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Jiang H, Wang J, Cong W, Huang Y, Ramezani M, Sarma A, Dokholyan NV, Mahdavi M, Kandemir MT. Predicting Protein-Ligand Docking Structure with Graph Neural Network. J Chem Inf Model 2022; 62:2923-2932. [PMID: 35699430 PMCID: PMC10279412 DOI: 10.1021/acs.jcim.2c00127] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.
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Affiliation(s)
- Huaipan Jiang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Jian Wang
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Weilin Cong
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Yihe Huang
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Morteza Ramezani
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Anup Sarma
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Nikolay V Dokholyan
- Departments of Pharmacology and Biochemistry and Molecular Biology, Pennsylvania State College of Medicine, Hershey, Pennsylvania 17033, United States
- Departments of Chemistry and Biomedical Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mehrdad Mahdavi
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
| | - Mahmut T Kandemir
- Department of Computer Science and Engineering, Pennsylvania State University, State College, Pennsylvania 16802, United States
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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Fourches D, Ash J. 4D- quantitative structure-activity relationship modeling: making a comeback. Expert Opin Drug Discov 2019; 14:1227-1235. [PMID: 31513441 DOI: 10.1080/17460441.2019.1664467] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Jeremy Ash
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
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Plundrich NJ, Cook BT, Maleki SJ, Fourches D, Lila MA. Binding of peanut allergen Ara h 2 with Vaccinium fruit polyphenols. Food Chem 2019; 284:287-295. [PMID: 30744860 DOI: 10.1016/j.foodchem.2019.01.081] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 01/08/2019] [Accepted: 01/08/2019] [Indexed: 01/30/2023]
Abstract
The potential for 42 different polyphenols found in Vaccinium fruits to bind to peanut allergen Ara h 2 and inhibit IgE binding epitopes was investigated using cheminformatics techniques. Out of 12 predicted binders, delphinidin-3-glucoside, cyanidin-3-glucoside, procyanidin C1, and chlorogenic acid were further evaluated in vitro. Circular dichroism, UV-Vis spectroscopy, and immunoblotting determined their capacity to (i) bind to Ara h 2, (ii) induce protein secondary structural changes, and (iii) inhibit IgE binding epitopes. UV-Vis spectroscopy clearly indicated that procyanidin C1 and chlorogenic acid interacted with Ara h 2, and circular dichroism results suggested that interactions with these polyphenols resulted in changes to Ara h 2 secondary structures. Immunoblotting showed that procyanidin C1 and chlorogenic acid bound to Ara h 2 significantly decreased the IgE binding capacity by 37% and 50%, respectively. These results suggest that certain polyphenols can inhibit IgE recognition of Ara h 2 by obstructing linear IgE epitopes.
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Affiliation(s)
- Nathalie J Plundrich
- Plants for Human Health Institute, Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, North Carolina Research Campus, Kannapolis, NC 28081, USA
| | - Bethany T Cook
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Soheila J Maleki
- United States Department of Agriculture-Agricultural Research Service-Southern Regional Research Center, New Orleans, LA 70124, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA
| | - Mary Ann Lila
- Plants for Human Health Institute, Department of Food, Bioprocessing and Nutrition Sciences, North Carolina State University, North Carolina Research Campus, Kannapolis, NC 28081, USA.
