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Governa P, Biagi M, Manetti F, Forli S. Consensus screening for a challenging target: the quest for P-glycoprotein inhibitors. RSC Med Chem 2024; 15:720-732. [PMID: 38389870 PMCID: PMC10880898 DOI: 10.1039/d3md00649b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
ATP-binding cassette (ABC) transporters are a large family of proteins involved in membrane transport of a wide variety of substrates. Among them, ABCB1, also known as MDR-1 or P-glycoprotein (P-gp), is the most characterized. By exporting xenobiotics out of the cell, P-gp activity can affect the ADME properties of several drugs. Moreover, P-gp has been found to mediate multidrug resistance in cancer cells. Thus, the inhibition of P-gp activity may lead to increased absorption and/or intracellular accumulation of co-administered drugs, enhancing their effectiveness. Using the human-mouse chimeric cryoEM 3D structure of the P-gp in the inhibitor-bound intermediate form (PDBID: 6qee), approximately 200 000 commercially available natural compounds from the ZINC database were virtually screened. To build a model able to discriminate between substrate and inhibitors, two datasets of compounds with known activity, including P-gp inhibitors, substrates, and inactive molecules were also docked. The best docking pose of selected substrates and inhibitors were used to generate 3D common feature pharmacophoric models that were combined with the Autodock Vina binding energy values to prioritize compounds for visual inspection. With this consensus approach, 13 potential candidates were identified and then tested for their ability to inhibit P-gp, using zosuquidar, a third generation P-gp inhibitor, as a reference drug. Eight compounds were found to be active with 6 of them having an IC50 lower than 5 μM in a membrane-based ATPase activity assay. Moreover, the P-gp inhibitory activity was also confirmed by two different cell-based in vitro methods. Both retrospective and prospective results demonstrate the ability of the combined structure-based pharmacophore modeling and docking-based virtual screening approach to predict novel hit compounds with inhibitory activity toward P-gp. The resulting chemical scaffolds could serve as inspiration for the optimization of novel and more potent P-gp inhibitors.
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Affiliation(s)
- Paolo Governa
- Department of Integrative Structural and Computational Biology, Scripps Research Institute La Jolla CA 92037 USA
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena 53100 Siena Italy
| | - Marco Biagi
- Department of Food and Drug, University of Parma 43121 Parma Italy
| | - Fabrizio Manetti
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena 53100 Siena Italy
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, Scripps Research Institute La Jolla CA 92037 USA
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2
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Li G, Yuan Y, Zhang R. Ensemble of local and global information for Protein-Ligand Binding Affinity Prediction. Comput Biol Chem 2023; 107:107972. [PMID: 37883905 DOI: 10.1016/j.compbiolchem.2023.107972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/07/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023]
Abstract
Accurately predicting protein-ligand binding affinities is crucial for determining molecular properties and understanding their physical effects. Neural networks and transformers are the predominant methods for sequence modeling, and both have been successfully applied independently for protein-ligand binding affinity prediction. As local and global information of molecules are vital for protein-ligand binding affinity prediction, we aim to combine bi-directional gated recurrent unit (BiGRU) and convolutional neural network (CNN) to effectively capture both local and global molecular information. Additionally, attention mechanisms can be incorporated to automatically learn and adjust the level of attention given to local and global information, thereby enhancing the performance of the model. To achieve this, we propose the PLAsformer approach, which encodes local and global information of molecules using 3DCNN and BiGRU with attention mechanism, respectively. This approach enhances the model's ability to encode comprehensive local and global molecular information. PLAsformer achieved a Pearson's correlation coefficient of 0.812 and a Root Mean Square Error (RMSE) of 1.284 when comparing experimental and predicted affinity on the PDBBind-2016 dataset. These results surpass the current state-of-the-art methods for binding affinity prediction. The high accuracy of PLAsformer's predictive performance, along with its excellent generalization ability, is clearly demonstrated by these findings.
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Affiliation(s)
- Gaili Li
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Yongna Yuan
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
| | - Ruisheng Zhang
- School of Information science and Engineering, Lanzhou University, Lanzhou 730000, China.
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3
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Fischer A, Smieško M, Sellner M, Lill MA. Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results. J Med Chem 2021; 64:2489-2500. [PMID: 33617246 DOI: 10.1021/acs.jmedchem.0c02227] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.
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Affiliation(s)
- André Fischer
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Martin Smieško
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Manuel Sellner
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
| | - Markus A Lill
- Computational Pharmacy, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 61, 4056 Basel, Switzerland
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4
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Stein RM, Yang Y, Balius TE, O'Meara MJ, Lyu J, Young J, Tang K, Shoichet BK, Irwin JJ. Property-Unmatched Decoys in Docking Benchmarks. J Chem Inf Model 2021; 61:699-714. [PMID: 33494610 DOI: 10.1021/acs.jcim.0c00598] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.
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Affiliation(s)
- Reed M Stein
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Ying Yang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Trent E Balius
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., P.O. Box B, Frederick, Maryland 21702, United States
| | - Matt J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Palmer Commons, 100 Washtenaw Ave. #2017, Ann Arbor, Michigan 48109, United States
| | - Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Jennifer Young
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Khanh Tang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States
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5
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ERRγ ligand HPB2 upregulates BDNF-TrkB and enhances dopaminergic neuronal phenotype. Pharmacol Res 2021; 165:105423. [PMID: 33434621 DOI: 10.1016/j.phrs.2021.105423] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/24/2020] [Accepted: 12/31/2020] [Indexed: 12/27/2022]
Abstract
Brain derived neurotrophic factor (BDNF) promotes maturation of dopaminergic (DAergic) neurons in the midbrain and positively regulates their maintenance and outgrowth. Therefore, understanding the mechanisms regulating the BDNF signaling pathway in DAergic neurons may help discover potential therapeutic strategies for neuropsychological disorders associated with dysregulation of DAergic neurotransmission. Because estrogen-related receptor gamma (ERRγ) is highly expressed in both the fetal nervous system and adult brains during DAergic neuronal differentiation, and it is involved in regulating the DAergic neuronal phenotype, we asked in this study whether ERRγ ligand regulates BDNF signaling and subsequent DAergic neuronal phenotype. Based on the X-ray crystal structures of the ligand binding domain of ERRγ, we designed and synthesized the ERRγ agonist, (E)-4-hydroxy-N'-(4-(phenylethynyl)benzylidene)benzohydrazide (HPB2) (Kd value, 8.35 μmol/L). HPB2 increased BDNF mRNA and protein levels, and enhanced the expression of the BDNF receptor tropomyosin receptor kinase B (TrkB) in human neuroblastoma SH-SY5Y, differentiated Lund human mesencephalic (LUHMES) cells, and primary ventral mesencephalic (VM) neurons. HPB2-induced upregulation of BDNF was attenuated by GSK5182, an antagonist of ERRγ, and siRNA-mediated ERRγ silencing. HPB2-induced activation of extracellular-signal-regulated kinase (ERK) and phosphorylation of cAMP-response element binding protein (CREB) was responsible for BDNF upregulation in SH-SY5Y cells. HPB2 enhanced the DAergic neuronal phenotype, namely upregulation of tyrosine hydroxylase (TH) and DA transporter (DAT) with neurite outgrowth, both in SH-SY5Y and primary VM neurons, which was interfered by the inhibition of BDNF-TrkB signaling, ERRγ knockdown, or blockade of ERK activation. HPB2 also upregulated BDNF and TH in the striatum and induced neurite elongation in the substantia nigra of mice brain. In conclusion, ERRγ activation regulated BDNF expression and the subsequent DAergic neuronal phenotype in neuronal cells. Our results might provide new insights into the mechanism underlying the regulation of BDNF expression, leading to novel therapeutic strategies for neuropsychological disorders associated with DAergic dysregulation.
