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Ouyang R, Liu J, Wang S, Zhang W, Feng K, Liu C, Liu B, Miao Y, Zhou S. Virtual Screening-Based Study of Novel Anti-Cancer Drugs Targeting G-Quadruplex. Pharmaceutics 2023; 15:pharmaceutics15051414. [PMID: 37242656 DOI: 10.3390/pharmaceutics15051414] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/19/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
In order to develop new anti-cancer drugs more efficiently and reduce side effects based on active drug targets, the virtual drug screening was carried out through the target of G-quadruplexes and 23 hit compounds were, thus, screened out as potential anticancer drugs. Six classical G-quadruplex complexes were introduced as query molecules, and the three-dimensional similarity of molecules was calculated by shape feature similarity (SHAFTS) method so as to reduce the range of potential compounds. Afterwards, the molecular docking technology was utilized to perform the final screening followed by the exploration of the binding between each compound and four different structures of G-quadruplex. In order to verify the anticancer activity of the selected compounds, compounds 1, 6 and 7 were chosen to treat A549 cells in vitro, the lung cancer epithelial cells, for further exploring their anticancer activity. These three compounds were found to be of good characteristics in the treatment of cancer, which revealed the great application prospect of the virtual screening method in developing new drugs.
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Affiliation(s)
- Ruizhuo Ouyang
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Jinyao Liu
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Shen Wang
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Weilun Zhang
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Kai Feng
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Conghao Liu
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Baolin Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuqing Miao
- Institute of Bismuth and Rhenium Science, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Shuang Zhou
- Cancer Institute, Tongji University School of Medicine, Shanghai 200092, China
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2
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Qin T, Gao X, Lei L, Feng J, Zhang W, Hu Y, Shen Z, Liu Z, Huan Y, Wu S, Xia J, Zhang L. Machine learning- and structure-based discovery of a novel chemotype as FXR agonists for potential treatment of nonalcoholic fatty liver disease. Eur J Med Chem 2023; 252:115307. [PMID: 37003047 DOI: 10.1016/j.ejmech.2023.115307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/12/2023] [Accepted: 03/22/2023] [Indexed: 03/29/2023]
Abstract
Farnesoid X receptor (FXR) is a promising target for drug discovery against nonalcoholic fatty liver disease (NAFLD). However, no FXR agonist has been approved for NAFLD so far. The R & D of FXR agonists are somewhat hindered by the lack of effective and safe chemotypes. To this end, we developed a multi-stage computational workflow to screen the Specs and ChemDiv chemical library for FXR agonists, which consisted of machine learning (ML)-based classifiers, shape-based and electrostatic-based models, a FRED-based molecular docking protocol, an ADMET prediction protocol and substructure search. As a result, we identified a novel chemotype that has never been reported before, with compound XJ02862 (ChemDiv ID: Y020-6413) as the representative. By designing an asymmetric synthesis strategy, we were able to prepare four isomers of compound XJ02862. Interestingly, one of the isomers, 2-((S)-1-((2S,4R)-2-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)-1-oxopropan-2-yl)hexahydro-1H-isoindole-1,3(2H)-dione (XJ02862-S2), showed potent FXR agonistic activity in HEK293T cells. The molecular docking, molecular dynamics simulations and site-directed mutagenesis suggested the hydrogen bond between compound XJ02862-S2 and HIS294 of FXR is essential for ligand binding. We further demonstrated that compound XJ02862-S2 had no agonistic effect on TGR5. Further biological experiments have shown that compound XJ02862-S2 could ameliorate hypercholesterolemia, hepatic steatosis, hyperglycemia, insulin resistance (IR) in high-fat-diet induced obese (DIO) mice. In term of molecular mechanism, compound XJ02862-S2 regulates the expression of FXR downstream genes involved in lipogenesis, cholesterol transport and bile acid biosynthesis and transport. Taken together, we have discovered a novel chemotype as potent FXR agonists for NAFLD by computational modeling, chemical synthesis and biological evaluation.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xuefeng Gao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Lei Lei
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jing Feng
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Wenxuan Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Yuhua Hu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Zhufang Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Yi Huan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
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3
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Fan N, Hirte S, Kirchmair J. Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches. Int J Mol Sci 2022; 23:ijms23147747. [PMID: 35887097 PMCID: PMC9322642 DOI: 10.3390/ijms23147747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023] Open
Abstract
Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.
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Affiliation(s)
- Ningning Fan
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany;
| | - Steffen Hirte
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, 1090 Vienna, Austria
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany;
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +43-1-4277-55104
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4
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Li D, Jiang K, Teng D, Wu Z, Li W, Tang Y, Wang R, Liu G. Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking. J Chem Inf Model 2022; 62:486-497. [PMID: 35041411 DOI: 10.1021/acs.jcim.1c00662] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.
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Affiliation(s)
- Dongping Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Kexin Jiang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Dan Teng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Rui Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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5
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Suay-García B, Bueso-Bordils JI, Falcó A, Antón-Fos GM, Alemán-López PA. Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design. Int J Mol Sci 2022; 23:ijms23031620. [PMID: 35163543 PMCID: PMC8836228 DOI: 10.3390/ijms23031620] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/24/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.
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Affiliation(s)
- Beatriz Suay-García
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
- Correspondence:
| | - Jose I. Bueso-Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Antonio Falcó
- ESI International @ UCHCEU, Departamento de Matemáticas, Física y Ciencias Tecnológicas, Universidad Cardenal Herrera—CEU, CEU Universities San Bartolomé 55, Alfara del Patriarca, 46115 Valencia, Spain;
| | - Gerardo M. Antón-Fos
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
| | - Pedro A. Alemán-López
- Departamento de Farmacia, Universidad Cardenal Herrera—CEU, CEU Universities, C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain; (G.M.A.-F.); (P.A.A.-L.); (J.I.B.-B.)
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6
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Yin Y, Hu H, Yang Z, Xu H, Wu J. RealVS: Toward Enhancing the Precision of Top Hits in Ligand-Based Virtual Screening of Drug Leads from Large Compound Databases. J Chem Inf Model 2021; 61:4924-4939. [PMID: 34619030 DOI: 10.1021/acs.jcim.1c01021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Accurate modeling of compound bioactivities is essential for the virtual screening of drug leads. In real-world scenarios, pharmacists tend to choose from the top-k hit compounds ranked by predicted bioactivities from a large database with interest to continue wet experiments for drug discovery. Significant improvement of the precision of the top hits in ligand-based virtual screening of drug leads is more valuable than conventional schemes for accurately predicting the bioactivities of all compounds from a large database. Here, we proposed a new method, RealVS, to significantly improve the top hits' precision and learn interpretable key substructures associated with compound bioactivities. The features of RealVS involve the following points. (1) Abundant transferable information from the source domain was introduced for alleviating the insufficiency of inactive ligands associated with drug targets. (2) The adversarial domain alignment was adopted to fit the distribution of generated features of compounds from the training data set and that from the screening database for greater model generalization ability. (3) A novel objective function was proposed to simultaneously optimize the classification loss, regression loss, and adversarial loss, where most inactive ligands tend to be screened out before activity regression prediction. (4) Graph attention networks were adopted for learning key substructures associated with ligand bioactivities for better model interpretability. The results on a large number of benchmark data sets show that our method has significantly improved the precision of top hits under various k values in ligand-based virtual screening of drug leads from large compound databases, which is of great value in real-world scenarios. The web server of RealVS is freely available at noveldelta.com/RealVS for academic purposes, where virtual screening of hits from large compound databases is accessible.