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Ivanov SM, Huber RG, Alibay I, Warwicker J, Bond PJ. Energetic Fingerprinting of Ligand Binding to Paralogous Proteins: The Case of the Apoptotic Pathway. J Chem Inf Model 2018; 59:245-261. [DOI: 10.1021/acs.jcim.8b00765] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Stefan M. Ivanov
- Manchester Institute of Biotechnology, School of Chemistry, The University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Matrix 07-01, 30 Biopolis Street, Singapore 138671, Singapore
| | - Roland G. Huber
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Matrix 07-01, 30 Biopolis Street, Singapore 138671, Singapore
| | - Irfan Alibay
- Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester, Oxford Road, Manchester M13 9PT, U.K
| | - Jim Warwicker
- Manchester Institute of Biotechnology, School of Chemistry, The University of Manchester, 131 Princess Street, Manchester M1 7DN, U.K
| | - Peter J. Bond
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Matrix 07-01, 30 Biopolis Street, Singapore 138671, Singapore
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore
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Siebert DCB, Wieder M, Schlener L, Scholze P, Boresch S, Langer T, Schnürch M, Mihovilovic MD, Richter L, Ernst M, Ecker GF. SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2- Benzodiazepine Site. J Chem Inf Model 2018; 58:1682-1696. [PMID: 30028134 DOI: 10.1021/acs.jcim.8b00199] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The structural resolution of a bound ligand-receptor complex is a key asset to efficiently drive lead optimization in drug design. However, structural resolution of many drug targets still remains a challenging endeavor. In the absence of structural knowledge, scientists resort to structure-activity relationships (SARs) to promote compound development. In this study, we incorporated ligand-based knowledge to formulate a docking scoring function that evaluates binding poses for their agreement with a known SAR. We showcased this protocol by identifying the binding mode of the pyrazoloquinolinone (PQ) CGS-8216 at the benzodiazepine binding site of the GABAA receptor. Further evaluation of the final pose by molecular dynamics and free energy simulations revealed a close proximity between the pendent phenyl ring of the PQ and γ2D56, congruent with the low potency of carboxyphenyl analogues. Ultimately, we introduced the γ2D56A mutation and in fact observed a 10-fold potency increase in the carboxyphenyl analogue, providing experimental evidence in favor of our binding hypothesis.
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Affiliation(s)
- David C B Siebert
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Marcus Wieder
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria.,Faculty of Chemistry, Department of Computational Biological Chemistry , University of Vienna , Währingerstrasse 17 , 1090 Vienna , Austria
| | - Lydia Schlener
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Petra Scholze
- Department of Pathobiology of the Nervous System, Center for Brain Research , Medical University of Vienna , Spitalgasse 4 , 1090 Vienna , Austria
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry , University of Vienna , Währingerstrasse 17 , 1090 Vienna , Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Michael Schnürch
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Marko D Mihovilovic
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Lars Richter
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Margot Ernst
- Department of Molecular Neurosciences, Center for Brain Research , Medical University of Vienna , Spitalgasse 4 , 1090 Vienna , Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
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Acevedo CH, Scotti L, Scotti MT. In Silico Studies Designed to Select Sesquiterpene Lactones with Potential Antichagasic Activity from an In-House Asteraceae Database. ChemMedChem 2018; 13:634-645. [PMID: 29323468 DOI: 10.1002/cmdc.201700743] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 12/29/2017] [Indexed: 01/04/2023]
Abstract
Chagas disease is an endemic disease caused by Trypanosoma cruzi, which affects more than eight million people, mostly in the Americas. A search for new treatments is necessary to control and eliminate this disease. Sesquiterpene lactones (SLs) are an interesting group of secondary metabolites characteristic of the Asteraceae family that have presented a wide range of biological activities. From the ChEMBL database, we selected a diverse set of 4452, 1635, and 1322 structures with tested activity against the three T. cruzi parasitic forms: amastigote, trypomastigote, and epimastigote, respectively, to create random forest (RF) models with an accuracy of greater than 74 % for cross-validation and test sets. Afterward, a ligand-based virtual screen of the entire SLs of the Asteraceae database stored in SistematX (1306 structures) was performed. In addition, a structure-based virtual screen was also performed for the same set of SLs using molecular docking. Finally, using an approach combining ligand-based and structure-based virtual screening along with the equations proposed in this study to normalize the probability scores, we verified potentially active compounds and established a possible mechanism of action.