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Baruah VJ, Paul R, Gogoi D, Mazumder N, Chakraborty S, Das A, Mondal TK, Sarmah B. Integrated computational approach toward discovery of multi-targeted natural products from Thumbai ( Leucas aspera) for attuning NKT cells. J Biomol Struct Dyn 2020; 40:2893-2907. [PMID: 33179569 DOI: 10.1080/07391102.2020.1844056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
A multi-omics-based approach targeting the plant-based natural products from Thumbai (Leucas aspera), an important yet untapped potential source of many therapeutic agents for myriads of immunological conditions and genetic disorders, was conceptualized to reconnoiter its potential biomedical application. A library of 79 compounds from this plant was created, out of which 9 compounds qualified the pharmacokinetics parameters. Reverse pharmacophore technique for target fishing of the screened compounds was executed through which renin receptor (ATP6AP2) and thymidylate kinase (DTYMK) were identified as potential targets. Network biology approaches were used to comprehend and validate the functional, biochemical and clinical relevance of the targets. The target-ligand interaction and subsequent stability parameters at molecular scale were investigated using multiple strategies including molecular modeling, pharmacophore approaches and molecular dynamics simulation. Herein, isololiolide and 4-hydroxy-2-methoxycinnamaldehyde were substantiated as the lead molecules exhibiting comparatively the best binding affinity against the two putative protein targets. These natural lead products from L. aspera and the combinatorial effects may have plausible medical applications in a wide variety of neurodegenerative, genetic and developmental disorders. The lead molecules also exhibit promising alternative in diagnostics and therapeutics through immuno-modulation targeting natural killer T-cell function in transplantation-related pathogenesis, autoimmune and other immunological disorders.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Vishwa Jyoti Baruah
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India
| | - Rasana Paul
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India
| | - Dhrubajyoti Gogoi
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | | | - Aparoopa Das
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India
| | - Tapan Kumar Mondal
- ICAR-National Institute for Plant Biotechnology, LBS Centre, IARI Campus, New Delhi, India
| | - Bhaswati Sarmah
- Department of Plant Breeding and Genetics, Assam Agricultural University, Jorhat, Assam, India
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7
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Kwon Y, Shin WH, Ko J, Lee J. AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks. Int J Mol Sci 2020; 21:E8424. [PMID: 33182567 PMCID: PMC7697539 DOI: 10.3390/ijms21228424] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 10/24/2020] [Accepted: 11/07/2020] [Indexed: 02/04/2023] Open
Abstract
Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.
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Affiliation(s)
- Yongbeom Kwon
- Department of Chemistry, Kangwon National University, Gangwon-do, Chuncheon 24341, Korea;
| | - Woong-Hee Shin
- Department of Chemical Science Education, Sunchon National University, Jeollanam-do, Suncheon 57922, Korea
| | - Junsu Ko
- Arontier, 241 Gangnam-daero, Seocho-gu, Seoul 06735, Korea
| | - Juyong Lee
- Department of Chemistry, Kangwon National University, Gangwon-do, Chuncheon 24341, Korea;
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8
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Yi JJ, Heo SY, Ju JH, Oh BR, Son WS, Seo JW. Synthesis of two new lipid mediators from docosahexaenoic acid by combinatorial catalysis involving enzymatic and chemical reaction. Sci Rep 2020; 10:18849. [PMID: 33139849 PMCID: PMC7606508 DOI: 10.1038/s41598-020-76005-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/21/2020] [Indexed: 12/31/2022] Open
Abstract
Omega-3 polyunsaturated fatty acids (PUFAs) have been known to have beneficial effects in the prevention of various diseases. Recently, it was identified that the bioactivities of omega-3 are related to lipid mediators, called pro-resolving lipid mediators (SPMs), converted from PUFAs, so they have attracted much attention as potential pharmaceutical targets. Here, we aimed to build an efficient production system composed of enzymatic and chemical catalysis that converts docosahexaenoic acid (DHA) into lipid mediators. The cyanobacterial lipoxygenase, named Osc-LOX, was identified and characterized, and the binding poses of enzyme and substrates were predicted by ligand docking simulation. DHA was converted into three lipid mediators, a 17S-hydroxy-DHA, a 7S,17S-dihydroxy-DHA (RvD5), and a 7S,15R-dihydroxy-16S,17S-epoxy-DPA (new type), by an enzymatic reaction and deoxygenation. Also, two lipid mediators, 7S,15R,16S,17S-tetrahydroxy-DPA (new type) and 7S,16R,17S-trihydroxy-DHA (RvD2), were generated from 7S,15R-dihydroxy-16S,17S-epoxy-DPA by a chemical reaction. Our study suggests that discovering new enzymes that have not been functionally characterized would be a powerful strategy for producing various lipid mediators. Also, this combination catalysis approach including biological and chemical reactions could be an effective production system for the manufacturing lipid mediators.
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Affiliation(s)
- Jong-Jae Yi
- Microbial Biotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeongeup-Si, 56212, Republic of Korea
- Department of Pharmacy, College of Pharmacy and Institute of Pharmaceutical Sciences, CHA University, Pocheon-Si, Gyeonggi-do, 11160, Republic of Korea
| | - Sun-Yeon Heo
- Microbial Biotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeongeup-Si, 56212, Republic of Korea
| | - Jung-Hyun Ju
- Microbial Biotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeongeup-Si, 56212, Republic of Korea
| | - Baek-Rock Oh
- Microbial Biotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeongeup-Si, 56212, Republic of Korea
| | - Woo Sung Son
- Department of Pharmacy, College of Pharmacy and Institute of Pharmaceutical Sciences, CHA University, Pocheon-Si, Gyeonggi-do, 11160, Republic of Korea.
| | - Jeong-Woo Seo
- Microbial Biotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Jeongeup-Si, 56212, Republic of Korea.
