(s)
- Guojun Sun
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Dashun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Zuojun Dong
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Qian Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Hui Fang
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
| | - Chaojun Wang
- Hangzhou Aeronautical Sanatorium for Special Service of Chinese Air Force, Hangzhou, China
| | - Shaoya Zhang
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Shuaijun Wu
- Institute of Pharmaceutical Preparations, Department of Pharmacy, Zhejiang University of Technology, Hangzhou, China
| | - Yichen Dong
- Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China
| | - Yuehua Wan
- Institute of Information Resource, Zhejiang University of Technology, Hangzhou, China
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12
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Cheng Z, Zhao Q, Li Y, Wang J. IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism. Bioinformatics 2022; 38:4153-4161. [PMID: 35801934 DOI: 10.1093/bioinformatics/btac485] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/02/2022] [Accepted: 07/07/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Identifying drug-target interactions is a crucial step for drug discovery and design. Traditional biochemical experiments are credible to accurately validate drug-target interactions. However, they are also extremely laborious, time-consuming and expensive. With the collection of more validated biomedical data and the advancement of computing technology, the computational methods based on chemogenomics gradually attract more attention, which guide the experimental verifications. RESULTS In this study, we propose an end-to-end deep learning-based method named IIFDTI to predict drug-target interactions (DTIs) based on independent features of drug-target pairs and interactive features of their substructures. First, the interactive features of substructures between drugs and targets are extracted by the bidirectional encoder-decoder architecture. The independent features of drugs and targets are extracted by the graph neural networks and convolutional neural networks, respectively. Then, all extracted features are fused and inputted into fully connected dense layers in downstream tasks for predicting DTIs. IIFDTI takes into account the independent features of drugs/targets and simulates the interactive features of the substructures from the biological perspective. Multiple experiments show that IIFDTI outperforms the state-of-the-art methods in terms of the area under the receiver operating characteristics curve (AUC), the area under the precision-recall curve (AUPR), precision, and recall on benchmark datasets. In addition, the mapped visualizations of attention weights indicate that IIFDTI has learned the biological knowledge insights, and two case studies illustrate the capabilities of IIFDTI in practical applications. AVAILABILITY AND IMPLEMENTATION The data and codes underlying this article are available in Github at https://github.com/czjczj/IIFDTI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhongjian Cheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Qichang Zhao
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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13
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Bui-Thi D, Rivière E, Meysman P, Laukens K. Predicting compound-protein interaction using hierarchical graph convolutional networks. PLoS One 2022; 17:e0258628. [PMID: 35862351 PMCID: PMC9302762 DOI: 10.1371/journal.pone.0258628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/12/2022] [Indexed: 11/18/2022] Open
Abstract
Motivation
Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction.
Results
Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.
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Affiliation(s)
- Danh Bui-Thi
- Adrem Data Lab, University of Antwerp, Antwerp, Belgium
| | | | | | - Kris Laukens
- Adrem Data Lab, University of Antwerp, Antwerp, Belgium
- * E-mail:
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14
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Cheng Z, Yan C, Wu FX, Wang J. Drug-Target Interaction Prediction Using Multi-Head Self-Attention and Graph Attention Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2208-2218. [PMID: 33956632 DOI: 10.1109/tcbb.2021.3077905] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Identifying drug-target interactions (DTIs) is an important step in the process of new drug discovery and drug repositioning. Accurate predictions for DTIs can improve the efficiency in the drug discovery and development. Although rapid advances in deep learning technologies have generated various computational methods, it is still appealing to further investigate how to design efficient networks for predicting DTIs. In this study, we propose an end-to-end deep learning method (called MHSADTI) to predict DTIs based on the graph attention network and multi-head self-attention mechanism. First, the characteristics of drugs and proteins are extracted by the graph attention network and multi-head self-attention mechanism, respectively. Then, the attention scores are used to consider which amino acid subsequence in a protein is more important for the drug to predict its interactions. Finally, we predict DTIs by a fully connected layer after obtaining the feature vectors of drugs and proteins. MHSADTI takes advantage of self-attention mechanism for obtaining long-dependent contextual relationship in amino acid sequences and predicting DTI interpretability. More effective molecular characteristics are also obtained by the attention mechanism in graph attention networks. Multiple cross validation experiments are adopted to assess the performance of our MHSADTI. The experiments on four datasets, human, C.elegans, DUD-E and DrugBank show our method outperforms the state-of-the-art methods in terms of AUC, Precision, Recall, AUPR and F1-score. In addition, the case studies further demonstrate that our method can provide effective visualizations to interpret the prediction results from biological insights.