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Affiliation(s)
- Yueming Yin
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Haifeng Hu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Zhen Yang
- National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Huajian Xu
- College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Jiansheng Wu
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
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7
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Koval A, Bassanini I, Xu J, Tonelli M, Boido V, Sparatore F, Amant F, Annibali D, Leucci E, Sparatore A, Katanaev VL. Optimization of the clofazimine structure leads to a highly water-soluble C3-aminopyridinyl riminophenazine endowed with improved anti-Wnt and anti-cancer activity in vitro and in vivo. Eur J Med Chem 2021; 222:113562. [PMID: 34116325 DOI: 10.1016/j.ejmech.2021.113562] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/10/2021] [Accepted: 05/17/2021] [Indexed: 12/15/2022]
Abstract
Triple-negative breast cancer (TNBC) is a cancer subtype critically dependent upon excessive activation of Wnt pathway. The anti-mycobacterial drug clofazimine is an efficient inhibitor of canonical Wnt signaling in TNBC, reducing tumor cell proliferation in vitro and in animal models. These properties make clofazimine a candidate to become first targeted therapy against TNBC. In this work, we optimized the clofazimine structure to enhance its water solubility and potency as a Wnt inhibitor. After extensive structure-activity relationships investigations, the riminophenazine 5-(4-(chlorophenyl)-3-((2-(piperazin-1-yl)ethyl)imino)-N-(pyridin-3-yl)-3,5-dihydrophenazin-2-amine (MU17) was identified as the new lead compound for the riminophenazine-based targeted therapy against TNBC and Wnt-dependent cancers. Compared to clofazimine, the water-soluble MU17 displayed a 7-fold improved potency against Wnt signaling in TNBC cells resulting in on-target suppression of tumor growth in a patient-derived mouse model of TNBC. Moreover, allowing the administration of reduced yet effective dosages, MU17 displayed no adverse effects, most notably no clofazimine-related skin coloration.
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Affiliation(s)
- Alexey Koval
- Department of Cell Physiology and Metabolism, Translational Research Centre in Oncohaematology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland
| | - Ivan Bassanini
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, 20133, Milano, Italy; Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", Consiglio Nazonale delle Ricerche, 20131, Milano, Italy
| | - Jiabin Xu
- Department of Cell Physiology and Metabolism, Translational Research Centre in Oncohaematology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland; Department of Biomedical Sciences, Faculty of Biology and Medicine, 1011, University of Lausanne, Lausanne, Switzerland
| | - Michele Tonelli
- Dipartimento di Farmacia, Università di Genova, 16132, Genova, Italy
| | - Vito Boido
- Dipartimento di Farmacia, Università di Genova, 16132, Genova, Italy
| | - Fabio Sparatore
- Dipartimento di Farmacia, Università di Genova, 16132, Genova, Italy
| | - Frederic Amant
- Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), 3000, Leuven, Belgium; Department of Obstetrics and Gynecology, University Hospitals Leuven and Department of Oncology, 3000, Leuven, Belgium; Centre for Gynecologic Oncology Amsterdam (CGOA), Antoni Van Leeuwenhoek-Netherlands Cancer Institute (AvL-NKI), University Medical Center (UMC), 1066, Amsterdam, the Netherlands
| | - Daniela Annibali
- Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), 3000, Leuven, Belgium
| | - Eleonora Leucci
- Laboratory for RNA Cancer Biology, Department of Oncology, KU Leuven, 3000, Leuven, Belgium; Trace, LKI Leuven Cancer Institute, KU Leuven, 3000, Leuven, Belgium
| | - Anna Sparatore
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, 20133, Milano, Italy.
| | - Vladimir L Katanaev
- Department of Cell Physiology and Metabolism, Translational Research Centre in Oncohaematology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland; School of Biomedicine, Far Eastern Federal University, 690922, Vladivostok, Russia.
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8
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Coban MA, Morrison J, Maharjan S, Hernandez Medina DH, Li W, Zhang YS, Freeman WD, Radisky ES, Le Roch KG, Weisend CM, Ebihara H, Caulfield TR. Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement. Biomolecules 2021; 11:biom11060787. [PMID: 34071060 PMCID: PMC8224684 DOI: 10.3390/biom11060787] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/19/2021] [Accepted: 05/20/2021] [Indexed: 12/23/2022] Open
Abstract
COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors-S/Ace2, Tmprss2, Cathepsins L and K, and Mpro-to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.
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Affiliation(s)
- Mathew A. Coban
- Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA; (M.A.C.); (E.S.R.)
| | - Juliet Morrison
- Department of Microbiology and Plant Pathology, University of California, 900 University, Riverside, CA 92521, USA;
| | - Sushila Maharjan
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA; (S.M.); (D.H.H.M.); (W.L.); (Y.S.Z.)
| | - David Hyram Hernandez Medina
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA; (S.M.); (D.H.H.M.); (W.L.); (Y.S.Z.)
| | - Wanlu Li
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA; (S.M.); (D.H.H.M.); (W.L.); (Y.S.Z.)
| | - Yu Shrike Zhang
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St, Cambridge, MA 02139, USA; (S.M.); (D.H.H.M.); (W.L.); (Y.S.Z.)
| | - William D. Freeman
- Department of Neurology, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL 32224, USA;
| | - Evette S. Radisky
- Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA; (M.A.C.); (E.S.R.)
| | - Karine G. Le Roch
- Department of Molecular, Cell and Systems Biology, University of California, 900 University, Riverside, CA 92521, USA;
| | - Carla M. Weisend
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.M.W.); (H.E.)
| | - Hideki Ebihara
- Department of Molecular Medicine, Mayo Clinic, Rochester, MN 55905, USA; (C.M.W.); (H.E.)
| | - Thomas R. Caulfield
- Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA; (M.A.C.); (E.S.R.)