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da Silva IR, Parise MR, Pereira M, da Silva RA. Prospecting for new catechol- O-methyltransferase (COMT) inhibitors as a potential treatment for Parkinson's disease: a study by molecular dynamics and structure-based virtual screening. J Biomol Struct Dyn 2020; 39:5872-5891. [PMID: 32691671 DOI: 10.1080/07391102.2020.1794963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative, chronic, and progressive disease, common in the elderly. The catechol-O-methyltransferase (COMT) is a monomeric enzyme involved in dopamine (DA) degradation, the neurotransmitter in deficit in patients with PD. The reference treatment of PD consists of levodopa (L-dopa) administration, which is the precursor of DA. The inhibition of COMT is an adjuvant treatment in PD since it keeps DA levels constant. The goal of this study was to identify drug candidates capable of inhibiting COMT for the treatment of PD and identify important fragments of these molecules. Initially, we analyzed the flexibility of COMT and defined its main conformations in solution regarding the absence (system I) and presence of the S-adenosyl-L-methionine (SAM) cofactor (system II) through molecular dynamics (MD) simulations. Two regions in these structures were selected for molecular docking, firstly the entire cavity where the cofactor and substrates are bound and secondly the specific biding region of the enzyme substrates. Based on the conformations of the MD, the virtual screening (VS) was performed against FDA Approved and Zinc Natural Products databases aiming at the selection of the best compounds. Subsequently, the absorption, distribution, metabolization, excretion, and toxicity (ADMET) properties, as well as drug-score and drug-likeness indexes of the most promising compounds were analyzed. After a detailed analysis of the compounds selected by structure-based VS, it was possible to highlight the fragments most frequently involved in their stability: 2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole, 9H-Benz(c)indole(3,2,1-ij)(1,5)naphthyridin-9-one and (10R,13S)-10,13-dimethyl-1,2,6,7,8,9,11,12,14,15,16,17dodecahydrocyclopenta[a]phenanthren-3-one. The identification of these potential fragments is essential for the prospection of more specific inhibitors against COMT using the technique of Fragment-based lead discovery (FBLD). Besides, this study allowed us to identify the potential COMT inhibitors through a complete understanding of molecular-level interactions based on the flexibility of this protein.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Michelle Rocha Parise
- Laboratório de Farmacologia e Fisiologia, Universidade Federal de Jataí, Jataí, Brasil
| | - Maristela Pereira
- Laboratório de Biologia Molecular, Universidade Federal de Goiás, Goiânia, Brasil
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Duarte Y, Cáceres J, Sepúlveda RV, Arriagada D, Olivares P, Díaz-Franulic I, Stehberg J, González-Nilo F. Novel TRPV1 Channel Agonists With Faster and More Potent Analgesic Properties Than Capsaicin. Front Pharmacol 2020; 11:1040. [PMID: 32760273 PMCID: PMC7372189 DOI: 10.3389/fphar.2020.01040] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 06/26/2020] [Indexed: 01/12/2023] Open
Abstract
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a member of the family of Transient Receptor Potential (TRP) channels that acts as a molecular detector of noxious signals in primary sensory neurons. Activated by capsaicin, heat, voltage and protons, it is also well known for its desensitization, which led to the medical use of topically applied TRPV1 agonist capsaicin for its long-lasting analgesic effects. Here we report three novel small molecules, which were identified using a Structure-Based Virtual Screening for TRPV1 from the ZINC database. The three compounds were tested using electrophysiological assays, which confirmed their capsaicin-like agonist activity. von Frey filaments were used to measure the analgesic effects of the compounds applied topically on tactile allodynia induced by intra-plantar carrageenan. All compounds had anti-nociceptive activity, but two of them showed faster and longer lasting analgesic effects than capsaicin. The present results suggest that TRPV1 agonists different from capsaicin could be used to develop topical analgesics with faster onset and more potent effects.
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Affiliation(s)
- Yorley Duarte
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Javier Cáceres
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Romina V Sepúlveda
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Diego Arriagada
- Laboratorio de Neurobiologia, Instituto de Ciencias Biomédicas, Facultad de Medicina y Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Pedro Olivares
- Laboratorio de Neurobiologia, Instituto de Ciencias Biomédicas, Facultad de Medicina y Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Ignacio Díaz-Franulic
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile.,Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
| | - Jimmy Stehberg
- Laboratorio de Neurobiologia, Instituto de Ciencias Biomédicas, Facultad de Medicina y Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile
| | - Fernando González-Nilo
- Center for Bioinformatics and Integrative Biology, Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago, Chile.,Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
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11
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Wang X, Perryman AL, Li SG, Paget SD, Stratton TP, Lemenze A, Olson AJ, Ekins S, Kumar P, Freundlich JS. Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis. ACS Infect Dis 2019; 5:2148-2163. [PMID: 31625383 DOI: 10.1021/acsinfecdis.9b00295] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified N-(3-methoxyphenyl)-7-nitrobenzo[c][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus in vitro cultured M. tuberculosis. When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid F420H2-dependent biotransformation within M. tuberculosis to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured M. tuberculosis. This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.
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Affiliation(s)
- Xin Wang
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Alexander L. Perryman
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Shao-Gang Li
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Steve D. Paget
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Thomas P. Stratton
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Alex Lemenze
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Arthur J. Olson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, Room MB112/Mail Drop MB5, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
| | - Pradeep Kumar
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
| | - Joel S. Freundlich
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
- Division of Infectious Disease, Department of Medicine, and the Ruy V. Lourenço Center for the Study of Emerging and Reemerging Pathogens, Rutgers University−New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States
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12
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da Silva LS, Barbosa UR, Silva LDC, Soares CMA, Pereira M, da Silva RA. Identification of a new antifungal compound against isocitrate lyase of Paracoccidioides brasiliensis. Future Microbiol 2019; 14:1589-1606. [DOI: 10.2217/fmb-2019-0166] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Aim: To perform virtual screening of compounds based on natural products targeting isocitrate lyase of Paracoccidioides brasiliensis. Materials & methods: Homology modeling and molecular dynamics simulations were applied in order to obtain conformational models for virtual screening. The selected hits were tested in vitro against enzymatic activity of ICL of the dimorphic fungus P. brasiliensis and growth of the Paracoccidioides spp. The cytotoxicity and selectivity index of the compounds were defined. Results & conclusion: Carboxamide, lactone and β-carboline moieties were identified as interesting chemical groups for the design of new antifungal compounds. The compounds inhibited ICL of the dimorphic fungus P. brasiliensis activity. The compound 4559339 presented minimum inhibitory concentration of 7.3 μg/ml in P. brasiliensis with fungicidal effect at this concentration. Thus, a new potential antifungal against P. brasiliensis is proposed.
