1
|
Chen L, Gu R, Li Y, Liu H, Han W, Yan Y, Chen Y, Zhang Y, Jiang Y. Epigenetic target identification strategy based on multi-feature learning. J Biomol Struct Dyn 2024; 42:5946-5962. [PMID: 37827992 DOI: 10.1080/07391102.2023.2259511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 06/20/2023] [Indexed: 10/14/2023]
Abstract
The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Lingfeng Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Rui Gu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yuanyuan Li
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yingchao Yan
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| | - Yulei Jiang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, Nanjing, China
| |
Collapse
|
2
|
Singh S, Singh PK, Sachan K, Kumar M, Bhardwaj P. Automation of Drug Discovery through Cutting-edge In-silico Research in Pharmaceuticals: Challenges and Future Scope. Curr Comput Aided Drug Des 2024; 20:723-735. [PMID: 37807412 DOI: 10.2174/0115734099260187230921073932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/05/2023] [Accepted: 08/18/2023] [Indexed: 10/10/2023]
Abstract
The rapidity and high-throughput nature of in silico technologies make them advantageous for predicting the properties of a large array of substances. In silico approaches can be used for compounds intended for synthesis at the beginning of drug development when there is either no or very little compound available. In silico approaches can be used for impurities or degradation products. Quantifying drugs and related substances (RS) with pharmaceutical drug analysis (PDA) can also improve drug discovery (DD) by providing additional avenues to pursue. Potential future applications of PDA include combining it with other methods to make insilico predictions about drugs and RS. One possible outcome of this is a determination of the drug potential of nontoxic RS. ADME estimation, QSAR research, molecular docking, bioactivity prediction, and toxicity testing all involve impurity profiling. Before committing to DD, RS with minimal toxicity can be utilised in silico. The efficacy of molecular docking in getting a medication to market is still debated despite its refinement and improvement. Biomedical labs and pharmaceutical companies were hesitant to adopt molecular docking algorithms for drug screening despite their decades of development and improvement. Despite the widespread use of "force fields" to represent the energy exerted within and between molecules, it has been impossible to reliably predict or compute the binding affinities between proteins and potential binding medications.
Collapse
Affiliation(s)
- Smita Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Pranjal Kumar Singh
- Department of Pharmaceutics, SRM Modinagar College of Pharmacy, SRM Institute of Science and Technology, Delhi NCR Campus, Modinagar, Ghaziabad, India
| | - Kapil Sachan
- KIET School of Pharmacy, KIET Group of Institutions, Ghaziabad, India
| | - Mukesh Kumar
- IIMT College of Medical Sciences, IIMT University, Ganga Nagar, Meerut, India
| | - Poonam Bhardwaj
- NKBR College of Pharmacy and Research Center, Phaphunda, Meerut, India
| |
Collapse
|
3
|
Yin X, Wang X, Li Y, Wang J, Wang Y, Deng Y, Hou T, Liu H, Luo P, Yao X. CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules. J Chem Inf Model 2023; 63:6169-6176. [PMID: 37820365 DOI: 10.1021/acs.jcim.3c00685] [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: 10/13/2023]
Abstract
Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.
Collapse
Affiliation(s)
- Xiaodan Yin
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, 999078, China
- Carbon-Silicon AI Technology Co., Ltd, Zhejiang, Hangzhou 310018, China
| | - Xiaorui Wang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, 999078, China
- Carbon-Silicon AI Technology Co., Ltd, Zhejiang, Hangzhou 310018, China
| | - Yuquan Li
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Jike Wang
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Yuwei Wang
- College of Pharmacy, Shaanxi University of Chinese Medicine, Xianyang, 712000, China
| | - Yafeng Deng
- Carbon-Silicon AI Technology Co., Ltd, Zhejiang, Hangzhou 310018, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences and Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
| | - Pei Luo
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, 999078, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
| |
Collapse
|
4
|
Yu S, Zhong ZP, Fang Y, Patel RB, Li QP, Liu W, Li Z, Xu L, Sagona-Stophel S, Mer E, Thomas SE, Meng Y, Li ZP, Yang YZ, Wang ZA, Guo NJ, Zhang WH, Tranmer GK, Dong Y, Wang YT, Tang JS, Li CF, Walmsley IA, Guo GC. A universal programmable Gaussian boson sampler for drug discovery. NATURE COMPUTATIONAL SCIENCE 2023; 3:839-848. [PMID: 38177757 PMCID: PMC10768638 DOI: 10.1038/s43588-023-00526-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 09/01/2023] [Indexed: 01/06/2024]
Abstract
Gaussian boson sampling (GBS) has the potential to solve complex graph problems, such as clique finding, which is relevant to drug discovery tasks. However, realizing the full benefits of quantum enhancements requires large-scale quantum hardware with universal programmability. Here we have developed a time-bin-encoded GBS photonic quantum processor that is universal, programmable and software-scalable. Our processor features freely adjustable squeezing parameters and can implement arbitrary unitary operations with a programmable interferometer. Leveraging our processor, we successfully executed clique finding on a 32-node graph, achieving approximately twice the success probability compared to classical sampling. As proof of concept, we implemented a versatile quantum drug discovery platform using this GBS processor, enabling molecular docking and RNA-folding prediction tasks. Our work achieves GBS circuitry with its universal and programmable architecture, advancing GBS toward use in real-world applications.
Collapse
Affiliation(s)
- Shang Yu
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou, People's Republic of China.
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK.
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China.
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China.
| | - Zhi-Peng Zhong
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yuhua Fang
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Raj B Patel
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK.
| | - Qing-Peng Li
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Wei Liu
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Zhenghao Li
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK
| | - Liang Xu
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Steven Sagona-Stophel
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK
| | - Ewan Mer
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK
| | - Sarah E Thomas
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK
| | - Yu Meng
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Zhi-Peng Li
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Yuan-Ze Yang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Zhao-An Wang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Nai-Jie Guo
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Wen-Hao Zhang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
| | - Geoffrey K Tranmer
- College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Ying Dong
- Research Center for Quantum Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Yi-Tao Wang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China.