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Quantitative Health Science, Division of Computational Biology, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Clinical Genomics, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biochemistry & Molecular Biology, Mayo Clinic, Rochester, MN 55905, USA
- Correspondence: ; Tel.: +1-904-953-6072
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9
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Abdo A, Pupin M. LINGO-DL: a text-based approach for molecular similarity searching. J Comput Aided Mol Des 2021; 35:657-665. [PMID: 33797669 DOI: 10.1007/s10822-021-00383-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/26/2021] [Indexed: 11/24/2022]
Abstract
The line notations of chemical structures are more compact than those of graphs and connection tables, so they can be useful for storing and transferring a large number of molecular structures. The simplified molecular input line system (SMILES) representation is the most extensively used, as it is much easier to utilise and comprehend than others, and it can be generated automatically from connection tables. A SMILES represents and encodes the molecule structure. It has been used by an existing method, LINGO, to calculate the molecular similarities and predict the structure-related properties. The LINGO method decomposes a canonical SMILES into a set of substrings of four characters referred to as LINGOs. The purpose of LINGO method is to measure the similarity between a pair of molecules by comparing the LINGOs that occur in each molecule. This paper aims to introduce an alternative version of the LINGO method using LINGOs of different lengths, called LINGO-DL. LINGO-DL is based on the fragmentation of canonical SMILES into substrings of three different lengths rather than one in LINGO method. Retrospective virtual screening experiments with MDDR, DUD, and MUV datasets show that the LINGO-DL outperforms the LINGO method, especially when the active molecules being sought have a high degree of structural heterogeneity.
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Affiliation(s)
- Ammar Abdo
- Universite de Lille, Villeneuve d'Ascq cedex, France.
| | - Maude Pupin
- Universite de Lille, Villeneuve d'Ascq cedex, France
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10
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Jin H, Xia J, Liu Z, Wang XS, Zhang L. A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening. Chem Biol Drug Des 2021; 97:944-961. [PMID: 33386704 PMCID: PMC8048943 DOI: 10.1111/cbdd.13820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/12/2020] [Accepted: 12/13/2020] [Indexed: 12/29/2022]
Abstract
CC chemokine receptor 2 (CCR2) antagonists that disrupt CCR2/MCP-1 interaction are expected to treat a variety of inflammatory and autoimmune diseases. The lack of CCR2 crystal structure limits the application of structure-based drug design (SBDD) to this target. Although a few three-dimensional theoretical models have been reported, their accuracy remains to be improved in terms of templates and modeling approaches. In this study, we developed a unique ligand-steered strategy for CCR2 homology modeling. It starts with an initial model based on the X-ray structure of the closest homolog so far, that is, CXCR4. Then, it uses Elastic Network Normal Mode Analysis (EN-NMA) and flexible docking (FD) by AutoDock Vina software to generate ligand-induced fit models. It selects optimal model(s) as well as scoring function(s) via extensive evaluation of model performance based on a unique benchmarking set constructed by our in-house tool, that is, MUBD-DecoyMaker. The model of 81_04 presents the optimal enrichment when combined with the scoring function of PMF04, and the proposed binding mode between CCR2 and Teijin lead by this model complies with the reported mutagenesis data. To highlight the advantage of our strategy, we compared it with the only reported ligand-steered strategy for CCR2 homology modeling, that is, Discovery Studio/Ligand Minimization. Lastly, we performed prospective virtual screening based on 81_04 and CCR2 antagonist bioassay. The identification of two hit compounds, that is, E859-1281 and MolPort-007-767-945, validated the efficacy of our model and the ligand-steered strategy.
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Affiliation(s)
- Hongwei Jin
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural MedicinesDepartment of New Drug Research and DevelopmentInstitute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core for District of Columbia Center for AIDS Research (DC CFAR)Laboratory of Cheminformatics and Drug DesignDepartment of Pharmaceutical SciencesCollege of PharmacyHoward UniversityWashingtonDCUSA
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic DrugsSchool of Pharmaceutical SciencesPeking UniversityBeijingChina
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11
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Li S, Ding Y, Chen M, Chen Y, Kirchmair J, Zhu Z, Wu S, Xia J. HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors. Mol Inform 2020; 40:e2000105. [PMID: 33067876 DOI: 10.1002/minf.202000105] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 10/16/2020] [Indexed: 11/10/2022]
Abstract
Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two-dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k-Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best-performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well-curated benchmarking set named MUBD-HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel-scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i-Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i-Finder is available at https://github.com/jwxia2014/HDAC3i-Finder.
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Affiliation(s)
- Shan Li
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Yu Ding
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Miaomiao Chen
- College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
| | - Ya Chen
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Computer Science, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, Hamburg, Germany.,Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, Vienna, Austria
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
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12
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Stojanović L, Popović M, Tijanić N, Rakočević G, Kalinić M. Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning. J Chem Inf Model 2020; 60:4629-4639. [DOI: 10.1021/acs.jcim.0c00622] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Miloš Popović
- Totient, Inc., Sinđelićeva 9, 11000 Belgrade, Serbia
| | | | | | - Marko Kalinić
- Totient, Inc., Sinđelićeva 9, 11000 Belgrade, Serbia
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13
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Vazquez J, Deplano A, Herrero A, Gibert E, Herrero E, Luque FJ. Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening. J Chem Inf Model 2020; 60:4231-4245. [PMID: 32364713 DOI: 10.1021/acs.jcim.9b01191] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.
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Affiliation(s)
- Javier Vazquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain.,Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, Santa Coloma de Gramanet E-08921, Spain
| | - Alessandro Deplano
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Albert Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, Barcelona 08039, Spain
| | - F Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, Santa Coloma de Gramanet E-08921, Spain
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14
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Shaker B, Yu MS, Lee J, Lee Y, Jung C, Na D. User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation. J Microbiol 2020; 58:235-244. [PMID: 32108318 DOI: 10.1007/s12275-020-9563-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 01/17/2020] [Accepted: 01/22/2020] [Indexed: 11/24/2022]
Abstract
Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.
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Affiliation(s)
- Bilal Shaker
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Myung-Sang Yu
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Jingyu Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Yongmin Lee
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Chanjin Jung
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea
| | - Dokyun Na
- School of Integrative Engineering, Chung-Ang University, Seoul, 06974, Republic of Korea.
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15
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Bassanini I, Parapini S, Galli C, Vaiana N, Pancotti A, Basilico N, Taramelli D, Romeo S. Discovery and Pharmacophore Mapping of a Low-Nanomolar Inhibitor of P. falciparum Growth. ChemMedChem 2019; 14:1982-1994. [PMID: 31665565 DOI: 10.1002/cmdc.201900526] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/04/2019] [Indexed: 11/06/2022]
Abstract
The treatment of malaria, the most common parasitic disease worldwide and the third deadliest infection after HIV and tuberculosis, is currently compromised by the dramatic increase and diffusion of drug resistance among the various species of Plasmodium, especially P. falciparum (Pf). In this view, the development of new antiplasmodial agents that are able to act via innovative mechanisms of action, is crucial to ensure efficacious antimalarial treatments. In one of our previous communications, we described a novel class of compounds endowed with high antiplasmodial activity, characterized by a pharmacophore never described before as antiplasmodial and identified by their 4,4'-oxybisbenzoyl amide cores. Here, through a detailed structure-activity relationship (SAR) study, we thoroughly investigated the chemical features of the reported scaffolds and successfully built a novel antiplasmodial agent active on both chloroquine (CQ)-sensitive and CQ-resistant Pf strains in the low nanomolar range, without displaying cross-resistance. Moreover, we conducted an in silico pharmacophore mapping.