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Affiliation(s)
- Luciane S da Silva
- LBM – Laboratory of Molecular Biology, Universidade Federal de Goiás, Goiânia, Goiás, 74690-900, Brazil
- Collaborative Nucleus of Biosystems, Universidade Federal de Goiás, Jataí, Goiás, 75804-020, Brazil
| | - Uessiley R Barbosa
- Collaborative Nucleus of Biosystems, Universidade Federal de Goiás, Jataí, Goiás, 75804-020, Brazil
- UNIFIMES, Centro Universitário de Mineiros, Mineiros, Goiás, 75833-130, Brazil
| | - Lívia do C Silva
- LBM – Laboratory of Molecular Biology, Universidade Federal de Goiás, Goiânia, Goiás, 74690-900, Brazil
| | - Célia MA Soares
- LBM – Laboratory of Molecular Biology, Universidade Federal de Goiás, Goiânia, Goiás, 74690-900, Brazil
| | - Maristela Pereira
- LBM – Laboratory of Molecular Biology, Universidade Federal de Goiás, Goiânia, Goiás, 74690-900, Brazil
| | - Roosevelt A da Silva
- Collaborative Nucleus of Biosystems, Universidade Federal de Goiás, Jataí, Goiás, 75804-020, Brazil
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13
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Molecular Docking: Shifting Paradigms in Drug Discovery. Int J Mol Sci 2019; 20:ijms20184331. [PMID: 31487867 PMCID: PMC6769923 DOI: 10.3390/ijms20184331] [Citation(s) in RCA: 724] [Impact Index Per Article: 144.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 09/02/2019] [Accepted: 09/02/2019] [Indexed: 12/11/2022] Open
Abstract
Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
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14
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Xia J, Flynn W, Gallicchio E, Uplinger K, Armstrong JD, Forli S, Olson AJ, Levy RM. Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network. J Chem Inf Model 2019; 59:1382-1397. [PMID: 30758197 DOI: 10.1021/acs.jcim.8b00817] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are ∼10000 simulated complexes, ∼1 million replicas, and ∼2000 μs of aggregated MD simulations. Running AsyncRE MD simulations on the WCG requires accepting a trade-off between the number of replicas that can be run (breadth) and the number of full RE cycles that can be completed per replica (depth). As compared with synchronous Replica Exchange (SyncRE) running on tightly coupled clusters like XSEDE, on the WCG many more replicas can be launched simultaneously on heterogeneous distributed hardware, but each full RE cycle requires more overhead. We compared the WCG results with that from AutoDock and more advanced RE simulations including the use of flattening potentials to accelerate sampling of selected degrees of freedom of ligands and/or receptors related to slow dynamics due to high energy barriers. We propose a suitable strategy of RE simulations to refine high throughput docking results which can be matched to corresponding computing resources: from HPC clusters, to small or medium-size distributed campus grids, and finally to massive-scale computing networks including millions of CPUs like the resources available on the WCG.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Emilio Gallicchio
- Department of Chemistry , CUNY Brooklyn College , Brooklyn , New York 11210 , United States
| | - Keith Uplinger
- IBM WCG Team, 1177 South Belt Line Road , Coppell , Texas 75019 , United States
| | - Jonathan D Armstrong
- IBM WCG Team, 11400 Burnet Road , 0453B129, Austin , Texas 78758 , United States
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Arthur J Olson
- Department of Integrative Structural and Computational Biology , The Scripps Research Institute , La Jolla , California 92037-1000 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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15
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Williams TL, Senft SL, Yeo J, Martín-Martínez FJ, Kuzirian AM, Martin CA, DiBona CW, Chen CT, Dinneen SR, Nguyen HT, Gomes CM, Rosenthal JJC, MacManes MD, Chu F, Buehler MJ, Hanlon RT, Deravi LF. Dynamic pigmentary and structural coloration within cephalopod chromatophore organs. Nat Commun 2019; 10:1004. [PMID: 30824708 PMCID: PMC6397165 DOI: 10.1038/s41467-019-08891-x] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 01/23/2019] [Indexed: 01/08/2023] Open
Abstract
Chromatophore organs in cephalopod skin are known to produce ultra-fast changes in appearance for camouflage and communication. Light-scattering pigment granules within chromatocytes have been presumed to be the sole source of coloration in these complex organs. We report the discovery of structural coloration emanating in precise register with expanded pigmented chromatocytes. Concurrently, using an annotated squid chromatophore proteome together with microscopy, we identify a likely biochemical component of this reflective coloration as reflectin proteins distributed in sheath cells that envelop each chromatocyte. Additionally, within the chromatocytes, where the pigment resides in nanostructured granules, we find the lens protein Ω- crystallin interfacing tightly with pigment molecules. These findings offer fresh perspectives on the intricate biophotonic interplay between pigmentary and structural coloration elements tightly co-located within the same dynamic flexible organ - a feature that may help inspire the development of new classes of engineered materials that change color and pattern.
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Affiliation(s)
- Thomas L Williams
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Stephen L Senft
- The Eugene Bell Center, The Marine Biological Laboratory, Woods Hole, MA, 02543, USA
| | - Jingjie Yeo
- Department of Biomedical Engineering, Tufts University, Medford, MA, 02155, USA.,Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Institute of High Performance Computing, A*STAR, Singapore, 138632, Singapore
| | - Francisco J Martín-Martínez
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alan M Kuzirian
- The Eugene Bell Center, The Marine Biological Laboratory, Woods Hole, MA, 02543, USA
| | - Camille A Martin
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Christopher W DiBona
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Chun-Teh Chen
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sean R Dinneen
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Hieu T Nguyen
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, 03824, USA
| | - Conor M Gomes
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA
| | - Joshua J C Rosenthal
- The Eugene Bell Center, The Marine Biological Laboratory, Woods Hole, MA, 02543, USA
| | - Matthew D MacManes
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, 03824, USA
| | - Feixia Chu
- Department of Molecular, Cellular, and Biomedical Sciences, University of New Hampshire, Durham, NH, 03824, USA
| | - Markus J Buehler
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Roger T Hanlon
- The Eugene Bell Center, The Marine Biological Laboratory, Woods Hole, MA, 02543, USA.
| | - Leila F Deravi
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, 02115, USA.