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China.
| | - Jian-Shun Tang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China.
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, China.
| | - Chuan-Feng Li
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China.
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China.
- Hefei National Laboratory, University of Science and Technology of China, Hefei, China.
| | - Ian A Walmsley
- Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, London, UK
| | - Guang-Can Guo
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, China
- CAS Center For Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei, China
| |
Collapse
|
5
|
Zhang DY, Cui WQ, Hou L, Yang J, Lyu LY, Wang ZY, Linghu KG, He WB, Yu H, Hu YJ. Expanding potential targets of herbal chemicals by node2vec based on herb-drug interactions. Chin Med 2023; 18:64. [PMID: 37264453 PMCID: PMC10233865 DOI: 10.1186/s13020-023-00763-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 05/01/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND The identification of chemical-target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification. METHODS Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein-protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification. RESULTS Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets "CTC, CCC, PPI". Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively. CONCLUSIONS The analytical framework and methods established in the study provide an important reference for researchers in discovering herb-drug interactions, alerting clinical risks, and understanding complex mechanisms of TM.
Collapse
Affiliation(s)
- Dai-Yan Zhang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Wen-Qing Cui
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Ling Hou
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Jing Yang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Li-Yang Lyu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Ze-Yu Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Ke-Gang Linghu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Wen-Bin He
- Shanxi Key Laboratory of Chinese Medicine Encephalopathy, Shanxi University of Chinese Medicine, Taiyuan, China
| | - Hua Yu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China
| | - Yuan-Jia Hu
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, 999078, Macao, China.
- DPM, Faculty of Health Sciences, University of Macau, Macao, China.
| |
Collapse
|
6
|
Nchourupouo KWT, Nde J, Ngouongo YJW, Zekeng SS, Fongang B. Evolutionary Couplings and Molecular Dynamic Simulations Highlight Details of GPCRs Heterodimers' Interfaces. Molecules 2023; 28:1838. [PMID: 36838825 PMCID: PMC9966702 DOI: 10.3390/molecules28041838] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 02/18/2023] Open
Abstract
A growing body of evidence suggests that only a few amino acids ("hot-spots") at the interface contribute most of the binding energy in transient protein-protein interactions. However, experimental protocols to identify these hot-spots are highly labor-intensive and expensive. Computational methods, including evolutionary couplings, have been proposed to predict the hot-spots, but they generally fail to provide details of the interacting amino acids. Here we showed that unbiased evolutionary methods followed by biased molecular dynamic simulations could achieve this goal and reveal critical elements of protein complexes. We applied the methodology to selected G-protein coupled receptors (GPCRs), known for their therapeutic properties. We used the structure-prior-assisted direct coupling analysis (SP-DCA) to predict the binding interfaces of A2aR/D2R, CB1R/D2R, A2aR/CB1R, 5HT2AR/D2R, and 5-HT2AR/mGluR2 receptor heterodimers, which all agreed with published data. In order to highlight details of the interactions, we performed molecular dynamic (MD) simulations using the newly developed AWSEM energy model. We found that these receptors interact primarily through critical residues at the C and N terminal domains and the third intracellular loop (ICL3). The MD simulations showed that these residues are energetically necessary for dimerization and revealed their native conformational state. We subsequently applied the methodology to the 5-HT2AR/5-HTR4R heterodimer, given its implication in drug addiction and neurodegenerative pathologies such as Alzheimer's disease (AD). Further, the SP-DCA analysis showed that 5-HT2AR and 5-HTR4R heterodimerize through the C-terminal domain of 5-HT2AR and ICL3 of 5-HT4R. However, elucidating the details of GPCR interactions would accelerate the discovery of druggable sites and improve our knowledge of the etiology of common diseases, including AD.
Collapse
Affiliation(s)
- Karim Widad Temgbet Nchourupouo
- Laboratory of Mechanics, Materials, and Structures, Department of Physics, Faculty of Science, University of Yaoundé I, Yaoundé P.O. Box 812, Cameroon
| | - Jules Nde
- Department of Physics, University of Washington Seattle, Seattle, WA 98105, USA
| | - Yannick Joel Wadop Ngouongo
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Serge Sylvain Zekeng
- Laboratory of Mechanics, Materials, and Structures, Department of Physics, Faculty of Science, University of Yaoundé I, Yaoundé P.O. Box 812, Cameroon
| | - Bernard Fongang
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Biochemistry and Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| |
Collapse
|
7
|
Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
Collapse
Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| |
Collapse
|
8
|
Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
Collapse
Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
| |
Collapse
|
9
|
DNA, a target of mixed chelate copper(II) compounds (Casiopeinas®) studied by electrophoresis, UV–vis and circular dichroism techniques. J Inorg Biochem 2022; 231:111772. [DOI: 10.1016/j.jinorgbio.2022.111772] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/15/2022] [Accepted: 02/19/2022] [Indexed: 11/22/2022]
|
10
|
Antifungal Activity of N-(4-Halobenzyl)amides against Candida spp. and Molecular Modeling Studies. Int J Mol Sci 2021; 23:ijms23010419. [PMID: 35008845 PMCID: PMC8745543 DOI: 10.3390/ijms23010419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/08/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022] Open
Abstract
Fungal infections remain a high-incidence worldwide health problem that is aggravated by limited therapeutic options and the emergence of drug-resistant strains. Cinnamic and benzoic acid amides have previously shown bioactivity against different species belonging to the Candida genus. Here, 20 cinnamic and benzoic acid amides were synthesized and tested for inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019. Five compounds inhibited the Candida strains tested, with compound 16 (MIC = 7.8 µg/mL) producing stronger antifungal activity than fluconazole (MIC = 16 µg/mL) against C. krusei ATCC 14243. It was also tested against eight Candida strains, including five clinical strains resistant to fluconazole, and showed an inhibitory effect against all strains tested (MIC = 85.3–341.3 µg/mL). The MIC value against C. krusei ATCC 6258 was 85.3 mcg/mL, while against C. krusei ATCC 14243, it was 10.9 times smaller. This strain had greater sensitivity to the antifungal action of compound 16. The inhibition of C. krusei ATCC 14243 and C. parapsilosis ATCC 22019 was also achieved by compounds 2, 9, 12, 14 and 15. Computational experiments combining target fishing, molecular docking and molecular dynamics simulations were performed to study the potential mechanism of action of compound 16 against C. krusei. From these, a multi-target mechanism of action is proposed for this compound that involves proteins related to critical cellular processes such as the redox balance, kinases-mediated signaling, protein folding and cell wall synthesis. The modeling results might guide future experiments focusing on the wet-lab investigation of the mechanism of action of this series of compounds, as well as on the optimization of their inhibitory potency.