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Affiliation(s)
- Ivan Bassanini
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli 25, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Silvia Parapini
- Dipartimento di Scienze Biomediche, Chirurgiche e Odontoiatriche, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Corinna Galli
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli 25, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Nadia Vaiana
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli 25, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Andrea Pancotti
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli 25, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Nicoletta Basilico
- Dipartimento di Scienze Biomediche, Chirurgiche e Odontoiatriche, Università degli Studi di Milano, Via Pascal, 36, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Donatella Taramelli
- Dipartimento di Scienze Farmacologiche e Biomolecolari, Università degli Studi di Milano, Via Pascal 36, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
| | - Sergio Romeo
- Dipartimento di Scienze Farmaceutiche, Università degli Studi di Milano, Via Mangiagalli 25, 20133, Milan, Italy.,Centro Interuniversitario di Ricerca sulla Malaria-Italian Malaria Network
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16
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Hernandez M, Liang Gan G, Linvill K, Dukatz C, Feng J, Bhisetti G. A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening. J Chem Inf Model 2019; 59:4475-4485. [DOI: 10.1021/acs.jcim.9b00195] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Maritza Hernandez
- 1QB Information Technologies (1QBit), 200-1285 West Pender Street, Vancouver, BC V6E 4B1, Canada
| | - Guo Liang Gan
- 1QB Information Technologies (1QBit), 200-1285 West Pender Street, Vancouver, BC V6E 4B1, Canada
- School of Computing Science, Simon Fraser University, 8888 University Drive, Vancouver, BC V5A 1S6, Canada
| | - Kirby Linvill
- Accenture Labs, Accenture PLC, 415 Mission Street, Suite 3300, San Francisco, California 94105, United States
| | - Carl Dukatz
- Accenture Labs, Accenture PLC, 415 Mission Street, Suite 3300, San Francisco, California 94105, United States
| | - Jun Feng
- Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
| | - Govinda Bhisetti
- Biogen, 225 Binney Street, Cambridge, Massachusetts 02142, United States
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17
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Xu Y, Wang S, Hu Q, Gao S, Ma X, Zhang W, Shen Y, Chen F, Lai L, Pei J. CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction. Nucleic Acids Res 2019; 46:W374-W379. [PMID: 29750256 PMCID: PMC6031032 DOI: 10.1093/nar/gky380] [Citation(s) in RCA: 188] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Accepted: 04/30/2018] [Indexed: 12/02/2022] Open
Abstract
CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.
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Affiliation(s)
- Youjun Xu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shiwei Wang
- School of Life Sciences, Peking University, Beijing 100871, China
| | - Qiwan Hu
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Shuaishi Gao
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Xiaomin Ma
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Weilin Zhang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yihang Shen
- BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Fangjin Chen
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Luhua Lai
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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18
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Pérez-Sianes J, Pérez-Sánchez H, Díaz F. Virtual Screening Meets Deep Learning. Curr Comput Aided Drug Des 2019; 15:6-28. [PMID: 30338743 DOI: 10.2174/1573409914666181018141602] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 10/08/2018] [Accepted: 10/11/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. OBJECTIVE This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
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Affiliation(s)
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
| | - Fernando Díaz
- Departamento de Informática, Escuela de Ingeniería Informática, University of Valladolid, Segovia, Spain
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19
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Battini L, Bollini M. Challenges and approaches in the discovery of human immunodeficiency virus type‐1 non‐nucleoside reverse transcriptase inhibitors. Med Res Rev 2018; 39:1235-1273. [DOI: 10.1002/med.21544] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/04/2018] [Accepted: 10/04/2018] [Indexed: 12/11/2022]
Affiliation(s)
- Leandro Battini
- Laboratorio de Química Medicinal, Centro de Investigaciones en Bionanociencias (CIBION), CONICETCiudad de Buenos Aires Argentina
| | - Mariela Bollini
- Laboratorio de Química Medicinal, Centro de Investigaciones en Bionanociencias (CIBION), CONICETCiudad de Buenos Aires Argentina
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20
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Xia J, Reid TE, Wu S, Zhang L, Wang XS. Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis. J Chem Inf Model 2018; 58:1104-1120. [PMID: 29698608 DOI: 10.1021/acs.jcim.8b00004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV-1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approach including both structure- and ligand-based VS somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and is readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of "artificial enrichment", "analogue bias". In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure- and ligand-based VS approaches in comparison with other benchmarking data sets available in the public domain and demonstrated that MUBD-hCRs is very capable of designating the optimal VS approach. MUBD-hCRs is a unique and maximal unbiased benchmarking set that covers major CRs subtypes so far.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica , Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , China.,State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences , Peking University , Beijing 100191 , China
| | - Terry-Elinor Reid
- Molecular Modeling and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy , Howard University , Washington , D.C. 20059 , United States
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica , Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050 , China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences , Peking University , Beijing 100191 , China
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy , Howard University , Washington , D.C. 20059 , United States
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21
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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22
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Xia J, Hsieh JH, Hu H, Wu S, Wang XS. The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening. J Chem Inf Model 2017; 57:1414-1425. [PMID: 28511009 DOI: 10.1021/acs.jcim.6b00749] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structure-based virtual screening (SBVS) has become an indispensable technique for hit identification at the early stage of drug discovery. However, the accuracy of current scoring functions is not high enough to confer success to every target and thus remains to be improved. Previously, we had developed binary pose filters (PFs) using knowledge derived from the protein-ligand interface of a single X-ray structure of a specific target. This novel approach had been validated as an effective way to improve ligand enrichment. Continuing from it, in the present work we attempted to incorporate knowledge collected from diverse protein-ligand interfaces of multiple crystal structures of the same target to build PF ensembles (PFEs). Toward this end, we first constructed a comprehensive data set to meet the requirements of ensemble modeling and validation. This set contains 10 diverse targets, 118 well-prepared X-ray structures of protein-ligand complexes, and large benchmarking actives/decoys sets. Notably, we designed a unique workflow of two-layer classifiers based on the concept of ensemble learning and applied it to the construction of PFEs for all of the targets. Through extensive benchmarking studies, we demonstrated that (1) coupling PFE with Chemgauss4 significantly improves the early enrichment of Chemgauss4 itself and (2) PFEs show greater consistency in boosting early enrichment and larger overall enrichment than our prior PFs. In addition, we analyzed the pairwise topological similarities among cognate ligands used to construct PFEs and found that it is the higher chemical diversity of the cognate ligands that leads to the improved performance of PFEs. Taken together, the results so far prove that the incorporation of knowledge from diverse protein-ligand interfaces by ensemble modeling is able to enhance the screening competence of SBVS scoring functions.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050, China
| | - Jui-Hua Hsieh
- Kelly Government Solutions , Research Triangle Park, North Carolina 27709, United States
| | - Huabin Hu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College , Beijing 100050, China
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University , Washington, D.C. 20059, United States
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A Thoroughly Validated Virtual Screening Strategy for Discovery of Novel HDAC3 Inhibitors. Int J Mol Sci 2017; 18:ijms18010137. [PMID: 28106794 PMCID: PMC5297770 DOI: 10.3390/ijms18010137] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/09/2017] [Accepted: 01/09/2017] [Indexed: 12/15/2022] Open
Abstract
Histone deacetylase 3 (HDAC3) has been recently identified as a potential target for the treatment of cancer and other diseases, such as chronic inflammation, neurodegenerative diseases, and diabetes. Virtual screening (VS) is currently a routine technique for hit identification, but its success depends on rational development of VS strategies. To facilitate this process, we applied our previously released benchmarking dataset, i.e., MUBD-HDAC3 to the evaluation of structure-based VS (SBVS) and ligand-based VS (LBVS) combinatorial approaches. We have identified FRED (Chemgauss4) docking against a structural model of HDAC3, i.e., SAHA-3 generated by a computationally inexpensive “flexible docking”, as the best SBVS approach and a common feature pharmacophore model, i.e., Hypo1 generated by Catalyst/HipHop as the optimal model for LBVS. We then developed a pipeline that was composed of Hypo1, FRED (Chemgauss4), and SAHA-3 sequentially, and demonstrated that it was superior to other combinations in terms of ligand enrichment. In summary, we present the first highly-validated, rationally-designed VS strategy specific to HDAC3 inhibitor discovery. The constructed pipeline is publicly accessible for the scientific community to identify novel HDAC3 inhibitors in a time-efficient and cost-effective way.