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16
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Johari S, Sharma A, Sinha S, Das A. Integrating pharmacophore mapping, virtual screening, density functional theory, molecular simulation towards the discovery of novel apolipoprotein (apoE ε4) inhibitors. Comput Biol Chem 2019; 79:83-90. [PMID: 30743160 DOI: 10.1016/j.compbiolchem.2018.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 12/25/2018] [Indexed: 11/30/2022]
Abstract
AIM An integrated protocol of virtual screening involving molecular docking, pharmacophore probing, and simulations was established to identify small novel molecules targeting crucial residues involved in the variant apoE ε4 to mimic its behavior as apoE2 thereby eliminating the amyloid plaque accumulation and facilitating its clearance. MATERIALS AND METHODS An excellent ligand-based and structure-based approach was made to identify common pharmacophoric features involving structure-based docking with respect to apoE ε4 leading to the development of apoE ε4 inhibitors possessing new scaffolds. An effort was made to design multiple-substituted triazine derivatives series bearing a novel scaffold. A structure-based pharmacophore mapping was developed to explore the binding sites of apoE ε4 which was taken into consideration. Subsequently, virtual screening, ADMET, DFT searches were at work to narrow down the proposed hits to be forwarded as a potential drug likes candidates. Further, the binding patterns of the best-proposed hits were studied and were forwarded for molecular dynamic simulations of 10 ns for its structural optimization. RESULTS Selectivity profile for the most promising candidates was studied, revealing significantly C13 and C15 to be the most potent compounds. The proposed hits can be forwarded for further study against apoE ε4 involved in neurological disorder Alzheimer's.
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Affiliation(s)
- Surabhi Johari
- Centre for Biotechnology and Bioinformatics Studies, Dibrugarh University, Dibrugarh, 786004, Assam, India.
| | - Ashwani Sharma
- Laboratory of Molecular Electrochemistry UMR CNRS - P7 7591, 75205, Paris Cedex 13, France
| | - Subrata Sinha
- Centre for Biotechnology and Bioinformatics Studies, Dibrugarh University, Dibrugarh, 786004, Assam, India
| | - Aparoop Das
- Department of Pharmaceutical Sciences, Dibrugarh University, Dibrugarh, 786004, Assam, India
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17
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Solvents to Fragments to Drugs: MD Applications in Drug Design. Molecules 2018; 23:molecules23123269. [PMID: 30544890 PMCID: PMC6321499 DOI: 10.3390/molecules23123269] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/02/2018] [Accepted: 12/03/2018] [Indexed: 01/24/2023] Open
Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
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18
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Kataria A, Singh J, Kundu B. Identification and validation of
l
‐asparaginase as a potential metabolic target against
Mycobacterium tuberculosis. J Cell Biochem 2018; 120:143-154. [DOI: 10.1002/jcb.27169] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 05/18/2018] [Indexed: 12/15/2022]
Affiliation(s)
- Arti Kataria
- Kusuma School of Biological Sciences Indian Institute of Technology Delhi New Delhi India
| | - Jasdeep Singh
- Kusuma School of Biological Sciences Indian Institute of Technology Delhi New Delhi India
| | - Bishwajit Kundu
- Kusuma School of Biological Sciences Indian Institute of Technology Delhi New Delhi India
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19
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Xia J, Flynn W, Levy RM. Improving Prediction Accuracy of Binding Free Energies and Poses of HIV Integrase Complexes Using the Binding Energy Distribution Analysis Method with Flattening Potentials. J Chem Inf Model 2018; 58:1356-1371. [PMID: 29927237 PMCID: PMC6287956 DOI: 10.1021/acs.jcim.8b00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
To accelerate conformation sampling of slow dynamics from receptor or ligand, we introduced flattening potentials on selected bonded and nonbonded intramolecular interactions to the binding energy distribution analysis method (BEDAM) for calculating absolute binding free energies of protein-ligand complexes using an implicit solvent model and implemented flattening BEDAM using the asynchronous replica exchange (AsyncRE) framework for performing large scale replica exchange molecular dynamics (REMD) simulations. The advantage of using the flattening feature to reduce high energy barriers was exhibited first by the p-xylene-T4 lysozyme complex, where the intramolecular interactions of a protein side chain on the binding site were flattened to accelerate the conformational transition of the side chain from the trans to the gauche state when the p-xylene ligand is present in the binding site. Much more extensive flattening BEDAM simulations were performed for 53 experimental binders and 248 nonbinders of HIV-1 integrase which formed the SAMPL4 challenge, with the total simulation time of 24.3 μs. We demonstrated that the flattening BEDAM simulations not only substantially increase the number of true positives (and reduce false negatives) but also improve the prediction accuracy of binding poses of experimental binders. Furthermore, the values of area under the curve (AUC) of receiver operating characteristic (ROC) and the enrichment factors at 20% cutoff calculated from the flattening BEDAM simulations were improved significantly in comparison with that of simulations without flattening as we previously reported for the whole SAMPL4 database. Detailed analysis found that the improved ability to discriminate the binding free energies between the binders and nonbinders is due to the fact that the flattening simulations reduce the reorganization free energy penalties of binders and decrease the overlap of binding free energy distributions of binders relative to that of nonbinders. This happens because the conformational ensemble distributions for both the ligand and protein in solution match those at the fully coupled (complex) state more closely when the systems are more fully sampled after the flattening potentials are applied to the intermediate states.
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Affiliation(s)
- Junchao Xia
- Center for Biophysics and Computational Biology and Department of Physics , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - William Flynn
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
| | - Ronald M Levy
- Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States
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20
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Kilburg D, Gallicchio E. Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes. Front Mol Biosci 2018; 5:22. [PMID: 29568737 PMCID: PMC5852065 DOI: 10.3389/fmolb.2018.00022] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Accepted: 02/21/2018] [Indexed: 01/24/2023] Open
Abstract
The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.