Collapse
|
11
|
Castro LHE, Sant'Anna CMR. Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications. Curr Top Med Chem 2021; 22:333-346. [PMID: 34844540 DOI: 10.2174/1568026621666211129140958] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/23/2021] [Accepted: 10/28/2021] [Indexed: 11/22/2022]
Abstract
Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.
Collapse
Affiliation(s)
| | - Carlos Mauricio R Sant'Anna
- Programa de Pós-Graduação em Química, Instituto de Química, Universidade Federal Rural do Rio de Janeiro, Seropédica. Brazil
| |
Collapse
|
12
|
MacKinnon SS, Madani Tonekaboni SA, Windemuth A. Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications. Curr Protoc 2021; 1:e302. [PMID: 34794211 DOI: 10.1002/cpz1.302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.
Collapse
|
13
|
Li M, Hu J, Wang Y, Li Y, Zhang L, Liu Z. Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation. Mol Inform 2021; 41:e2100063. [PMID: 34787366 DOI: 10.1002/minf.202100063] [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: 08/16/2021] [Accepted: 10/15/2021] [Indexed: 11/08/2022]
Abstract
As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.
Collapse
Affiliation(s)
- Mingna Li
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Yanxing Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Yibo Li
- Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road 5, Haidian District, Beijing, P.R. China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, 100191, Beijing, P.R. China
| |
Collapse
|
14
|
Shaker B, Ahmad S, Lee J, Jung C, Na D. In silico methods and tools for drug discovery. Comput Biol Med 2021; 137:104851. [PMID: 34520990 DOI: 10.1016/j.compbiomed.2021.104851] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/05/2021] [Accepted: 09/05/2021] [Indexed: 12/28/2022]
Abstract
In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.
Collapse
Affiliation(s)
- Bilal Shaker
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Sajjad Ahmad
- Department of Health and Biological Sciences, Abasyn University, Peshawar, 25000, Pakistan
| | - Jingyu Lee
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Chanjin Jung
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea
| | - Dokyun Na
- Department of Biomedical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul, 06974, Republic of Korea.
| |
Collapse
|
15
|
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.
Collapse
|
16
|
MUHAMMED MT, AKI-YALCIN E. Pharmacophore Modeling in Drug Discovery: Methodology and Current Status. JOURNAL OF THE TURKISH CHEMICAL SOCIETY, SECTION A: CHEMISTRY 2021. [DOI: 10.18596/jotcsa.927426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
|
17
|
Shiuan D, Tai DF, Huang KJ, Yu Z, Ni F, Li J. Target-based discovery of therapeutic agents from food ingredients. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
18
|
Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4516250. [PMID: 32908888 PMCID: PMC7463380 DOI: 10.1155/2020/4516250] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 08/02/2020] [Accepted: 08/10/2020] [Indexed: 12/02/2022]
Abstract
Identifying the drug-target interactions (DTIs) plays an essential role in new drug development. However, there still has the limited knowledge of DTIs and a significant number of unknown DTI pairs. Moreover, the traditional experimental methods have inevitable disadvantages such as high cost and time-consuming. Therefore, developing computational methods for predicting DTIs is attracting more and more attention. In this study, we report a novel computational approach for predicting DTI using GIST feature, position-specific scoring matrix (PSSM), and rotation forest (RF). Specifically, each target protein is first converted into a PSSM for retaining evolutionary information. Then, the GIST feature is extracted from PSSM and substructure fingerprint information is adopted to extract the feature of the drug. Finally, combining each protein and drug features to form a new drug-target pair, which is employed as input feature for RF classifier. In the experiment, the proposed method achieves high average accuracies of 89.25%, 85.93%, 82.36%, and 73.89% on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively. For further evaluating the prediction performance of the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the same golden standard dataset. These promising results illustrate that the proposed method is more effective and stable than other methods. We expect the proposed method to be a useful tool for predicting large-scale DTIs.
Collapse
|
19
|
Djikic T, Vucicevic J, Laurila J, Radi M, Veljkovic N, Xhaard H, Nikolic K. Deciphering Imidazoline Off‐targets by Fishing in the Class A of GPCR field. Mol Inform 2020; 39:e1900165. [DOI: 10.1002/minf.201900165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 02/20/2020] [Indexed: 12/13/2022]
Affiliation(s)
- Teodora Djikic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jelica Vucicevic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| | - Jonne Laurila
- Research Center for Integrative Physiology and Pharmacology, Institute of BiomedicineUniversity of Turku FI-20014 Turun yliopisto, Turku Finland
| | - Marco Radi
- Dipartimento di Scienze degli Alimenti e del FarmacoUniversità degli Studi di Parma Viale delle Scienze, 27/A 43124 Parma Italy
| | - Nevena Veljkovic
- Laboratory for bioinformatics and computational chemistry, Institute of Nuclear Sciences VincaUniversity of Belgrade Mihaila Petrovica Alasa 14 11001 Belgrade Serbia
| | - Henri Xhaard
- Division of Pharmaceutical Chemistry, Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of PharmacyUniversity of Helsinki P.O. Box 56 FI-00014 Helsinki Finland
| | - Katarina Nikolic
- Department of Pharmaceutical ChemistryFaculty of PharmacyUniversity of Belgrade Vojvode Stepe 450 11000 Belgrade Serbia
| |
Collapse
|
20
|
Mirza MU, Vanmeert M, Ali A, Iman K, Froeyen M, Idrees M. Perspectives towards antiviral drug discovery against Ebola virus. J Med Virol 2019; 91:2029-2048. [PMID: 30431654 PMCID: PMC7166701 DOI: 10.1002/jmv.25357] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/04/2018] [Indexed: 12/18/2022]
Abstract
Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.