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15
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ZHANG BY, ZHENG YF, ZHAO J, KANG D, WANG Z, XU LJ, LIU AL, DU GH. Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation. Chin J Nat Med 2022; 20:332-351. [DOI: 10.1016/s1875-5364(22)60180-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Indexed: 11/03/2022]
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16
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Multi-TransDTI: Transformer for Drug–Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy. Biomolecules 2022; 12:biom12050644. [PMID: 35625572 PMCID: PMC9138327 DOI: 10.3390/biom12050644] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/19/2022] [Accepted: 04/25/2022] [Indexed: 01/03/2023] Open
Abstract
Prediction on drug–target interaction has always been a crucial link for drug discovery and repositioning, which have witnessed tremendous progress in recent years. Despite many efforts made, the existing representation learning or feature generation approaches of both drugs and proteins remain complicated as well as in high dimension. In addition, it is difficult for current methods to extract local important residues from sequence information while remaining focused on global structure. At the same time, massive data is not always easily accessible, which makes model learning from small datasets imminent. As a result, we propose an end-to-end learning model with SUPD and SUDD methods to encode drugs and proteins, which not only leave out the complicated feature extraction process but also greatly reduce the dimension of the embedding matrix. Meanwhile, we use a multi-view strategy with a transformer to extract local important residues of proteins for better representation learning. Finally, we evaluate our model on the BindingDB dataset in comparisons with different state-of-the-art models from comprehensive indicators. In results of 100% BindingDB, our AUC, AUPR, ACC, and F1-score reached 90.9%, 89.8%, 84.2%, and 84.3% respectively, which successively exceed the average values of other models by 2.2%, 2.3%, 2.6%, and 2.6%. Moreover, our model also generally surpasses their performance on 30% and 50% BindingDB datasets.
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17
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Li M, Lu Z, Wu Y, Li Y. BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. Bioinformatics 2022; 38:1995-2002. [PMID: 35043942 DOI: 10.1093/bioinformatics/btac035] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 12/06/2021] [Accepted: 01/14/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION The identification of compound-protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has therefore become a promising and efficient alternative for predicting novel interactions between compounds and proteins on a large scale. Most supervised machine learning prediction models are approached as a binary classification problem, which aim to predict whether there is an interaction between the compound and the protein or not. However, CPI is not a simple binary on-off relationship, but a continuous value reflects how tightly the compound binds to a particular target protein, also called binding affinity. RESULTS In this study, we propose an end-to-end neural network model, called BACPI, to predict CPI and binding affinity. We employ graph attention network and convolutional neural network (CNN) to learn the representations of compounds and proteins and develop a bi-directional attention neural network model to integrate the representations. To evaluate the performance of BACPI, we use three CPI datasets and four binding affinity datasets in our experiments. The results show that, when predicting CPIs, BACPI significantly outperforms other available machine learning methods on both balanced and unbalanced datasets. This suggests that the end-to-end neural network model that predicts CPIs directly from low-level representations is more robust than traditional machine learning-based methods. And when predicting binding affinities, BACPI achieves higher performance on large datasets compared to other state-of-the-art deep learning methods. This comparison result suggests that the proposed method with bi-directional attention neural network can capture the important regions of compounds and proteins for binding affinity prediction. AVAILABILITY AND IMPLEMENTATION Data and source codes are available at https://github.com/CSUBioGroup/BACPI.
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Affiliation(s)
- Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Zhangli Lu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - Yifan Wu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China
| | - YaoHang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA, USA
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18
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Du BX, Qin Y, Jiang YF, Xu Y, Yiu SM, Yu H, Shi JY. Compound–protein interaction prediction by deep learning: Databases, descriptors and models. Drug Discov Today 2022; 27:1350-1366. [DOI: 10.1016/j.drudis.2022.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/19/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022]
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19
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Alakus TB, Turkoglu I. A Comparative Study of Amino Acid Encoding Methods for Predicting Drug-Target Interactions in COVID-19 Disease. MODELING, CONTROL AND DRUG DEVELOPMENT FOR COVID-19 OUTBREAK PREVENTION 2022:619-643. [DOI: 10.1007/978-3-030-72834-2_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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20
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Jin Y, Lu J, Shi R, Yang Y. EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction. Biomolecules 2021; 11:biom11121783. [PMID: 34944427 PMCID: PMC8698792 DOI: 10.3390/biom11121783] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 11/20/2021] [Accepted: 11/24/2021] [Indexed: 01/09/2023] Open
Abstract
The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds.
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Affiliation(s)
- Yuan Jin
- Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China; (Y.J.); (R.S.)
| | - Jiarui Lu
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China;
| | - Runhan Shi
- Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China; (Y.J.); (R.S.)
| | - Yang Yang
- Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China; (Y.J.); (R.S.)
- Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China
- Correspondence:
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21
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Monteiro NRC, Ribeiro B, Arrais JP. Drug-Target Interaction Prediction: End-to-End Deep Learning Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2364-2374. [PMID: 32142454 DOI: 10.1109/tcbb.2020.2977335] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Although putting efforts on the traditional in vivo or in vitro methods, pharmaceutical financial investment has been reduced over the years. Therefore, establishing effective computational methods is decisive to find new leads in a reasonable amount of time. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. In this paper, we present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein sequences (amino acid sequence) and compounds SMILES (Simplified Molecular Input Line Entry System) strings. These representations can be interpreted as features that express local dependencies or patterns that can then be used in a Fully Connected Neural Network (FCNN), acting as a binary classifier. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance. The proposed end-to-end deep learning method outperformed traditional machine learning approaches in the correct classification of both positive and negative interactions.
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22
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Recent Advances in In Silico Target Fishing. Molecules 2021; 26:molecules26175124. [PMID: 34500568 PMCID: PMC8433825 DOI: 10.3390/molecules26175124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/14/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
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23
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Perpetuo L, Klein J, Ferreira R, Guedes S, Amado F, Leite-Moreira A, Silva AMS, Thongboonkerd V, Vitorino R. How can artificial intelligence be used for peptidomics? Expert Rev Proteomics 2021; 18:527-556. [PMID: 34343059 DOI: 10.1080/14789450.2021.1962303] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION Peptidomics is an emerging field of omics sciences using advanced isolation, analysis, and computational techniques that enable qualitative and quantitative analyses of various peptides in biological samples. Peptides can act as useful biomarkers and as therapeutic molecules for diseases. AREAS COVERED The use of therapeutic peptides can be predicted quickly and efficiently using data-driven computational methods, particularly artificial intelligence (AI) approach. Various AI approaches are useful for peptide-based drug discovery, such as support vector machine, random forest, extremely randomized trees, and other more recently developed deep learning methods. AI methods are relatively new to the development of peptide-based therapies, but these techniques already become essential tools in protein science by dissecting novel therapeutic peptides and their functions (Figure 1).[Figure: see text]. EXPERT OPINION Researchers have shown that AI models can facilitate the development of peptidomics and selective peptide therapies in the field of peptide science. Biopeptide prediction is important for the discovery and development of successful peptide-based drugs. Due to their ability to predict therapeutic roles based on sequence details, many AI-dependent prediction tools have been developed (Figure 1).
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Affiliation(s)
- Luís Perpetuo
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Université Toulouse III, Toulouse, France
| | - Rita Ferreira
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Francisco Amado
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Adelino Leite-Moreira
- UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
| | - Artur M S Silva
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro
| | - Visith Thongboonkerd
- Medical Proteomics Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Rui Vitorino
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro.,LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro.,UnIC, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina da Universidade do Porto, Porto
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24
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Yang S, Ye Q, Ding J, Yin, Lu A, Chen X, Hou T, Cao D. Current advances in ligand‐based target prediction. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1504] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Su‐Qing Yang
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
| | - Qing Ye
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Jun‐Jie Ding
- Beijing Institute of Pharmaceutical Chemistry Beijing China
| | - Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ai‐Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital Central South University Changsha Hunan China
| | - Ting‐Jun Hou
- College of Pharmaceutical Sciences Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University Hangzhou, Zhejiang China
| | - Dong‐Sheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha Hunan China
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine Hong Kong Baptist University Hong Kong China
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25
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Li Z, Li X, Liu X, Fu Z, Xiong Z, Wu X, Tan X, Zhao J, Zhong F, Wan X, Luo X, Chen K, Jiang H, Zheng M. KinomeX: a web application for predicting kinome-wide polypharmacology effect of small molecules. Bioinformatics 2020; 35:5354-5356. [PMID: 31228181 DOI: 10.1093/bioinformatics/btz519] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 06/13/2019] [Accepted: 06/18/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The large-scale kinome-wide virtual profiling for small molecules is a daunting task by experimental and traditional in silico drug design approaches. Recent advances in deep learning algorithms have brought about new opportunities in promoting this process. RESULTS KinomeX is an online platform to predict kinome-wide polypharmacology effect of small molecules based solely on their chemical structures. The prediction is made by a multi-task deep neural network model trained with over 140 000 bioactivity data points for 391 kinases. Extensive computational and experimental validations have been performed. Overall, KinomeX enables users to create a comprehensive kinome interaction network for designing novel chemical modulators, and is of practical value on exploring the previously less studied or untargeted kinases. AVAILABILITY AND IMPLEMENTATION KinomeX is available at: https://kinome.dddc.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhaojun Li
- School of Information Management, Dezhou University, Dezhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaohong Liu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Zunyun Fu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Zhaoping Xiong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Xiaolong Wu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jihui Zhao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Feisheng Zhong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaozhe Wan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
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26
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Karimi M, Wu D, Wang Z, Shen Y. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 2020; 35:3329-3338. [PMID: 30768156 DOI: 10.1093/bioinformatics/btz111] [Citation(s) in RCA: 217] [Impact Index Per Article: 54.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 12/26/2018] [Accepted: 02/12/2019] [Indexed: 02/02/2023] Open
Abstract
MOTIVATION Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability. RESULTS We present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead. AVAILABILITY AND IMPLEMENTATION Data and source codes are available at https://github.com/Shen-Lab/DeepAffinity. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mostafa Karimi
- Department of Electrical and Computer Engineering, College Station, TX, USA.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, College Station, TX, USA
| | - Di Wu
- Department of Electrical and Computer Engineering, College Station, TX, USA
| | - Zhangyang Wang
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Yang Shen
- Department of Electrical and Computer Engineering, College Station, TX, USA.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, College Station, TX, USA
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Chen L, Tan X, Wang D, Zhong F, Liu X, Yang T, Luo X, Chen K, Jiang H, Zheng M. TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 2020; 36:4406-4414. [DOI: 10.1093/bioinformatics/btaa524] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 04/13/2020] [Accepted: 05/14/2020] [Indexed: 12/13/2022] Open
Abstract
Abstract
Motivation
Identifying compound–protein interaction (CPI) is a crucial task in drug discovery and chemogenomics studies, and proteins without three-dimensional structure account for a large part of potential biological targets, which requires developing methods using only protein sequence information to predict CPI. However, sequence-based CPI models may face some specific pitfalls, including using inappropriate datasets, hidden ligand bias and splitting datasets inappropriately, resulting in overestimation of their prediction performance.