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Affiliation(s)
- Denise Kilburg
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, Brooklyn, NY, United States.,Ph.D. Program in Chemistry, The Graduate Center, City University of New York, New York, NY, United States.,Ph.D. Program in Biochemistry, The Graduate Center, City University of New York, New York, NY, United States
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21
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Zhang B, Kilburg D, Eastman P, Pande VS, Gallicchio E. Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. J Comput Chem 2017; 38:740-752. [PMID: 28160511 DOI: 10.1002/jcc.24745] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/05/2017] [Accepted: 01/08/2017] [Indexed: 11/07/2022]
Abstract
We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Baofeng Zhang
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210
| | - Denise Kilburg
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
| | - Peter Eastman
- Department of Bioengineering, Stanford University, Stanford, California, 94035
| | - Vijay S Pande
- Department of Chemistry, Stanford University, Stanford, California, 94035
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
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22
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Ekins S, Liebler J, Neves BJ, Lewis WG, Coffee M, Bienstock R, Southan C, Andrade CH. Illustrating and homology modeling the proteins of the Zika virus. F1000Res 2016; 5:275. [PMID: 27746901 DOI: 10.12688/f1000research.8213.1] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/29/2016] [Indexed: 12/28/2022] Open
Abstract
The Zika virus (ZIKV) is a flavivirus of the family Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either in vitro or in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, USA; Collaborations Pharmaceuticals Inc., Fuquay-Varina, NC, USA; Collaborative Drug Discovery Inc, Burlingame, CA, USA
| | | | - Bruno J Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, GO, Brazil
| | - Warren G Lewis
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Megan Coffee
- The International Rescue Committee, New York, NY, USA
| | | | | | - Carolina H Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, GO, Brazil
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23
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Zhang B, D’Erasmo M, Murelli RP, Gallicchio E. Free Energy-Based Virtual Screening and Optimization of RNase H Inhibitors of HIV-1 Reverse Transcriptase. ACS OMEGA 2016; 1:435-447. [PMID: 27713931 PMCID: PMC5046171 DOI: 10.1021/acsomega.6b00123] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 09/07/2016] [Indexed: 06/06/2023]
Abstract
We report the results of a binding free energy-based virtual screening campaign of a library of 77 α-hydroxytropolone derivatives against the challenging RNase H active site of the reverse transcriptase (RT) enzyme of human immunodeficiency virus-1. Multiple protonation states, rotamer states, and binding modalities of each compound were individually evaluated. The work involved more than 300 individual absolute alchemical binding free energy parallel molecular dynamics calculations and over 1 million CPU hours on national computing clusters and a local campus computational grid. The thermodynamic and structural measures obtained in this work rationalize a series of characteristics of this system useful for guiding future synthetic and biochemical efforts. The free energy model identified key ligand-dependent entropic and conformational reorganization processes difficult to capture using standard docking and scoring approaches. Binding free energy-based optimization of the lead compounds emerging from the virtual screen has yielded four compounds with very favorable binding properties, which will be the subject of further experimental investigations. This work is one of the few reported applications of advanced-binding free energy models to large-scale virtual screening and optimization projects. It further demonstrates that, with suitable algorithms and automation, advanced-binding free energy models can have a useful role in early-stage drug-discovery programs.
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Affiliation(s)
- Baofeng Zhang
- Department
of Chemistry, Brooklyn
College, City University of New York, Brooklyn, New York 11210, United States
| | - Michael
P. D’Erasmo
- Department
of Chemistry, Brooklyn
College, City University of New York, Brooklyn, New York 11210, United States
- Ph.D. Program in Chemistry and Ph.D. Program in
Biochemistry, The Graduate Center of the
City University of New York, New
York, New York 10016, United States
| | - Ryan P. Murelli
- Department
of Chemistry, Brooklyn
College, City University of New York, Brooklyn, New York 11210, United States
- Ph.D. Program in Chemistry and Ph.D. Program in
Biochemistry, The Graduate Center of the
City University of New York, New
York, New York 10016, United States
| | - Emilio Gallicchio
- Department
of Chemistry, Brooklyn
College, City University of New York, Brooklyn, New York 11210, United States
- Ph.D. Program in Chemistry and Ph.D. Program in
Biochemistry, The Graduate Center of the
City University of New York, New
York, New York 10016, United States
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24
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Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge. J Comput Aided Mol Des 2016; 31:71-85. [PMID: 27677749 DOI: 10.1007/s10822-016-9968-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 09/08/2016] [Indexed: 12/11/2022]
Abstract
Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.
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Kilburg D, Gallicchio E. Recent Advances in Computational Models for the Study of Protein-Peptide Interactions. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2016; 105:27-57. [PMID: 27567483 DOI: 10.1016/bs.apcsb.2016.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.
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Affiliation(s)
- D Kilburg
- Brooklyn College, Brooklyn, NY, United States; The Graduate Center of the City University of New York, New York, NY, United States
| | - E Gallicchio
- Brooklyn College, Brooklyn, NY, United States; The Graduate Center of the City University of New York, New York, NY, United States.
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Mentes A, Deng NJ, Vijayan RSK, Xia J, Gallicchio E, Levy RM. Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation. J Chem Theory Comput 2016; 12:2459-70. [PMID: 27070865 PMCID: PMC4862910 DOI: 10.1021/acs.jctc.6b00134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.