Collapse
Affiliation(s)
- Muhammad Usman Mirza
- Department of Pharmaceutical Sciences, REGA Institute for Medical Research, Medicinal Chemistry, KU Leuven, Leuven, Belgium
| | - Michiel Vanmeert
- Department of Pharmaceutical Sciences, REGA Institute for Medical Research, Medicinal Chemistry, KU Leuven, Leuven, Belgium
| | - Amjad Ali
- Department of Genetics, Hazara University, Mansehra, Pakistan.,Molecular Virology Laboratory, Centre for Applied Molecular Biology (CAMB), University of the Punjab, Lahore, Pakistan
| | - Kanzal Iman
- Biomedical Informatics Research Laboratory (BIRL), Department of Biology, Lahore University of Management Sciences (LUMS), Lahore, Pakistan
| | - Matheus Froeyen
- Department of Pharmaceutical Sciences, REGA Institute for Medical Research, Medicinal Chemistry, KU Leuven, Leuven, Belgium
| | - Muhammad Idrees
- Molecular Virology Laboratory, Centre for Applied Molecular Biology (CAMB), University of the Punjab, Lahore, Pakistan.,Hazara University Mansehra, Khyber Pakhtunkhwa Pakistan
| |
Collapse
|
21
|
Cicaloni V, Trezza A, Pettini F, Spiga O. Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions. Curr Top Med Chem 2019; 19:534-554. [PMID: 30836920 DOI: 10.2174/1568026619666190304153901] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 01/02/2019] [Accepted: 01/25/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention. OBJECTIVE Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases. METHODS Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures. RESULTS In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules. CONCLUSION A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.
Collapse
Affiliation(s)
- Vittoria Cicaloni
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy.,Toscana Life Sciences Foundation, via Fiorentina 1, 53100 Siena, Italy
| | - Alfonso Trezza
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
| | - Francesco Pettini
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy (Dept. of Excellence 2018-2022), University of Siena, via A. Moro 2, 53100 Siena, Italy
| |
Collapse
|
22
|
Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| |
Collapse
|
23
|
Lapillo M, Tuccinardi T, Martinelli A, Macchia M, Giordano A, Poli G. Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies. Int J Mol Sci 2019; 20:ijms20051023. [PMID: 30818741 PMCID: PMC6429110 DOI: 10.3390/ijms20051023] [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: 01/31/2019] [Revised: 02/21/2019] [Accepted: 02/22/2019] [Indexed: 01/03/2023] Open
Abstract
The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.
Collapse
Affiliation(s)
| | | | | | - Marco Macchia
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.
| | - Antonio Giordano
- Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, College of Science and Technology, Temple University, Philadelphia, PA 19122, USA.
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy.
| | - Giulio Poli
- Department of Pharmacy, University of Pisa, 56126 Pisa, Italy.
| |
Collapse
|
24
|
Nogueira MS, Koch O. The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction. J Chem Inf Model 2019; 59:1238-1252. [DOI: 10.1021/acs.jcim.8b00773] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Mauro S. Nogueira
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, 44227, Dortmund, Germany
| |
Collapse
|
25
|
Pabon NA, Xia Y, Estabrooks SK, Ye Z, Herbrand AK, Süß E, Biondi RM, Assimon VA, Gestwicki JE, Brodsky JL, Camacho CJ, Bar-Joseph Z. Predicting protein targets for drug-like compounds using transcriptomics. PLoS Comput Biol 2018; 14:e1006651. [PMID: 30532261 PMCID: PMC6300300 DOI: 10.1371/journal.pcbi.1006651] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 12/19/2018] [Accepted: 11/13/2018] [Indexed: 01/07/2023] Open
Abstract
An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions. Bioactive compounds often disrupt cellular gene expression in ways that are difficult to predict. While the correlation between a cellular response after treatment with a small molecule and the knockdown of its target protein should be simple to establish, in practice this goal has been difficult to achieve. The main challenges are that data are noisy, drugs are not intended to be active in all cell types, and signals from a bona fide target(s) may be obscured by correlations with knockdowns of other proteins in the same pathway(s). Here, we find that a random forest classification model can detect meaningful correlational patterns when gene expression profiles after compound treatment and gene knockdowns in four or more cell lines are compared. When this approach is enriched by a structure-based screen, novel drug-target interactions can be predicted. We then validated new ligand-protein interactions for four difficult targets. Although the initial compounds are not especially potent in vitro, they are capable of disrupting their target pathway in the cell to an extent that generates a significant and characteristic gene expression profile. Collectively, our studies provide insight on cell-level transcriptomic responses to pharmaceutical intervention and the use of these patterns for target identification. In addition, the method provides a novel drug discovery pipeline to test chemistries without a priori knowledge of their target(s).