Results
To address these issues, we here constructed new datasets specific for CPI prediction, proposed a novel transformer neural network named TransformerCPI, and introduced a more rigorous label reversal experiment to test whether a model learns true interaction features. TransformerCPI achieved much improved performance on the new experiments, and it can be deconvolved to highlight important interacting regions of protein sequences and compound atoms, which may contribute chemical biology studies with useful guidance for further ligand structural optimization.
Availability and implementation
https://github.com/lifanchen-simm/transformerCPI.
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Affiliation(s)
- Lifan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoqin Tan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dingyan Wang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feisheng Zhong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaohong Liu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Tianbiao Yang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaomin Luo
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Kaixian Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Hualiang Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
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Liu Z, Du J, Fang J, Yin Y, Xu G, Xie L. DeepScreening: a deep learning-based screening web server for accelerating drug discovery. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5585580. [PMID: 31608949 PMCID: PMC6790966 DOI: 10.1093/database/baz104] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/16/2019] [Accepted: 08/01/2019] [Indexed: 12/31/2022]
Abstract
Deep learning contributes significantly to researches in biological sciences and drug discovery. Previous studies suggested that deep learning techniques have shown superior performance to other machine learning algorithms in virtual screening, which is a critical step to accelerate the drug discovery. However, the application of deep learning techniques in drug discovery and chemical biology are hindered due to the data availability, data further processing and lacking of the user-friendly deep learning tools and interface. Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists perform virtual screening either the chemical probes or drugs for a specific target of interest. With DeepScreening, user could conveniently construct a deep learning model and generate the target-focused de novo libraries. The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or diverse chemical libraries in stock. From deep models training to virtual screening, and target focused de novo library generation, all those tasks could be finished with DeepScreening. We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery.
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Affiliation(s)
- Zhihong Liu
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, 100 Xianlie Middle Road, Guangzhou 510070, China
| | - Jiewen Du
- Division of Algorithm, Beijing Jingpai Technology Co., Ltd. 1500-1, Hailong Building Z-Park, Beijing 100090, China
| | - Jiansong Fang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9620 Carnegie Ave n building, Cleveland, OH 44106, USA
| | - Yulong Yin
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, 100 Xianlie Middle Road, Guangzhou 510070, China
| | - Guohuan Xu
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, 100 Xianlie Middle Road, Guangzhou 510070, China
| | - Liwei Xie
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, 100 Xianlie Middle Road, Guangzhou 510070, China.,Zhujiang Hospital, Southern Medical University, 253 Industrial Avenue, Guangzhou 510282, China
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29
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Zakharov AV, Zhao T, Nguyen DT, Peryea T, Sheils T, Yasgar A, Huang R, Southall N, Simeonov A. Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models. J Chem Inf Model 2019; 59:4613-4624. [PMID: 31584270 DOI: 10.1021/acs.jcim.9b00526] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .