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Affiliation(s)
- Ahmet Mentes
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Nan-Jie Deng
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - R. S. K. Vijayan
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Junchao Xia
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, New York 11210, United States
| | - Ronald M. Levy
- Department of Chemistry, Temple University, Philadelphia, Pennsylvania 19122, United States
- Center for Biophysics and Computational Biology and Institute for Computational Molecular Science, Temple University, Philadelphia, Pennsylvania 19122, United States
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Ekins S, Liebler J, Neves BJ, Lewis WG, Coffee M, Bienstock R, Southan C, Andrade CH. Illustrating and homology modeling the proteins of the Zika virus. F1000Res 2016; 5:275. [PMID: 27746901 PMCID: PMC5040154 DOI: 10.12688/f1000research.8213.2] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/30/2016] [Indexed: 12/20/2022] Open
Abstract
The Zika virus (ZIKV) is a flavivirus of the family
Flaviviridae, which is similar to dengue virus, yellow fever and West Nile virus. Recent outbreaks in South America, Latin America, the Caribbean and in particular Brazil have led to concern for the spread of the disease and potential to cause Guillain-Barré syndrome and microcephaly. Although ZIKV has been known of for over 60 years there is very little in the way of knowledge of the virus with few publications and no crystal structures. No antivirals have been tested against it either
in vitro or
in vivo. ZIKV therefore epitomizes a neglected disease. Several suggested steps have been proposed which could be taken to initiate ZIKV antiviral drug discovery using both high throughput screens as well as structure-based design based on homology models for the key proteins. We now describe preliminary homology models created for NS5, FtsJ, NS4B, NS4A, HELICc, DEXDc, peptidase S7, NS2B, NS2A, NS1, E stem, glycoprotein M, propeptide, capsid and glycoprotein E using SWISS-MODEL. Eleven out of 15 models pass our model quality criteria for their further use. While a ZIKV glycoprotein E homology model was initially described in the immature conformation as a trimer, we now describe the mature dimer conformer which allowed the construction of an illustration of the complete virion. By comparing illustrations of ZIKV based on this new homology model and the dengue virus crystal structure we propose potential differences that could be exploited for antiviral and vaccine design. The prediction of sites for glycosylation on this protein may also be useful in this regard. While we await a cryo-EM structure of ZIKV and eventual crystal structures of the individual proteins, these homology models provide the community with a starting point for structure-based design of drugs and vaccines as well as a for computational virtual screening.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry, Fuquay-Varina, NC, USA; Collaborations Pharmaceuticals Inc., Fuquay-Varina, NC, USA; Collaborative Drug Discovery Inc, Burlingame, CA, USA
| | | | - Bruno J Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, GO, Brazil
| | - Warren G Lewis
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Megan Coffee
- The International Rescue Committee, New York, NY, USA
| | | | | | - Carolina H Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, GO, Brazil
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Anighoro A, Bajorath J. Three-Dimensional Similarity in Molecular Docking: Prioritizing Ligand Poses on the Basis of Experimental Binding Modes. J Chem Inf Model 2016; 56:580-7. [DOI: 10.1021/acs.jcim.5b00745] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Andrew Anighoro
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science
Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal
Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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Lin JH. Review structure- and dynamics-based computational design of anticancer drugs. Biopolymers 2015; 105:2-9. [DOI: 10.1002/bip.22744] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 09/16/2015] [Accepted: 09/16/2015] [Indexed: 01/13/2023]
Affiliation(s)
- Jung Hsin Lin
- Research Center for Applied Sciences, Academia Sinica; Taipei Taiwan
- Institute of Biomedical Sciences, Academia Sinica; Taipei Taiwan
- School of Pharmacy; National Taiwan University; Taipei Taiwan
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Forli S. Charting a Path to Success in Virtual Screening. Molecules 2015; 20:18732-58. [PMID: 26501243 PMCID: PMC4630810 DOI: 10.3390/molecules201018732] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 10/07/2015] [Accepted: 10/12/2015] [Indexed: 12/27/2022] Open
Abstract
Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.
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Affiliation(s)
- Stefano Forli
- Molecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.
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31
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Hu B, Cui F, Yin F, Zeng X, Sun Y, Li Y. Caffeoylquinic acids competitively inhibit pancreatic lipase through binding to the catalytic triad. Int J Biol Macromol 2015; 80:529-35. [DOI: 10.1016/j.ijbiomac.2015.07.031] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 07/08/2015] [Accepted: 07/10/2015] [Indexed: 12/22/2022]
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BEDAM binding free energy predictions for the SAMPL4 octa-acid host challenge. J Comput Aided Mol Des 2015; 29:315-25. [PMID: 25726024 DOI: 10.1007/s10822-014-9795-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Accepted: 09/05/2014] [Indexed: 12/14/2022]
Abstract
The binding energy distribution analysis method (BEDAM) protocol has been employed as part of the SAMPL4 blind challenge to predict the binding free energies of a set of octa-acid host-guest complexes. The resulting predictions were consistently judged as some of the most accurate predictions in this category of the SAMPL4 challenge in terms of quantitative accuracy and statistical correlation relative to the experimental values, which were not known at the time the predictions were made. The work has been conducted as part of a hands-on graduate class laboratory session. Collectively the students, aided by automated setup and analysis tools, performed the bulk of the calculations and the numerical and structural analysis. The success of the experiment confirms the reliability of the BEDAM methodology and it shows that physics-based atomistic binding free energy estimation models, when properly streamlined and automated, can be successfully employed by non-specialists.
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Perryman AL, Yu W, Wang X, Ekins S, Forli S, Li SG, Freundlich JS, Tonge PJ, Olson AJ. A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA. J Chem Inf Model 2015; 55:645-59. [PMID: 25636146 DOI: 10.1021/ci500672v] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.
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Affiliation(s)
- Alexander L Perryman
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | - Sean Ekins
- ⊥Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States.,#Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, United States
| | - Stefano Forli
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | | | | | | | - Arthur J Olson
- †Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
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A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment. Mol Immunol 2015; 65:189-204. [PMID: 25682054 PMCID: PMC7172456 DOI: 10.1016/j.molimm.2014.12.013] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2014] [Revised: 12/15/2014] [Accepted: 12/19/2014] [Indexed: 01/07/2023]
Abstract
Recent concerning facts of Chikungunya virus (CHIKV); a Togaviridae family alphavirus has proved this as a worldwide emerging threat which causes Chikungunya fever and devitalizing arthritis. Despite severe outbreaks and lack of antiviral drug, a mere progress has been made regarding to an epitope-based vaccine designed for CHIKV. In this study, we aimed to design an epitope-based vaccine that can trigger a significant immune response as well as to prognosticate inhibitor that can bind with potential drug target sites by using various immunoinformatics and docking simulation tools. Initially, whole proteome of CHIKV was retrieved from database and perused to identify the most immunogenic protein. Structural properties of the selected protein were analyzed. The capacity to induce both humoral and cell-mediated immunity by T cell and B cell were checked for the selected protein. The peptide region spanning 9 amino acids from 397 to 405 and the sequence YYYELYPTM were found as the most potential B cell and T cell epitopes respectively. This peptide could interact with as many as 19 HLAs and showed high population coverage ranging from 69.50% to 84.94%. By using in silico docking techniques the epitope was further assessed for binding against HLA molecules to verify the binding cleft interaction. In addition with this, the allergenicity of the epitopes was also evaluated. In the post therapeutic strategy, three dimensional structure was predicted along with validation and verification that resulted in molecular docking study to identify the potential drug binding sites and suitable therapeutic inhibitor against targeted protein. Finally, pharmacophore study was also performed in quest of seeing potent drug activity. However, this computational epitope-based peptide vaccine designing and target site prediction against CHIKV opens up a new horizon which may be the prospective way in Chikungunya virus research; the results require validation by in vitro and in vivo experiments.
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Forli S, Olson AJ. Computational challenges of structure-based approaches applied to HIV. Curr Top Microbiol Immunol 2015; 389:31-51. [PMID: 25711462 DOI: 10.1007/82_2015_432] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.