Collapse
Affiliation(s)
- Nicolas A. Pabon
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Yan Xia
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Samuel K. Estabrooks
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Zhaofeng Ye
- School of Medicine, Tsinghua University, Beijing, China
| | - Amanda K. Herbrand
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Evelyn Süß
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Ricardo M. Biondi
- Department of Internal Medicine I, Universitätsklinikum Frankfurt, Frankfurt, Germany
| | - Victoria A. Assimon
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jason E. Gestwicki
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, United States of America
| | - Jeffrey L. Brodsky
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Carlos J. Camacho
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
| | - Ziv Bar-Joseph
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail: (CJC); (ZBJ)
| |
Collapse
|
26
|
Aleksandrov A, Myllykallio H. Advances and challenges in drug design against tuberculosis: application of in silico approaches. Expert Opin Drug Discov 2018; 14:35-46. [PMID: 30477360 DOI: 10.1080/17460441.2019.1550482] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) remains the deadliest infectious disease in the world with one-third of the world's population thought to be infected. Over the years, TB mortality rate has been largely reduced; however, this progress has been threatened by the increasing appearance of multidrug-resistant Mtb. Considerable recent efforts have been undertaken to develop new generation antituberculosis drugs. Many of these attempts have relied on in silico approaches, which have emerged recently as powerful tools complementary to biochemical attempts. Areas covered: The authors review the status of pharmaceutical drug development against TB with a special emphasis on computational work. They focus on those studies that have been validated by in vitro and/or in vivo experiments, and thus, that can be considered as successful. The major goals of this review are to present target protein systems, to highlight how in silico efforts compliment experiments, and to aid future drug design endeavors. Expert opinion: Despite having access to all of the gene and protein sequences of Mtb, the search for new optimal treatments against this deadly pathogen are still ongoing. Together with the geometric growth of protein structural and sequence databases, computational methods have become a powerful technique accelerating the successful identification of new ligands.
Collapse
Affiliation(s)
- Alexey Aleksandrov
- a Laboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182) , Ecole Polytechnique , Palaiseau , France
| | - Hannu Myllykallio
- a Laboratoire d'Optique et Biosciences (CNRS UMR7645, INSERM U1182) , Ecole Polytechnique , Palaiseau , France
| |
Collapse
|
27
|
Muhammad N, Ali Shah N, Ali S, Wadood A, Ghufran M, Rashid Khan M, Siddiq P, Shujah S, Meetsma A. Antimicrobial efficiency of diorganotin(IV) bis-[3-(4-chlorophenyl)-2-methylacrylate]. J COORD CHEM 2018. [DOI: 10.1080/00958972.2018.1513131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Niaz Muhammad
- Department of Chemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Naseer Ali Shah
- Department of Biosciences, COMSATS University, Islamabad, Pakistan
| | - Saqib Ali
- Department of Chemistry, Quaid-I-Azam University, Islamabad, Pakistan
| | - Abdul Wadood
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | - Mehreen Ghufran
- Department of Biochemistry, Abdul Wali Khan University Mardan, Mardan, Pakistan
| | | | - Pakiza Siddiq
- Department of Biochemistry, Quaid-I-Azam University, Islamabad, Pakistan
| | - Shaukat Shujah
- Department of Chemistry, Kohat University of Science & Technology, Kohat, Pakistan
| | - Auke Meetsma
- Crystal Structure Center, Chemical Physics, Materials Science Center, University of Groningen, Groningen, The Netherlands
| |
Collapse
|
28
|
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: 101] [Impact Index Per Article: 16.8] [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.
Collapse
Affiliation(s)
| | | | | | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| |
Collapse
|
29
|
Symmetrical and unsymmetrical substituted 2,5-diarylidene cyclohexanones as anti-parasitic compounds. Eur J Med Chem 2018; 155:596-608. [DOI: 10.1016/j.ejmech.2018.06.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 03/07/2018] [Accepted: 06/12/2018] [Indexed: 12/21/2022]
|
30
|
Huang H, Zhang G, Zhou Y, Lin C, Chen S, Lin Y, Mai S, Huang Z. Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds. Front Chem 2018; 6:138. [PMID: 29868550 PMCID: PMC5954125 DOI: 10.3389/fchem.2018.00138] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 04/09/2018] [Indexed: 12/13/2022] Open
Abstract
This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.
Collapse
Affiliation(s)
- Hongbin Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Guigui Zhang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Yuquan Zhou
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Chenru Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Suling Chen
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Yutong Lin
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| | - Shangkang Mai
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,The Second School of Clinical Medicine, Guangdong Medical University Dongguan, China
| | - Zunnan Huang
- Key Laboratory for Medical Molecular Diagnostics of Guangdong Province, Dongguan Scientific Research Center, Guangdong Medical University Dongguan, China.,School of Pharmacy, Guangdong Medical University Dongguan, China
| |
Collapse
|
31
|
Meng F, Murray GF, Kurgan L, Donahue HJ. Functional and structural characterization of osteocytic MLO-Y4 cell proteins encoded by genes differentially expressed in response to mechanical signals in vitro. Sci Rep 2018; 8:6716. [PMID: 29712973 PMCID: PMC5928037 DOI: 10.1038/s41598-018-25113-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/09/2018] [Indexed: 12/29/2022] Open
Abstract
The anabolic response of bone to mechanical load is partially the result of osteocyte response to fluid flow-induced shear stress. Understanding signaling pathways activated in osteocytes exposed to fluid flow could identify novel signaling pathways involved in the response of bone to mechanical load. Bioinformatics allows for a unique perspective and provides key first steps in understanding these signaling pathways. We examined proteins encoded by genes differentially expressed in response to fluid flow in murine osteocytic MLO-Y4 cells. We considered structural and functional characteristics including putative intrinsic disorder, evolutionary conservation, interconnectedness in protein-protein interaction networks, and cellular localization. Our analysis suggests that proteins encoded by fluid flow activated genes have lower than expected conservation, are depleted in intrinsic disorder, maintain typical levels of connectivity for the murine proteome, and are found in the cytoplasm and extracellular space. Pathway analyses reveal that these proteins are associated with cellular response to stress, chemokine and cytokine activity, enzyme binding, and osteoclast differentiation. The lower than expected disorder of proteins encoded by flow activated genes suggests they are relatively specialized.
Collapse
Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Graeme F Murray
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, Virginia, United States of America.
| | - Henry J Donahue
- Bone Engineering, Science and Technology (BEST) Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, Virginia, United States of America.
| |
Collapse
|
32
|
Kooistra AJ, Vass M, McGuire R, Leurs R, de Esch IJP, Vriend G, Verhoeven S, de Graaf C. 3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery. ChemMedChem 2018; 13:614-626. [PMID: 29337438 PMCID: PMC5900740 DOI: 10.1002/cmdc.201700754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/11/2018] [Indexed: 01/06/2023]
Abstract
eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.