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Affiliation(s)
- Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Tongan Zhao
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Tyler Peryea
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Timothy Sheils
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Adam Yasgar
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Ruili Huang
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Noel Southall
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , 9800 Medical Center Drive , Rockville , Maryland 20850 , United States
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30
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Tsubaki M, Tomii K, Sese J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 2019; 35:309-318. [PMID: 29982330 DOI: 10.1093/bioinformatics/bty535] [Citation(s) in RCA: 272] [Impact Index Per Article: 54.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 07/03/2018] [Indexed: 01/20/2023] Open
Abstract
Motivation In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. words in natural language processing) using deep neural networks has demonstrated excellent performance on various difficult problems. For the CPI problem, data are provided as discrete symbolic data, i.e. compounds are represented as graphs where the vertices are atoms, the edges are chemical bonds, and proteins are sequences in which the characters are amino acids. In this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new CPI prediction approach by combining a graph neural network (GNN) for compounds and a convolutional neural network (CNN) for proteins. Results Our experiments using three CPI datasets demonstrated that the proposed end-to-end approach achieves competitive or higher performance as compared to various existing CPI prediction methods. In addition, the proposed approach significantly outperformed existing methods on an unbalanced dataset. This suggests that data-driven representations of compounds and proteins obtained by end-to-end GNNs and CNNs are more robust than traditional chemical and biological features obtained from databases. Although analyzing deep learning models is difficult due to their black-box nature, we address this issue using a neural attention mechanism, which allows us to consider which subsequences in a protein are more important for a drug compound when predicting its interaction. The neural attention mechanism also provides effective visualization, which makes it easier to analyze a model even when modeling is performed using real-valued representations instead of discrete features. Availability and implementation https://github.com/masashitsubaki. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Masashi Tsubaki
- National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan
| | - Kentaro Tomii
- National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.,AIST- Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
| | - Jun Sese
- National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.,AIST- Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory, Tokyo, Japan
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31
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Da C, Zhang D, Stashko M, Vasileiadi E, Parker R, Minson KA, Huey MG, Huelse JM, Hunter D, Gilbert TSK, Norris-Drouin J, Miley M, Herring LE, Graves LM, DeRyckere D, Earp HS, Graham D, Frye SV, Wang X, Kireev D. Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology. J Am Chem Soc 2019; 141:15700-15709. [PMID: 31497954 PMCID: PMC6894422 DOI: 10.1021/jacs.9b08660] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Controlling which particular members of a large protein family are targeted by a drug is key to achieving a desired therapeutic response. In this study, we report a rational data-driven strategy for achieving restricted polypharmacology in the design of antitumor agents selectively targeting the TYRO3, AXL, and MERTK (TAM) family tyrosine kinases. Our computational approach, based on the concept of fragments in structural environments (FRASE), distills relevant chemical information from structural and chemogenomic databases to assemble a three-dimensional inhibitor structure directly in the protein pocket. Target engagement by the inhibitors designed led to disruption of oncogenic phenotypes as demonstrated in enzymatic assays and in a panel of cancer cell lines, including acute lymphoblastic and myeloid leukemia (ALL/AML) and nonsmall cell lung cancer (NSCLC). Structural rationale underlying the approach was corroborated by X-ray crystallography. The lead compound demonstrated potent target inhibition in a pharmacodynamic study in leukemic mice.
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Affiliation(s)
- Chenxiao Da
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Dehui Zhang
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Michael Stashko
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Eleana Vasileiadi
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Rebecca Parker
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Katherine A. Minson
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Madeline G. Huey
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Justus M. Huelse
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Debra Hunter
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Thomas S. K. Gilbert
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jacqueline Norris-Drouin
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Michael Miley
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Laura E. Herring
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Lee M. Graves
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Deborah DeRyckere
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - H. Shelton Earp
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Douglas Graham
- Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta, and Department of Pediatrics, Emory University, Atlanta, GA 30322
| | - Stephen V. Frye
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
- Lineberger Comprehensive Cancer Center, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Xiaodong Wang
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
| | - Dmitri Kireev
- Center for Integrative Chemical Biology and Drug Discovery, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599-7363
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32
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Wei Y, Li W, Du T, Hong Z, Lin J. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method. Int J Mol Sci 2019; 20:ijms20143572. [PMID: 31336592 PMCID: PMC6678913 DOI: 10.3390/ijms20143572] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 07/13/2019] [Accepted: 07/21/2019] [Indexed: 12/11/2022] Open
Abstract
Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.
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Affiliation(s)
- Yu Wei
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Wei Li
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China
| | - Tengfei Du
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China
| | - Zhangyong Hong
- State Key Laboratory of Medicinal Chemical Biology, College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China.
| | - Jianping Lin
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University, Haihe Education Park, 38 Tongyan Road, Tianjin 300353, China.
- Platform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine, Tianjin 300000, China.
- Biodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
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33
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Lee I, Keum J, Nam H. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol 2019; 15:e1007129. [PMID: 31199797 PMCID: PMC6594651 DOI: 10.1371/journal.pcbi.1007129] [Citation(s) in RCA: 229] [Impact Index Per Article: 45.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 06/26/2019] [Accepted: 05/24/2019] [Indexed: 12/04/2022] Open
Abstract
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed that our model can detect binding sites of proteins for DTIs. In conclusion, our prediction model for detecting local residue patterns of target proteins successfully enriches the protein features of a raw protein sequence, yielding better prediction results than previous approaches. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.