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Affiliation(s)
- Stefano Forli
- MGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA
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Al Olaby RR, Cocquerel L, Zemla A, Saas L, Dubuisson J, Vielmetter J, Marcotrigiano J, Khan AG, Catalan FV, Perryman AL, Freundlich JS, Forli S, Levy S, Balhorn R, Azzazy HM. Identification of a novel drug lead that inhibits HCV infection and cell-to-cell transmission by targeting the HCV E2 glycoprotein. PLoS One 2014; 9:e111333. [PMID: 25357246 PMCID: PMC4214736 DOI: 10.1371/journal.pone.0111333] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Accepted: 09/23/2014] [Indexed: 12/17/2022] Open
Abstract
Hepatitis C Virus (HCV) infects 200 million individuals worldwide. Although several FDA approved drugs targeting the HCV serine protease and polymerase have shown promising results, there is a need for better drugs that are effective in treating a broader range of HCV genotypes and subtypes without being used in combination with interferon and/or ribavirin. Recently, two crystal structures of the core of the HCV E2 protein (E2c) have been determined, providing structural information that can now be used to target the E2 protein and develop drugs that disrupt the early stages of HCV infection by blocking E2’s interaction with different host factors. Using the E2c structure as a template, we have created a structural model of the E2 protein core (residues 421–645) that contains the three amino acid segments that are not present in either structure. Computational docking of a diverse library of 1,715 small molecules to this model led to the identification of a set of 34 ligands predicted to bind near conserved amino acid residues involved in the HCV E2: CD81 interaction. Surface plasmon resonance detection was used to screen the ligand set for binding to recombinant E2 protein, and the best binders were subsequently tested to identify compounds that inhibit the infection of Huh-7 cells by HCV. One compound, 281816, blocked E2 binding to CD81 and inhibited HCV infection in a genotype-independent manner with IC50’s ranging from 2.2 µM to 4.6 µM. 281816 blocked the early and late steps of cell-free HCV entry and also abrogated the cell-to-cell transmission of HCV. Collectively the results obtained with this new structural model of E2c suggest the development of small molecule inhibitors such as 281816 that target E2 and disrupt its interaction with CD81 may provide a new paradigm for HCV treatment.
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Affiliation(s)
- Reem R. Al Olaby
- Department of Chemistry, The American University in Cairo, New Cairo, Egypt
| | - Laurence Cocquerel
- Center for Infection and Immunity of Lille, CNRS-UMR8204/Inserm-U1019, Pasteur Institute of Lille, University of Lille North of France, Lille, France
| | - Adam Zemla
- Pathogen Bioinformatics, Lawrence Livermore National Laboratory, Livermore, CA, United States of America
| | - Laure Saas
- Center for Infection and Immunity of Lille, CNRS-UMR8204/Inserm-U1019, Pasteur Institute of Lille, University of Lille North of France, Lille, France
| | - Jean Dubuisson
- Center for Infection and Immunity of Lille, CNRS-UMR8204/Inserm-U1019, Pasteur Institute of Lille, University of Lille North of France, Lille, France
| | - Jost Vielmetter
- Protein Expression Center, Beckman Institute, California Institute of Technology, Pasadena, CA, United States of America
| | - Joseph Marcotrigiano
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, United States of America
| | - Abdul Ghafoor Khan
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ, United States of America
| | - Felipe Vences Catalan
- Department of Medicine, Stanford University Medical Center, Stanford, CA, United States of America
| | - Alexander L. Perryman
- Department of Medicine, Division of Infectious Diseases, Center for Emerging & Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, NJ, United States of America
| | - Joel S. Freundlich
- Department of Medicine, Division of Infectious Diseases, Center for Emerging & Re-emerging Pathogens, Rutgers University-New Jersey Medical School, Newark, NJ, United States of America
- Department of Pharmacology and Physiology, Rutgers University-New Jersey Medical School, Newark, NJ, United States of America
| | - Stefano Forli
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, United States of America
| | - Shoshana Levy
- Department of Medicine, Stanford University Medical Center, Stanford, CA, United States of America
| | - Rod Balhorn
- Department of Applied Science, University of California Davis, Davis, CA, United States of America
- * E-mail:
| | - Hassan M. Azzazy
- Department of Chemistry, The American University in Cairo, New Cairo, Egypt
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Deng N, Forli S, He P, Perryman A, Wickstrom L, Vijayan RSK, Tiefenbrunn T, Stout D, Gallicchio E, Olson AJ, Levy RM. Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease. J Phys Chem B 2014; 119:976-88. [PMID: 25189630 PMCID: PMC4306491 DOI: 10.1021/jp506376z] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
![]()
Molecular docking is a powerful tool
used in drug discovery and
structural biology for predicting the structures of ligand–receptor
complexes. However, the accuracy of docking calculations can be limited
by factors such as the neglect of protein reorganization in the scoring
function; as a result, ligand screening can produce a high rate of
false positive hits. Although absolute binding free energy methods
still have difficulty in accurately rank-ordering binders, we believe
that they can be fruitfully employed to distinguish binders from nonbinders
and reduce the false positive rate. Here we study a set of ligands
that dock favorably to a newly discovered, potentially allosteric
site on the flap of HIV-1 protease. Fragment binding to this site
stabilizes a closed form of protease, which could be exploited for
the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand
complexes from AutoDock were subject to the free energy screening
using two methods, the recently developed binding energy analysis
method (BEDAM) and the standard double decoupling method (DDM). Free
energy calculations correctly identified most of the false positives
(≥83%) and recovered all the confirmed binders. The results
show a gap averaging ≥3.7 kcal/mol, separating the binders
and the false positives. We present a formula that decomposes the
binding free energy into contributions from the receptor conformational
macrostates, which provides insights into the roles of different binding
modes. Our binding free energy component analysis further suggests
that improving the treatment for the desolvation penalty associated
with the unfulfilled polar groups could reduce the rate of false positive
hits in docking. The current study demonstrates that the combination
of docking with free energy methods can be very useful for more accurate
ligand screening against valuable drug targets.
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Affiliation(s)
- Nanjie Deng
- Center for Biophysics & Computational Biology/ICMS, ‡Department of Chemistry, Temple University , Philadelphia, Pennsylvania19122, United States
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Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge. J Comput Aided Mol Des 2014; 28:475-90. [PMID: 24504704 DOI: 10.1007/s10822-014-9711-9] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2013] [Accepted: 01/16/2014] [Indexed: 10/25/2022]
Abstract
As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.
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Voet ARD, Kumar A, Berenger F, Zhang KYJ. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des 2014; 28:363-73. [PMID: 24446075 DOI: 10.1007/s10822-013-9702-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2013] [Accepted: 12/17/2013] [Indexed: 12/14/2022]
Abstract
The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.
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Affiliation(s)
- Arnout R D Voet
- Zhang Initiative Research Unit, Institute Laboratories, RIKEN, 2-1 Hirosawa, Wakō, Saitama, 351-0198, Japan
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