Collapse
Affiliation(s)
- Albert J. Kooistra
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Márton Vass
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Ross McGuire
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- BioAxis Research, Pivot ParkOssThe Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
| | | | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| |
Collapse
|
33
|
Xu X, Huang M, Zou X. Docking-based inverse virtual screening: methods, applications, and challenges. BIOPHYSICS REPORTS 2018; 4:1-16. [PMID: 29577065 PMCID: PMC5860130 DOI: 10.1007/s41048-017-0045-8] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 09/08/2017] [Indexed: 01/09/2023] Open
Abstract
Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.
Collapse
Affiliation(s)
- Xianjin Xu
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
| | - Marshal Huang
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
| | - Xiaoqin Zou
- Dalton Cardiovascular Research Center, University of Missouri, Columbia, MO 65211 USA
- Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211 USA
- Informatics Institute, University of Missouri, Columbia, MO 65211 USA
- Department of Biochemistry, University of Missouri, Columbia, MO 65211 USA
| |
Collapse
|
34
|
Myrianthopoulos V, Lambrinidis G, Mikros E. In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies. Methods Mol Biol 2018; 1824:261-277. [PMID: 30039412 DOI: 10.1007/978-1-4939-8630-9_15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A number of diverse approaches for efficient screening of compound collections in silico are nowadays available, each with their own methodological background, successes and limitations. Implementation of such virtual screening methods has enabled an impressive acceleration in the search toward the most biologically relevant regions of chemical space and has greatly facilitated the discovery of novel biologically active molecules. It is noteworthy that the range of principles on which the available virtual screening methodologies are based is wide enough for several of these methods to be considered as orthogonal to a good extent. We hereby propose a simple and extensible protocol aiming at integrating the diverse information derived by such virtual screening methods in a consensus manner that can achieve an improvement of the hit rate obtained by individual use of those methods. The protocol can be performed in its basic version as described in this work, but it can also be extended manually by integrating a number of different screening tools and their case-specific variations to further increase the performance of virtual screening in prioritizing the most promising compounds for in vitro evaluations.
Collapse
Affiliation(s)
- Vassilios Myrianthopoulos
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens, Greece.,"Athena" Research and Innovation Center, Athens, Greece
| | - George Lambrinidis
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens, Greece
| | - Emmanuel Mikros
- Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, Athens, Greece. .,"Athena" Research and Innovation Center, Athens, Greece.
| |
Collapse
|
35
|
From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library. J Biotechnol 2017; 261:207-214. [DOI: 10.1016/j.jbiotec.2017.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 05/31/2017] [Accepted: 06/07/2017] [Indexed: 02/06/2023]
|
36
|
Roca C, Sebastián-Pérez V, Campillo NE. In silico Tools for Target Identification and Drug Molecular Docking in Leishmania. DRUG DISCOVERY FOR LEISHMANIASIS 2017. [DOI: 10.1039/9781788010177-00130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.
Collapse
Affiliation(s)
- Carlos Roca
- Centro de Investigaciones Biológicas (CSIC) Ramiro de Maeztu 9 28040 Madrid Spain
| | | | - Nuria E. Campillo
- Centro de Investigaciones Biológicas (CSIC) Ramiro de Maeztu 9 28040 Madrid Spain
| |
Collapse
|
37
|
|
38
|
The potential role of in silico approaches to identify novel bioactive molecules from natural resources. Future Med Chem 2017; 9:1665-1686. [PMID: 28841048 DOI: 10.4155/fmc-2017-0124] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].
Collapse
|
39
|
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.
Collapse
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
| |
Collapse
|
40
|
Luo Q, Zhao L, Hu J, Jin H, Liu Z, Zhang L. The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing. PLoS One 2017; 12:e0171433. [PMID: 28196116 PMCID: PMC5308821 DOI: 10.1371/journal.pone.0171433] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2016] [Accepted: 01/20/2017] [Indexed: 12/28/2022] Open
Abstract
Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.
Collapse
Affiliation(s)
- Qiyao Luo
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Liang Zhao
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Jianxing Hu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Hongwei Jin
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, P. R. China
- * E-mail: (ZL); (LZ)
| |
Collapse
|
41
|
Abstract
Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.
Collapse
Affiliation(s)
- Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 74 route du Rhin, 67400 Illkirch, France.
| |
Collapse
|
42
|
Maia EHB, Campos VA, Dos Reis Santos B, Costa MS, Lima IG, Greco SJ, Ribeiro RIMA, Munayer FM, da Silva AM, Taranto AG. Octopus: a platform for the virtual high-throughput screening of a pool of compounds against a set of molecular targets. J Mol Model 2017; 23:26. [PMID: 28064377 DOI: 10.1007/s00894-016-3184-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Accepted: 12/06/2016] [Indexed: 01/28/2023]
Abstract
Octopus is an automated workflow management tool that is scalable for virtual high-throughput screening (vHTS). It integrates MOPAC2016, MGLTools, PyMOL, and AutoDock Vina. In contrast to other platforms, Octopus can perform docking simulations of an unlimited number of compounds into a set of molecular targets. After generating the ligands in a drawing package in the Protein Data Bank (PDB) format, Octopus can carry out geometry refinement using the semi-empirical method PM7 implemented in MOPAC2016. Docking simulations can be performed using AutoDock Vina and can utilize the Our Own Molecular Targets (OOMT) databank. Finally, the proposed software compiles the best binding energies into a standard table. Here, we describe two successful case studies that were verified by biological assay. In the first case study, the vHTS process was carried out for 22 (phenylamino)urea derivatives. The vHTS process identified a metalloprotease with the PDB code 1GKC as a molecular target for derivative LE&007. In a biological assay, compound LE&007 was found to inhibit 80% of the activity of this enzyme. In the second case study, compound Tx001 was submitted to the Octopus routine, and the results suggested that Plasmodium falciparum ATP6 (PfATP6) as a molecular target for this compound. Following an antimalarial assay, Tx001 was found to have an inhibitory concentration (IC50) of 8.2 μM against PfATP6. These successful examples illustrate the utility of this software for finding appropriate molecular targets for compounds. Hits can then be identified and optimized as new antineoplastic and antimalarial drugs. Finally, Octopus has a friendly Linux-based user interface, and is available at www.drugdiscovery.com.br . Graphical Abstract Octopus: A platform for inverse virtual screening (IVS) to search new molecular targets for drugs.