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Affiliation(s)
- Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea
| | - Jongsoo Keum
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Buk-ku, Gwangju, Republic of Korea
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34
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Wu Q, Ke H, Li D, Wang Q, Fang J, Zhou J. Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery. Curr Top Med Chem 2019; 19:4-16. [DOI: 10.2174/1568026619666190122151634] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/14/2018] [Accepted: 11/16/2018] [Indexed: 12/25/2022]
Abstract
Over the past decades, peptide as a therapeutic candidate has received increasing attention in
drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory
peptides (AIPs). It is considered that the peptides can regulate various complex diseases
which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives
the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide-
based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in
the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with
traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly
machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the
peptide activity. In this review, we document the recent progress in machine learning-based prediction
of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.
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Affiliation(s)
- Qihui Wu
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Hanzhong Ke
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61802, United States
| | - Dongli Li
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
| | - Jingwei Zhou
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
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35
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Wang T, Liu XH, Guan J, Ge S, Wu MB, Lin JP, Yang LR. Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer's disease. Eur J Med Chem 2019; 169:200-223. [PMID: 30884327 DOI: 10.1016/j.ejmech.2019.02.076] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/12/2019] [Accepted: 02/28/2019] [Indexed: 12/22/2022]
Abstract
Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease.
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Affiliation(s)
- Tao Wang
- School of Biological Science, Jining Medical University, Jining, China; Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Xiao-Huan Liu
- School of Biological Science, Jining Medical University, Jining, China
| | - Jing Guan
- School of Biological Science, Jining Medical University, Jining, China
| | - Shun Ge
- School of Biological Science, Jining Medical University, Jining, China.
| | - Mian-Bin Wu
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China; Zhejiang Key Laboratory of Antifungal Drugs, Taizhou, 318000, China
| | - Jian-Ping Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Li-Rong Yang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, China
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Arrouchi H, Lakhlili W, Ibrahimi A. Re-positioning of known drugs for Pim-1 kinase target using molecular docking analysis. Bioinformation 2019; 15:116-120. [PMID: 31435157 PMCID: PMC6677905 DOI: 10.6026/97320630015116] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2019] [Revised: 02/01/2019] [Accepted: 02/01/2019] [Indexed: 12/02/2022] Open
Abstract
The Concept of reusing existing drugs for new targets is gaining momentum in recent years because of cost-effectiveness as safety and toxicology data are already available. Therefore, it is of interest to re-profile known drugs against the Pim-1 kinase target using molecular docking analysis. Results show that known drugs such as nilotinib, vemurafenib, Idelalisib, and other small kinases inhibitors have high binding ability with Pim-1 kinase for consideration as potential inhibitors.
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Affiliation(s)
- Housna Arrouchi
- Biotechnology Laboratory (Medbiotech),BioInova Research center,Rabat Medical and Pharmacy School,Mohammed V University in Rabat,Rabat,Morroco
| | - Wiame Lakhlili
- Biotechnology Laboratory (Medbiotech),BioInova Research center,Rabat Medical and Pharmacy School,Mohammed V University in Rabat,Rabat,Morroco
| | - Azeddine Ibrahimi
- Biotechnology Laboratory (Medbiotech),BioInova Research center,Rabat Medical and Pharmacy School,Mohammed V University in Rabat,Rabat,Morroco
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Abstract
Network-aided in silico approaches have been widely used for prediction of drug-target interactions and evaluation of drug safety to increase the clinical efficiency and productivity during drug discovery and development. Here we review the advances and new progress in this field and summarize the translational applications of several new network-aided in silico approaches we developed recently. In addition, we describe the detailed protocols for a network-aided drug repositioning infrastructure for identification of new targets for old drugs, failed drugs in clinical trials, and new chemical entities. These state-of-the-art network-aided in silico approaches have been used for the discovery and development of broad-acting and targeted clinical therapies for various complex diseases, in particular for oncology drug repositioning. In this chapter, the described network-aided in silico protocols are appropriate for target-centric drug repositioning to various complex diseases, but expertise is still necessary to perform the specific oncology projects based on the cancer targets of interest.