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Abstract
Molecular docking was earlier considered to predict the binding affinity of the receptor and ligand molecules. With the progress in computational power and developing approaches, new horizons are now opening for accurate prediction of molecular binding affinity. In the current book chapter, recent strategies for Computer-Aided Drug Designing (CADD) including virtual screening and molecular docking, encompassing molecular dynamics simulations, and binding free energy calculation methods are discussed. Brief overview of different binding free energy methods MMPBSA, MMGBSA, LIE and TI have also been given along with the recent Relaxed Complex Scheme protocol.
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Affiliation(s)
| | - Akhil Kumar
- CSIR-Central Institute of Medicinal and Aromatic Plants, India
| | | | - Ashok Sharma
- CSIR-Central Institute of Medicinal and Aromatic Plants, India
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Lo YC, Senese S, Damoiseaux R, Torres JZ. 3D Chemical Similarity Networks for Structure-Based Target Prediction and Scaffold Hopping. ACS Chem Biol 2016; 11:2244-53. [PMID: 27285961 DOI: 10.1021/acschembio.6b00253] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Target identification remains a major challenge for modern drug discovery programs aimed at understanding the molecular mechanisms of drugs. Computational target prediction approaches like 2D chemical similarity searches have been widely used but are limited to structures sharing high chemical similarity. Here, we present a new computational approach called chemical similarity network analysis pull-down 3D (CSNAP3D) that combines 3D chemical similarity metrics and network algorithms for structure-based drug target profiling, ligand deorphanization, and automated identification of scaffold hopping compounds. In conjunction with 2D chemical similarity fingerprints, CSNAP3D achieved a >95% success rate in correctly predicting the drug targets of 206 known drugs. Significant improvement in target prediction was observed for HIV reverse transcriptase (HIVRT) compounds, which consist of diverse scaffold hopping compounds targeting the nucleotidyltransferase binding site. CSNAP3D was further applied to a set of antimitotic compounds identified in a cell-based chemical screen and identified novel small molecules that share a pharmacophore with Taxol and display a Taxol-like mechanism of action, which were validated experimentally using in vitro microtubule polymerization assays and cell-based assays.
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Affiliation(s)
- Yu-Chen Lo
- Department of Chemistry
and Biochemistry, ‡Program in Bioengineering, §Department of Molecular and Medical Pharmacology, ∥California NanoSystems
Institute, ⊥Jonsson Comprehensive Cancer Center, and #The Molecular Biology Institute, University of California, Los Angeles, California 90095, United States
| | - Silvia Senese
- Department of Chemistry
and Biochemistry, ‡Program in Bioengineering, §Department of Molecular and Medical Pharmacology, ∥California NanoSystems
Institute, ⊥Jonsson Comprehensive Cancer Center, and #The Molecular Biology Institute, University of California, Los Angeles, California 90095, United States
| | - Robert Damoiseaux
- Department of Chemistry
and Biochemistry, ‡Program in Bioengineering, §Department of Molecular and Medical Pharmacology, ∥California NanoSystems
Institute, ⊥Jonsson Comprehensive Cancer Center, and #The Molecular Biology Institute, University of California, Los Angeles, California 90095, United States
| | - Jorge Z. Torres
- Department of Chemistry
and Biochemistry, ‡Program in Bioengineering, §Department of Molecular and Medical Pharmacology, ∥California NanoSystems
Institute, ⊥Jonsson Comprehensive Cancer Center, and #The Molecular Biology Institute, University of California, Los Angeles, California 90095, United States
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Lagarde N, Zagury JF, Montes M. Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives. J Chem Inf Model 2015; 55:1297-307. [PMID: 26038804 DOI: 10.1021/acs.jcim.5b00090] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.