Collapse
Affiliation(s)
- Eduardo Habib Bechelane Maia
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
- Centro Federal de Educação Tecnológica de Minas Gerais-Campus Divinópolis, Divinópolis, MG, Brazil
| | - Vinícius Alves Campos
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
- Centro Federal de Educação Tecnológica de Minas Gerais-Campus Divinópolis, Divinópolis, MG, Brazil
| | - Bianca Dos Reis Santos
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
- Centro Federal de Educação Tecnológica de Minas Gerais-Campus Divinópolis, Divinópolis, MG, Brazil
| | - Marina Santos Costa
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
- Centro Federal de Educação Tecnológica de Minas Gerais-Campus Divinópolis, Divinópolis, MG, Brazil
| | - Iann Gabriel Lima
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
- Centro Federal de Educação Tecnológica de Minas Gerais-Campus Divinópolis, Divinópolis, MG, Brazil
| | - Sandro J Greco
- Universidade Federal do Espírito Santo-UFES, Vitória, ES, Brazil
| | - Rosy I M A Ribeiro
- Universidade Federal de São João del Rei-Campus Centro-Oeste, Divinópolis, MG, Brazil
| | - Felipe M Munayer
- Universidade Federal do Espírito Santo-UFES, Vitória, ES, Brazil
| | | | | |
Collapse
|
43
|
Wang Y, Fu X, Xu J, Wang Q, Kuang H. Systems pharmacology to investigate the interaction of berberine and other drugs in treating polycystic ovary syndrome. Sci Rep 2016; 6:28089. [PMID: 27306862 PMCID: PMC4910093 DOI: 10.1038/srep28089] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 05/26/2016] [Indexed: 12/11/2022] Open
Abstract
Polycystic ovary syndrome (PCOS) is a common multifactorial endocrine disorder among women of childbearing age. PCOS has various and heterogeneous clinical features apart from its indefinite pathogenesis and mechanism. Clinical drugs for PCOS are multifarious because it only treats separate symptoms. Berberine is an isoquinoline plant alkaloid with numerous biological activities, and it was testified to improve some diseases related to PCOS in animal models and in humans. Systems pharmacology was utilized to predict the potential targets of berberine related to PCOS and the potential drug-drug interaction base on the disease network. In conclusion, berberine is a promising polypharmacological drug for treating PCOS, and for enhancing the efficacy of clinical drugs.
Collapse
Affiliation(s)
- Yu Wang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Xin Fu
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Jing Xu
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Qiuhong Wang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| | - Haixue Kuang
- Key Laboratory of Chinese Materia Medica (Ministry of Education), Heilongjiang University of Chinese Medicine, 150040, Harbin, P.R. China
| |
Collapse
|
44
|
Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 2016; 11:707-15. [PMID: 27186904 DOI: 10.1080/17460441.2016.1190706] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. AREAS COVERED In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. EXPERT OPINION There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.
Collapse
Affiliation(s)
- Aeri Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Kyoungyeul Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Dongsup Kim
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| |
Collapse
|
45
|
Katsila T, Spyroulias GA, Patrinos GP, Matsoukas MT. Computational approaches in target identification and drug discovery. Comput Struct Biotechnol J 2016; 14:177-84. [PMID: 27293534 PMCID: PMC4887558 DOI: 10.1016/j.csbj.2016.04.004] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 04/21/2016] [Accepted: 04/25/2016] [Indexed: 12/31/2022] Open
Abstract
In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.
Collapse
Affiliation(s)
- Theodora Katsila
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
| | - Georgios A. Spyroulias
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
| | - George P. Patrinos
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
- Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Minos-Timotheos Matsoukas
- University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece
| |
Collapse
|
46
|
Lavecchia A, Cerchia C. In silico methods to address polypharmacology: current status, applications and future perspectives. Drug Discov Today 2015; 21:288-98. [PMID: 26743596 DOI: 10.1016/j.drudis.2015.12.007] [Citation(s) in RCA: 131] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 11/20/2015] [Accepted: 12/21/2015] [Indexed: 12/15/2022]
Abstract
Polypharmacology, a new paradigm in drug discovery that focuses on multi-target drugs (MTDs), has potential application for drug repurposing, the process of finding new uses for existing approved drugs, prediction of off-target toxicities and rational design of MTDs. In this scenario, computational strategies have demonstrated great potential in predicting polypharmacology and in facilitating drug repurposing. Here, we provide a comprehensive overview of various computational approaches that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes and outline their potential for drug discovery. We give an outlook on the latest advances in rational design of MTDs and discuss possible future directions of algorithm development in this field.
Collapse
Affiliation(s)
- Antonio Lavecchia
- Department of Pharmacy, Drug Discovery Laboratory, University of Napoli Federico II, via D. Montesano 49, I-80131 Napoli, Italy.