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Li Y, Bao M, Yang C, Chen J, Zhou S, Sun R, Wu C, Li X, Bao J. Computer-aided identification of a novel pyruvate kinase M2 activator compound. Cell Prolif 2018; 51:e12509. [PMID: 30133040 PMCID: PMC6528871 DOI: 10.1111/cpr.12509] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 06/28/2018] [Accepted: 07/03/2018] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVES The aim of this study was to obtain antitumour molecules targeting to activate PKM2 through adequate computational methods combined with biological activity experiments. METHODS The structure-based virtual screening was utilized to screen effective activator targeting PKM2 from ZINC database. Molecular dynamics simulations were performed to evaluate the stability of the small molecule-binding PKM2 complex systems. Then, cell survival experiments, glutaraldehyde crosslinking reaction, western blot, and qPCR experiments were used to detect the effects of top hits on various cancer cells and the targeting specificity of PKM2. RESULTS Two small molecules in 1,5-2H-pyrrole-dione were obtained after virtual screening. In vitro experiments demonstrated that ZINC08383544 specifically activated PKM2 and affected the expression of upstream and downstream genes of PKM2 during glycolysis, leading to the inhibition of tumour cell growth. These results indicate that ZINC08383544 conforms to the characteristics of PKM2 activator and is potential to be a novel PKM2 activator as antitumour drug. DISCUSSION This work proves that ZINC08383544 promotes the formation of PKM2 tetramer, effectively blocks PKM2 nuclear translocation, and inhibits the growth of tumour, and ZINC08383544 may be a novel activator of PKM2. This work may provide a good choice of drug or molecular fragments for the antitumour strategy targeting PKM2. Screening of targeted drugs by combination of virtual screening and bioactivity experiments is a rapid method for drug discovery.
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Affiliation(s)
- Yuanyuan Li
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
| | - Minyue Bao
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityChengduChina
| | - Chunlan Yang
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
| | - Jiao Chen
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
| | - Shu Zhou
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
- State Key Laboratory of Biotherapy/Collaborative Innovation Centre for BiotherapyWest China Hospital, Sichuan UniversityChengduChina
| | - Rong Sun
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
| | - Chuanfang Wu
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
| | - Xin Li
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityChengduChina
| | - Jinku Bao
- College of Life Sciences and Key Laboratory of Bio‐resources and Eco‐environmentMinistry of Education, State Key Laboratory of Biotherapy, Sichuan UniversityChengduChina
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan UniversityChengduChina
- State Key Laboratory of Biotherapy/Collaborative Innovation Centre for BiotherapyWest China Hospital, Sichuan UniversityChengduChina
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Wu Z, Li W, Liu G, Tang Y. Network-Based Methods for Prediction of Drug-Target Interactions. Front Pharmacol 2018; 9:1134. [PMID: 30356768 PMCID: PMC6189482 DOI: 10.3389/fphar.2018.01134] [Citation(s) in RCA: 116] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 09/18/2018] [Indexed: 01/10/2023] Open
Abstract
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.
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Affiliation(s)
| | | | | | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Abstract
Following the elucidation of the human genome, chemogenomics emerged in the beginning of the twenty-first century as an interdisciplinary research field with the aim to accelerate target and drug discovery by making best usage of the genomic data and the data linkable to it. What started as a systematization approach within protein target families now encompasses all types of chemical compounds and gene products. A key objective of chemogenomics is the establishment, extension, analysis, and prediction of a comprehensive SAR matrix which by application will enable further systematization in drug discovery. Herein we outline future perspectives of chemogenomics including the extension to new molecular modalities, or the potential extension beyond the pharma to the agro and nutrition sectors, and the importance for environmental protection. The focus is on computational sciences with potential applications for compound library design, virtual screening, hit assessment, analysis of phenotypic screens, lead finding and optimization, and systems biology-based prediction of toxicology and translational research.
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Affiliation(s)
- Edgar Jacoby
- Janssen Research & Development, Beerse, Belgium.
| | - J B Brown
- Life Science Informatics Research Unit, Laboratory of Molecular Biosciences, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Cheng F, Hong H, Yang S, Wei Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief Bioinform 2017; 18:682-697. [PMID: 27296652 DOI: 10.1093/bib/bbw051] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Indexed: 12/12/2022] Open
Abstract
Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.
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Wu Z, Cheng F, Li J, Li W, Liu G, Tang Y. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform 2017; 18:333-347. [PMID: 26944082 DOI: 10.1093/bib/bbw012] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Indexed: 01/11/2023] Open
Abstract
Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Feixiong Cheng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai, China
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Wu Z, Lu W, Yu W, Wang T, Li W, Liu G, Zhang H, Pang X, Huang J, Liu M, Cheng F, Tang Y. Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Pharmacol Res 2017; 129:400-413. [PMID: 29133212 DOI: 10.1016/j.phrs.2017.11.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 11/07/2017] [Accepted: 11/07/2017] [Indexed: 02/06/2023]
Abstract
G protein-coupled receptors (GPCRs) are the largest super family with more than 800 membrane receptors. Currently, over 30% of the approved drugs target human GPCRs. However, only approximately 30 human GPCRs have been resolved three-dimensional crystal structures, which limits traditional structure-based drug discovery. Recent advances in network-based systems pharmacology approaches have demonstrated powerful strategies for identifying new targets of GPCR ligands. In this study, we proposed a network-based systems pharmacology framework for comprehensive identification of new drug-target interactions on GPCRs. Specifically, we reconstructed both global and local drug-target interaction networks for human GPCRs. Network analysis on the known drug-target networks showed rational strategies for designing new GPCR ligands and evaluating side effects of the approved GPCR drugs. We further built global and local network-based models for predicting new targets of the known GPCR ligands. The area under the receiver operating characteristic curve of more than 0.96 was obtained for the best network-based models in cross validation. In case studies, we identified that several network-predicted GPCR off-targets (e.g. ADRA2A, ADRA2C and CHRM2) were associated with cardiovascular complications (e.g. bradycardia and palpitations) of the approved GPCR drugs via an integrative analysis of drug-target and off-target-adverse drug event networks. Importantly, we experimentally validated that two newly predicted compounds, AM966 and Ki16425, showed high binding affinities on prostaglandin E2 receptor EP4 subtype with IC50=2.67μM and 6.34μM, respectively. In summary, this study offers powerful network-based tools for identifying polypharmacology of GPCR ligands in drug discovery and development.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Weiwei Yu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Tianduanyi Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Hankun Zhang
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Xiufeng Pang
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China; Institute of Biosciences and Technology, Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, Houston, TX 77030, USA.