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Affiliation(s)
- Nathalie Lagarde
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003 Paris, France
| | - Jean-François Zagury
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003 Paris, France
| | - Matthieu Montes
- Laboratoire Génomique, Bioinformatique et Applications, EA 4627, Conservatoire National des Arts et Métiers, 292 rue Saint Martin, 75003 Paris, France
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27
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Okuno T, Kato K, Terada TP, Sasai M, Chikenji G. VS-APPLE: A Virtual Screening Algorithm Using Promiscuous Protein–Ligand Complexes. J Chem Inf Model 2015; 55:1108-19. [DOI: 10.1021/acs.jcim.5b00134] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Tatsuya Okuno
- Department of Applied Physics and ‡Department of Computational Science and Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Koya Kato
- Department of Applied Physics and ‡Department of Computational Science and Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Tomoki P. Terada
- Department of Applied Physics and ‡Department of Computational Science and Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - Masaki Sasai
- Department of Applied Physics and ‡Department of Computational Science and Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
| | - George Chikenji
- Department of Applied Physics and ‡Department of Computational Science and Engineering, Nagoya University, Nagoya, Aichi 464-8603, Japan
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Lo YC, Senese S, Li CM, Hu Q, Huang Y, Damoiseaux R, Torres JZ. Large-scale chemical similarity networks for target profiling of compounds identified in cell-based chemical screens. PLoS Comput Biol 2015; 11:e1004153. [PMID: 25826798 PMCID: PMC4380459 DOI: 10.1371/journal.pcbi.1004153] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Accepted: 01/26/2015] [Indexed: 01/17/2023] Open
Abstract
Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). Determining the targets of compounds identified in cell-based high-throughput chemical screens is a critical step for downstream drug development and understanding of compound mechanism of action. However, current computational target prediction approaches like chemical similarity database searches are limited to single or sequential ligand analyses, which limits their ability to accurately deconvolve a large number of compounds that often have chemically diverse structures. Here, we have developed a new computational drug target prediction method, called CSNAP that is based on chemical similarity networks. By clustering diverse chemical structures into distinct sub-networks corresponding to chemotypes, we show that CSNAP improves target prediction accuracy and consistency over a board range of drug classes. We further coupled CSNAP to a mitotic database and successfully determined the major mitotic drug targets of a diverse compound set identified in a cell-based chemical screen. We demonstrate that CSNAP can easily integrate with diverse knowledge-based databases for on/off target prediction and post-target validation, thus broadening its applicability for identifying the targets of bioactive compounds from a wide range of chemical screens.
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Affiliation(s)
- Yu-Chen Lo
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
- Program in Bioengineering, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Silvia Senese
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Chien-Ming Li
- Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America
| | - Qiyang Hu
- Institute for Digital Research and Education, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Yong Huang
- Drug Studies Unit, Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, California, United States of America
| | - Robert Damoiseaux
- California NanoSystems Institute, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Jorge Z. Torres
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California, United States of America
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California, United States of America
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California, United States of America
- * E-mail:
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Xia J, Tilahun EL, Kebede EH, Reid TE, Zhang L, Wang XS. Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families. J Chem Inf Model 2015; 55:374-88. [PMID: 25633490 DOI: 10.1021/ci5005515] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Histone deacetylases (HDACs) are an important class of drug targets for the treatment of cancers, neurodegenerative diseases, and other types of diseases. Virtual screening (VS) has become fairly effective approaches for drug discovery of novel and highly selective histone deacetylase inhibitors (HDACIs). To facilitate the process, we constructed maximal unbiased benchmarking data sets for HDACs (MUBD-HDACs) using our recently published methods that were originally developed for building unbiased benchmarking sets for ligand-based virtual screening (LBVS). The MUBD-HDACs cover all four classes including Class III (Sirtuins family) and 14 HDAC isoforms, composed of 631 inhibitors and 24609 unbiased decoys. Its ligand sets have been validated extensively as chemically diverse, while the decoy sets were shown to be property-matching with ligands and maximal unbiased in terms of "artificial enrichment" and "analogue bias". We also conducted comparative studies with DUD-E and DEKOIS 2.0 sets against HDAC2 and HDAC8 targets and demonstrate that our MUBD-HDACs are unique in that they can be applied unbiasedly to both LBVS and SBVS approaches. In addition, we defined a novel metric, i.e. NLBScore, to detect the "2D bias" and "LBVS favorable" effect within the benchmarking sets. In summary, MUBD-HDACs are the only comprehensive and maximal-unbiased benchmark data sets for HDACs (including Sirtuins) that are available so far. MUBD-HDACs are freely available at http://www.xswlab.org/ .
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University , 38 Xueyuan Road, Beijing 100191, China
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Other Related Techniques. UNDERSTANDING THE BASICS OF QSAR FOR APPLICATIONS IN PHARMACEUTICAL SCIENCES AND RISK ASSESSMENT 2015. [PMCID: PMC7149793 DOI: 10.1016/b978-0-12-801505-6.00010-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.
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31
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Xia J, Tilahun EL, Reid TE, Zhang L, Wang XS. Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods 2015; 71:146-57. [PMID: 25481478 PMCID: PMC4278665 DOI: 10.1016/j.ymeth.2014.11.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 11/22/2014] [Accepted: 11/24/2014] [Indexed: 11/21/2022] Open
Abstract
Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China; Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Ermias Lemma Tilahun
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Terry-Elinor Reid
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China.
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
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Xia J, Jin H, Liu Z, Zhang L, Wang XS. An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs. J Chem Inf Model 2014; 54:1433-50. [PMID: 24749745 PMCID: PMC4038372 DOI: 10.1021/ci500062f] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Benchmarking data
sets have become common in recent years for the
purpose of virtual screening, though the main focus had been placed
on the structure-based virtual screening (SBVS) approaches. Due to
the lack of crystal structures, there is great need for unbiased benchmarking
sets to evaluate various ligand-based virtual screening (LBVS) methods
for important drug targets such as G protein-coupled receptors (GPCRs).
To date these ready-to-apply data sets for LBVS are fairly limited,
and the direct usage of benchmarking sets designed for SBVS could
bring the biases to the evaluation of LBVS. Herein, we propose an
unbiased method to build benchmarking sets for LBVS and validate it
on a multitude of GPCRs targets. To be more specific, our methods
can (1) ensure chemical diversity of ligands, (2) maintain the physicochemical
similarity between ligands and decoys, (3) make the decoys dissimilar
in chemical topology to all ligands to avoid false negatives, and
(4) maximize spatial random distribution of ligands and decoys. We
evaluated the quality of our Unbiased Ligand Set (ULS) and Unbiased
Decoy Set (UDS) using three common LBVS approaches, with Leave-One-Out
(LOO) Cross-Validation (CV) and a metric of average AUC of the ROC
curves. Our method has greatly reduced the “artificial enrichment”
and “analogue bias” of a published GPCRs benchmarking
set, i.e., GPCR Ligand Library (GLL)/GPCR Decoy Database (GDD). In
addition, we addressed an important issue about the ratio of decoys
per ligand and found that for a range of 30 to 100 it does not affect
the quality of the benchmarking set, so we kept the original ratio
of 39 from the GLL/GDD.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University , Beijing 100191, China
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Zhang Y, Yang S, Jiao Y, Liu H, Yuan H, Lu S, Ran T, Yao S, Ke Z, Xu J, Xiong X, Chen Y, Lu T. An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors. J Chem Inf Model 2013; 53:3163-77. [DOI: 10.1021/ci400429g] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Yanmin Zhang
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Shangyan Yang
- State
Key Laboratory of Natural Medcines, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Yu Jiao
- State
Key Laboratory of Natural Medcines, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Haichun Liu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Haoliang Yuan