| | - Carmen Cerchia
- Department of Pharmacy, Drug Discovery Laboratory, University of Napoli Federico II, via D. Montesano 49, I-80131 Napoli, Italy
| |
Collapse
|
47
|
Mervin LH, Afzal AM, Drakakis G, Lewis R, Engkvist O, Bender A. Target prediction utilising negative bioactivity data covering large chemical space. J Cheminform 2015; 7:51. [PMID: 26500705 PMCID: PMC4619454 DOI: 10.1186/s13321-015-0098-y] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 09/29/2015] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND In silico analyses are increasingly being used to support mode-of-action investigations; however many such approaches do not utilise the large amounts of inactive data held in chemogenomic repositories. The objective of this work is concerned with the integration of such bioactivity data in the target prediction of orphan compounds to produce the probability of activity and inactivity for a range of targets. To this end, a novel human bioactivity data set was constructed through the assimilation of over 195 million bioactivity data points deposited in the ChEMBL and PubChem repositories, and the subsequent application of a sphere-exclusion selection algorithm to oversample presumed inactive compounds. RESULTS A Bernoulli Naïve Bayes algorithm was trained using the data and evaluated using fivefold cross-validation, achieving a mean recall and precision of 67.7 and 63.8 % for active compounds and 99.6 and 99.7 % for inactive compounds, respectively. We show the performances of the models are considerably influenced by the underlying intraclass training similarity, the size of a given class of compounds, and the degree of additional oversampling. The method was also validated using compounds extracted from WOMBAT producing average precision-recall AUC and BEDROC scores of 0.56 and 0.85, respectively. Inactive data points used for this test are based on presumed inactivity, producing an approximated indication of the true extrapolative ability of the models. A distance-based applicability domain analysis was also conducted; indicating an average Tanimoto Coefficient distance of 0.3 or greater between a test and training set can be used to give a global measure of confidence in model predictions. A final comparison to a method trained solely on active data from ChEMBL performed with precision-recall AUC and BEDROC scores of 0.45 and 0.76. CONCLUSIONS The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models. The realised target prediction protocol is available at https://github.com/lhm30/PIDGIN.Graphical abstractThe inclusion of large scale negative training data for in silico target prediction improves the precision and recall AUC and BEDROC scores for target models.
Collapse
Affiliation(s)
- Lewis H. Mervin
- />Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Avid M. Afzal
- />Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Georgios Drakakis
- />Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Richard Lewis
- />Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| | - Ola Engkvist
- />Discovery Sciences, Chemistry Innovation Centre, AstraZeneca R&D, 43183 Mölndal, Sweden
| | - Andreas Bender
- />Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW UK
| |
Collapse
|
48
|
|
49
|
Multiple binding sites in the nicotinic acetylcholine receptors: An opportunity for polypharmacolgy. Pharmacol Res 2015; 101:9-17. [PMID: 26318763 DOI: 10.1016/j.phrs.2015.08.018] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 08/20/2015] [Accepted: 08/20/2015] [Indexed: 12/21/2022]
Abstract
For decades, the development of selective compounds has been the main goal for chemists and biologists involved in drug discovery. However, diverse lines of evidence indicate that polypharmacological agents, i.e. those that act simultaneously at various protein targets, might show better profiles than selective ligands, regarding both efficacy and side effects. On the other hand, the availability of the crystal structure of different receptors allows a detailed analysis of the main interactions between drugs and receptors in a specific binding site. Neuronal nicotinic acetylcholine receptors (nAChRs) constitute a large and diverse family of ligand-gated ion channels (LGICs) that, as a product of its modulation, regulate neurotransmitter release, which in turns produce a global neuromodulation of the central nervous system. nAChRs are pentameric protein complexes in such a way that expression of compatible subunits can lead to various receptor assemblies or subtypes. The agonist binding site, located at the extracellular region, exhibits different properties depending on the subunits that conform the receptor. In the last years, it has been recognized that nAChRs could also contain one or more allosteric sites which could bind non-classical nicotinic ligands including several therapeutically useful drugs. The presence of multiple binding sites in nAChRs offers an interesting possibility for the development of novel polypharmacological agents with a wide spectrum of actions.
Collapse
|
50
|
Möller-Acuña P, Contreras-Riquelme JS, Rojas-Fuentes C, Nuñez-Vivanco G, Alzate-Morales J, Iturriaga-Vásquez P, Arias HR, Reyes-Parada M. Similarities between the Binding Sites of SB-206553 at Serotonin Type 2 and Alpha7 Acetylcholine Nicotinic Receptors: Rationale for Its Polypharmacological Profile. PLoS One 2015; 10:e0134444. [PMID: 26244344 PMCID: PMC4526571 DOI: 10.1371/journal.pone.0134444] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 07/10/2015] [Indexed: 11/18/2022] Open
Abstract
Evidence from systems biology indicates that promiscuous drugs, i.e. those that act simultaneously at various protein targets, are clinically better in terms of efficacy, than those that act in a more selective fashion. This has generated a new trend in drug development called polypharmacology. However, the rational design of promiscuous compounds is a difficult task, particularly when the drugs are aimed to act at receptors with diverse structure, function and endogenous ligand. In the present work, using docking and molecular dynamics methodologies, we established the most probable binding sites of SB-206553, a drug originally described as a competitive antagonist of serotonin type 2B/2C metabotropic receptors (5-HT2B/2CRs) and more recently as a positive allosteric modulator of the ionotropic α7 nicotinic acetylcholine receptor (nAChR). To this end, we employed the crystal structures of the 5-HT2BR and acetylcholine binding protein as templates to build homology models of the 5-HT2CR and α7 nAChR, respectively. Then, using a statistical algorithm, the similarity between these binding sites was determined. Our analysis showed that the most plausible binding sites for SB-206553 at 5-HT2Rs and α7 nAChR are remarkably similar, both in size and chemical nature of the amino acid residues lining these pockets, thus providing a rationale to explain its affinity towards both receptor types. Finally, using a computational tool for multiple binding site alignment, we determined a consensus binding site, which should be useful for the rational design of novel compounds acting simultaneously at these two types of highly different protein targets.
Collapse
Affiliation(s)
- Patricia Möller-Acuña
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
- Programa de Doctorado en Biotecnología, Universidad de Santiago de Chile, Santiago, Chile
- * E-mail: (PMA); (MRP)
| | - J. Sebastián Contreras-Riquelme
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
- Laboratorio de Biología Computacional, Fundación Ciencia & Vida, Santiago, Chile
| | - Cecilia Rojas-Fuentes
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | - Gabriel Nuñez-Vivanco
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | - Jans Alzate-Morales
- Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
| | | | - Hugo R. Arias
- Department of Medical Education, California Northstate University College of Medicine, Elk Grove, CA, United States of America
| | - Miguel Reyes-Parada
- Escuela de Medicina, Facultad de Ciencias Médicas, Universidad de Santiago de Chile, Santiago, Chile
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
- * E-mail: (PMA); (MRP)
| |
Collapse
|