| | - Feixiong Cheng
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA; Center for Complex Networks Research, Northeastern University, Boston, MA 02115, USA.
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
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Fang J, Wu Z, Cai C, Wang Q, Tang Y, Cheng F. Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy. J Chem Inf Model 2017; 57:2657-2671. [PMID: 28956927 DOI: 10.1021/acs.jcim.7b00216] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
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Affiliation(s)
- Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237, China
| | - Chuipu Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237, China
| | - Feixiong Cheng
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts 02215, United States.,Center for Complex Networks Research (CCNR), Northeastern University , Boston, Massachusetts 02115, United States
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45
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Buza K, Peška L. Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.04.055] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Fang J, Wang L, Li Y, Lian W, Pang X, Wang H, Yuan D, Wang Q, Liu AL, Du GH. AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. PLoS One 2017; 12:e0178347. [PMID: 28542505 PMCID: PMC5460905 DOI: 10.1371/journal.pone.0178347] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 05/11/2017] [Indexed: 12/29/2022] Open
Abstract
Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.
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Affiliation(s)
- Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Encephalopathy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ling Wang
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China
| | - Yecheng Li
- Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Pre-Incubator for Innovative Drugs & Medicine, School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China
| | - Wenwen Lian
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Xiaocong Pang
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Hong Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dongsheng Yuan
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, China
- Department of Encephalopathy, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ai-Lin Liu
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Guan-Hua Du
- Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
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Fang J, Liu C, Wang Q, Lin P, Cheng F. In silico polypharmacology of natural products. Brief Bioinform 2017; 19:1153-1171. [DOI: 10.1093/bib/bbx045] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Indexed: 12/16/2022] Open
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He T, Heidemeyer M, Ban F, Cherkasov A, Ester M. SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J Cheminform 2017; 9:24. [PMID: 29086119 PMCID: PMC5395521 DOI: 10.1186/s13321-017-0209-z] [Citation(s) in RCA: 171] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 03/30/2017] [Indexed: 02/06/2023] Open
Abstract
Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug–target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug–target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets.
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Affiliation(s)
- Tong He
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Marten Heidemeyer
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
| | - Fuqiang Ban
- Faculty of Medicine, Vancouver Prostate Center, University of British Columbia, Vancouver, BC, V6H 3Z6, Canada
| | - Artem Cherkasov
- Faculty of Medicine, Vancouver Prostate Center, University of British Columbia, Vancouver, BC, V6H 3Z6, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
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Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction. PLoS Comput Biol 2016; 12:e1005219. [PMID: 27893735 PMCID: PMC5125559 DOI: 10.1371/journal.pcbi.1005219] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/21/2016] [Indexed: 12/23/2022] Open
Abstract
De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/. The emergence of multi-resistant bacterial strains and the existing void in the discovery and development of new classes of antibiotics is a growing concern. Indeed, some bacterial strains are now resistant to last-line antibiotics and considered untreatable. Drug repositioning has been suggested as a strategy to minimize time and cost expenses until the drug reaches the market, compared to traditional drug design. Drug-target interactions (DTIs) are the basis of rational drug design and thus, we proposed a computational approach to predict DTIs solely based on the primary sequence of the protein and the simplified molecular-input line-entry system of the ligand. In addition, network metrics are used to identify vital putative drug-targets in bacteria. Molecular docking experiments were performed to compare the binding affinities between a given ligand and a putative drug-target, as well as with their original targets. According to the docking results, the predicted DTIs have better or similar binding activities than the ligand and their real target, indicating the validity of the proposed model.
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Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, Li W, Liu G, Huang J, Cheng F, Tang Y. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol 2016; 173:3372-3385. [PMID: 27646592 DOI: 10.1111/bph.13629] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 08/26/2016] [Accepted: 09/10/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACH In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTS High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC50 or EC50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONS In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Anqi Luo
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hanping Bian
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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