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Shuai Lu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Ting Ran
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Sihui Yao
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Zhipeng Ke
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Jinxing Xu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Xiao Xiong
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Yadong Chen
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
| | - Tao Lu
- Laboratory
of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing 210009, China
- State
Key Laboratory of Natural Medcines, School of Science, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
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Riniker S, Fechner N, Landrum GA. Heterogeneous Classifier Fusion for Ligand-Based Virtual Screening: Or, How Decision Making by Committee Can Be a Good Thing. J Chem Inf Model 2013; 53:2829-36. [DOI: 10.1021/ci400466r] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Sereina Riniker
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, CH-4056 Basel, Switzerland
| | - Nikolas Fechner
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, CH-4056 Basel, Switzerland
| | - Gregory A. Landrum
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Novartis Campus, CH-4056 Basel, Switzerland
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36
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Wang Y, Backman TWH, Horan K, Girke T. fmcsR: mismatch tolerant maximum common substructure searching in R. Bioinformatics 2013; 29:2792-4. [PMID: 23962615 DOI: 10.1093/bioinformatics/btt475] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches. RESULTS The fmcsR package provides an R interface, with the time-consuming steps of the FMCS algorithm implemented in C++. It includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests. AVAILABILITY fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/packages/devel/bioc/html/fmcsR.html). CONTACT thomas.girke@ucr.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Wang
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
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Cai C, Gong J, Liu X, Gao D, Li H. SimG: an alignment based method for evaluating the similarity of small molecules and binding sites. J Chem Inf Model 2013; 53:2103-15. [PMID: 23889471 DOI: 10.1021/ci400139j] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
In this study, a Gaussian volume overlap and chemical feature based molecular similarity metric was devised, and a downhill simplex searching was carried out to evaluate the corresponding similarity. By representing the shapes of both the candidate small molecules and the binding site with chemical features and comparing the corresponding Gaussian volumes overlaps, the active compounds could be identified. These two aspects compose the proposed method named SimG which supports both structure-based and ligand-based strategies. The validity of the proposed method was examined by analyzing the similarity score variation between actives and decoys as well as correlation among distinct reference methods. A retrospective virtual screening test was carried out on DUD data sets, demonstrating that the performance of structure-based shape matching virtual screening in DUD data sets is substantially dependent on some physical properties, especially the solvent-exposure extent of the binding site: The enrichments of targets with less solvent-exposed binding sites generally exceeds that of the one with more solvent-exposed binding sites and even surpasses the corresponding ligand-based virtual screening.
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Affiliation(s)
- Chaoqian Cai
- School of Information Science and Engineering, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China
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38
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Durrant JD, Friedman AJ, Rogers KE, McCammon JA. Comparing neural-network scoring functions and the state of the art: applications to common library screening. J Chem Inf Model 2013; 53:1726-35. [PMID: 23734946 PMCID: PMC3735370 DOI: 10.1021/ci400042y] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2013] [Indexed: 11/29/2022]
Abstract
We compare established docking programs, AutoDock Vina and Schrödinger's Glide, to the recently published NNScore scoring functions. As expected, the best protocol to use in a virtual-screening project is highly dependent on the target receptor being studied. However, the mean screening performance obtained when candidate ligands are docked with Vina and rescored with NNScore 1.0 is not statistically different than the mean performance obtained when docking and scoring with Glide. We further demonstrate that the Vina and NNScore docking scores both correlate with chemical properties like small-molecule size and polarizability. Compensating for these potential biases leads to improvements in virtual screen performance. Composite NNScore-based scoring functions suited to a specific receptor further improve performance. We are hopeful that the current study will prove useful for those interested in computer-aided drug design.
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Affiliation(s)
- Jacob D Durrant
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA.
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39
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Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform 2013; 5:26. [PMID: 23721588 PMCID: PMC3686626 DOI: 10.1186/1758-2946-5-26] [Citation(s) in RCA: 207] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 05/20/2013] [Indexed: 01/21/2023] Open
Abstract
Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other.
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Affiliation(s)
- Sereina Riniker
- Novartis Institutes for BioMedical Research, Basel, Switzerland.
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40
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Rosenbaum L, Jahn A, Dörr A, Zell A. Optimization and visualization of the edge weights in optimal assignment methods for virtual screening. BioData Min 2013; 6:7. [PMID: 23531368 PMCID: PMC3639874 DOI: 10.1186/1756-0381-6-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2012] [Accepted: 03/10/2013] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Ligand-based virtual screening plays a fundamental part in the early drug discovery stage. In a virtual screening, a chemical library is searched for molecules with similar properties to a query molecule by means of a similarity function. The optimal assignment of chemical graphs has proven to be a valuable similarity function for many cheminformatic tasks, such as virtual screening. The optimal assignment assumes all atoms of a query molecule to be equally important, which is not realistic depending on the binding mode of a ligand. The importance of a query molecule's atoms can be integrated in the optimal assignment by weighting the assignment edges. We optimized the edge weights with respect to the virtual screening performance by means of evolutionary algorithms. Furthermore, we propose a visualization approach for the interpretation of the edge weights. RESULTS We evaluated two different evolutionary algorithms, differential evolution and particle swarm optimization, for their suitability for optimizing the assignment edge weights. The results showed that both optimization methods are suited to optimize the edge weights. Furthermore, we compared our approach to the optimal assignment with equal edge weights and two literature similarity functions on a subset of the Directory of Useful Decoys using sophisticated virtual screening performance metrics. Our approach achieved a considerably better overall and early enrichment performance. The visualization of the edge weights enables the identification of substructures that are important for a good retrieval of ligands and for the binding to the protein target. CONCLUSIONS The optimization of the edge weights in optimal assignment methods is a valuable approach for ligand-based virtual screening experiments. The approach can be applied to any similarity function that employs the optimal assignment method, which includes a variety of similarity measures that have proven to be valuable in various cheminformatic tasks. The proposed visualization helps to get a better understanding of the binding mode of the analyzed query molecule.
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Affiliation(s)
- Lars Rosenbaum
- University of Tübingen, Center for Bioinformatics (ZBIT), Sand 1, 72076 Tübingen, Germany.
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41
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42
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Retrospective group fusion similarity search based on eROCE evaluation metric. Bioorg Med Chem 2013; 21:1268-78. [PMID: 23375446 DOI: 10.1016/j.bmc.2012.12.041] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2012] [Revised: 12/16/2012] [Accepted: 12/18/2012] [Indexed: 12/26/2022]
Abstract
In this study, a simple evaluation metric, denoted as eROCE was proposed to measure the early enrichment of predictive methods. We demonstrated the superior robustness of eROCE compared to other known metrics throughout several active to inactive ratios ranging from 1:10 to 1:1000. Group fusion similarity search was investigated by varying 16 similarity coefficients, five molecular representations (binary and non-binary) and two group fusion rules using two reference structure set sizes. We used a dataset of 3478 actives and 43,938 inactive molecules and the enrichment was analyzed by means of eROCE. This retrospective study provides optimal similarity search parameters in the case of ALDH1A1 inhibitors.
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43
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Vaz de Lima LAC, Nascimento AS. MolShaCS: A free and open source tool for ligand similarity identification based on Gaussian descriptors. Eur J Med Chem 2013; 59:296-303. [DOI: 10.1016/j.ejmech.2012.11.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2012] [Revised: 11/07/2012] [Accepted: 11/08/2012] [Indexed: 10/27/2022]
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44
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Hu G, Li X, Li Y, Sun X, Liu G, Li W, Huang J, Shen X, Tang Y. Inhibitors of HIV-1 Integrase-Human LEDGF/p75 Interaction Identified from Natural Products via Virtual Screening. CHINESE J CHEM 2012. [DOI: 10.1002/cjoc.201200897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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45
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Hamza A, Wei NN, Hao C, Xiu Z, Zhan CG. A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model. J Biomol Struct Dyn 2012; 31:1236-50. [PMID: 23140256 DOI: 10.1080/07391102.2012.732341] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
In this work, we extend our previous ligand shape-based virtual screening approach by using the scoring function Hamza-Wei-Zhan (HWZ) score and an enhanced molecular shape-density model for the ligands. The performance of the method has been tested against the 40 targets in the Database of Useful Decoys and compared with the performance of our previous HWZ score method. The virtual screening results using the novel ligand shape-based approach demonstrated a favorable improvement (area under the receiver operator characteristics curve AUC = .89 ± .02) and effectiveness (hit rate HR(1%) = 53.0% ± 6.3 and HR(10%) = 71.1% ± 4.9). The comparison of the overall performance of our ligand shape-based method with the highest ligand shape-based virtual screening approach using the data fusion of multi queries showed that our strategy takes into account deeper the chemical information of the set of active ligands. Furthermore, the results indicated that our method are suitable for virtual screening and yields superior prediction accuracy than the other study derived from the data fusion using five queries. Therefore, our novel ligand shape-based screening method constitutes a robust and efficient approach to the 3D similarity screening of small compounds and open the door to a whole new approach to drug design by implementing the method in the structure-based virtual screening.
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Affiliation(s)
- Adel Hamza
- a Department of Pharmaceutical Sciences , College of Pharmacy, University of Kentucky , 789 South Limestone Street, Lexington , KY , 40536 , USA
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46
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Hu G, Li X, Zhang X, Li Y, Ma L, Yang LM, Liu G, Li W, Huang J, Shen X, Hu L, Zheng YT, Tang Y. Discovery of inhibitors to block interactions of HIV-1 integrase with human LEDGF/p75 via structure-based virtual screening and bioassays. J Med Chem 2012; 55:10108-17. [PMID: 23046280 DOI: 10.1021/jm301226a] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This study aims to identify inhibitors that bind at the interface of HIV-1 integrase (IN) and human LEDGF/p75, which represents a novel target for anti-HIV therapy. To date, only a few such inhibitors have been reported. Here structure-based virtual screening was performed to search for the inhibitors from an in-house library of natural products and their derivatives. Among the 38 compounds selected by our strategy, 18 hits were discovered. The two most potent inhibitors showed IC(50) values at 0.32 and 0.26 μM, respectively. Three compounds were subsequently selected for anti-HIV assays, among which (E)-3-(2-chlorophenyl)-1-(2,4-dihydroxyphenyl)prop-2-en-1-one (NPD170) showed the highest antiviral activity (EC(50) = 1.81 μM). The antiviral mechanism of these compounds was further explored, and the results validated that the compounds interrupted the binding of transfected IN to endogenous LEDGF/p75. These findings could be helpful for anti-HIV drug discovery.
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Affiliation(s)
- Guoping Hu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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47
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Hu G, Kuang G, Xiao W, Li W, Liu G, Tang Y. Performance Evaluation of 2D Fingerprint and 3D Shape Similarity Methods in Virtual Screening. J Chem Inf Model 2012; 52:1103-13. [DOI: 10.1021/ci300030u] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Guoping Hu
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guanglin Kuang
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Wen Xiao
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai
Key Laboratory of New Drug Design, School
of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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48
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Hamza A, Wei NN, Zhan CG. Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model 2012; 52:963-74. [PMID: 22486340 DOI: 10.1021/ci200617d] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
In this study, we aimed to develop a new ligand-based virtual screening approach using an effective shape-overlapping procedure and a more robust scoring function (denoted by the HWZ score for convenience). The HWZ score-based virtual screening approach was tested against the compounds for 40 protein targets available in the Database of Useful Decoys (DUD; dud.docking.org/jahn/ ), and the virtual screening performance was evaluated in terms of the area under the receiver operator characteristic (ROC) curve (AUC), enrichment factor (EF), and hit rate (HR), demonstrating an improved overall performance compared to other popularly used approaches examined. In particular, the HWZ score-based virtual screening led to an average AUC value of 0.84 ± 0.02 (95% confidence interval) for the 40 targets. The average HR values at the top 1% and 10% of the active compounds for the 40 targets were 46.3% ± 6.7% and 59.2% ± 4.7%, respectively. In addition, the performance of the HWZ score-based virtual screening approach is less sensitive to the choice of the target.
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Affiliation(s)
- Adel Hamza
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Kentucky , 789 South Limestone Street, Lexington, Kentucky 40536, United States
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49
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Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK. Recognizing Pitfalls in Virtual Screening: A Critical Review. J Chem Inf Model 2012; 52:867-81. [DOI: 10.1021/ci200528d] [Citation(s) in RCA: 294] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas Scior
- Pharmacy Department, Facultad de Ciencias Químicas, Universidad Autónoma de Puebla, Puebla, Pue, México
| | - Andreas Bender
- Unilever Centre for Molecular
Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Gary Tresadern
- Research Informatics & Integrative Genomics, Janssen Research & Development, Calle Jarama 75, Poligono Industrial, Toledo 45007, Spain
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port
St. Lucie, Florida 34987, United States
| | - Karina Martínez-Mayorga
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port
St. Lucie, Florida 34987, United States
| | - Thierry Langer
- Prestwick Chemical, Blvd Gonthier dʼAndernach, F-67400 Illkirch, France
| | - Karina Cuanalo-Contreras
- Pharmacy Department, Facultad de Ciencias Químicas, Universidad Autónoma de Puebla, Puebla, Pue, México
| | - Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, LLC, Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
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50
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Hsieh JH, Yin S, Wang XS, Liu S, Dokholyan NV, Tropsha A. Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening. J Chem Inf Model 2011; 52:16-28. [PMID: 22017385 DOI: 10.1021/ci2002507] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening (VS), which is most frequently manifested in the scoring functions' inability to discriminate between true ligands vs known nonbinders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from VS. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of VS in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE::HMSCORE, ChemScore, PLP, and Chemgauss3, in 6 out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP::LBX). We also compare our method with FLAP::RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP::RBLB, hinting effective directions for best VS applications. We suggest that this integrative VS approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.
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Affiliation(s)
- Jui-Hua Hsieh
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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