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Affiliation(s)
- Chonny Herrera Acevedo
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Cidade Universitária - Castelo Branco III, João Pessoa, PB, Brazil
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Cidade Universitária - Castelo Branco III, João Pessoa, PB, Brazil
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, Cidade Universitária - Castelo Branco III, João Pessoa, PB, Brazil
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8
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Van Den Driessche G, Fourches D. Adverse drug reactions triggered by the common HLA-B*57:01 variant: virtual screening of DrugBank using 3D molecular docking. J Cheminform 2018; 10:3. [PMID: 29383457 PMCID: PMC5790764 DOI: 10.1186/s13321-018-0257-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Accepted: 01/17/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Idiosyncratic adverse drug reactions have been linked to a drug's ability to bind with a human leukocyte antigen (HLA) protein. However, due to the thousands of HLA variants and limited structural data for drug-HLA complexes, predicting a specific drug-HLA combination represents a significant challenge. Recently, we investigated the binding mode of abacavir with the HLA-B*57:01 variant using molecular docking. Herein, we developed a new ensemble screening workflow involving three X-ray crystal derived docking procedures to screen the DrugBank database and identify potentially HLA-B*57:01 liable drugs. Then, we compared our workflow's performance with another model recently developed by Metushi et al., which proposed seven in silico HLA-B*57:01 actives, but were later found to be experimentally inactive. METHODS After curation, there were over 6000 approved and experimental drugs remaining in DrugBank for docking using Schrodinger's GLIDE SP and XP scoring functions. Docking was performed with our new consensus-like ensemble workflow, relying on three different X-ray crystals (3VRI, 3VRJ, and 3UPR) in presence and absence of co-binding peptides. The binding modes of HLA-B*57:01 hit compounds for all three peptides were further explored using 3D interaction fingerprints and hierarchical clustering. RESULTS The screening resulted in 22 hit compounds forecasted to bind HLA-B*57:01 in all docking conditions (SP and XP with and without peptides P1, P2, and P3). These 22 compounds afforded 2D-Tanimoto similarities being less than 0.6 when compared to the structure of native abacavir, whereas their 3D binding mode similarities varied in a broader range (0.2-0.8). Hierarchical clustering using a Ward Linkage revealed different clustering patterns for each co-binding peptide. When we docked Metushi et al.'s seven proposed hits using our workflow, our screening platform identified six out of seven as being inactive. Molecular dynamic simulations were used to explore the stability of abacavir and acyclovir in complex with peptide P3. CONCLUSIONS This study reports on the extensive docking of the DrugBank database and the 22 HLA-B*57:01 liable candidates we identified. Importantly, comparisons between this study and the one by Metushi et al. highlighted new critical and complementary knowledge for the development of future HLA-specific in silico models.
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Affiliation(s)
- George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
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9
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Kadukova M, Grudinin S. Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2. J Comput Aided Mol Des 2017; 32:151-162. [DOI: 10.1007/s10822-017-0062-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 09/08/2017] [Indexed: 10/18/2022]
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11
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Ash J, Fourches D. Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories. J Chem Inf Model 2017; 57:1286-1299. [DOI: 10.1021/acs.jcim.7b00048] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Jeremy Ash
- Department of Chemistry,
Bioinformatics Research Center, North Carolina State University, 322 Ricks Hall, 1 Lampe Drive, Raleigh, North Carolina 27695, United States
| | - Denis Fourches
- Department of Chemistry,
Bioinformatics Research Center, North Carolina State University, 322 Ricks Hall, 1 Lampe Drive, Raleigh, North Carolina 27695, United States
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12
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Van Den Driessche G, Fourches D. Adverse drug reactions triggered by the common HLA-B*57:01 variant: a molecular docking study. J Cheminform 2017; 9:13. [PMID: 28303164 PMCID: PMC5337232 DOI: 10.1186/s13321-017-0202-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 02/24/2017] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Human leukocyte antigen (HLA) surface proteins are directly involved in idiosyncratic adverse drug reactions. Herein, we present a structure-based analysis of the common HLA-B*57:01 variant known to be responsible for several HLA-linked adverse effects such as the abacavir hypersensitivity syndrome. METHODS First, we analyzed three X-ray crystal structures involving the HLA-B*57:01 protein variant, the anti-HIV drug abacavir, and different co-binding peptides present in the antigen-binding cleft. We superimposed the three complexes and showed that abacavir had no significant conformational variation whatever the co-binding peptide. Second, we self-docked abacavir in the HLA-B*57:01 antigen binding cleft with and without peptide using Glide. Third, we docked a small test set of 13 drugs with known ADRs and suspected HLA associations. RESULTS In the presence of an endogenous co-binding peptide, we found a significant stabilization (~2 kcal/mol) of the docking scores and identified several modified abacavir-peptide interactions indicating that the peptide does play a role in stabilizing the HLA-abacavir complex. Next, our model was used to dock a test set of 13 drugs at HLA-B*57:01 and measured their predicted binding affinities. Drug-specific interactions were observed at the antigen-binding cleft and we were able to discriminate the compounds with known HLA-B*57:01 liability from inactives. CONCLUSIONS Overall, our study highlights the relevance of molecular docking for evaluating and analyzing complex HLA-drug interactions. This is particularly important for virtual drug screening over thousands of HLA variants as other experimental techniques (e.g., in vitro HTS) and computational approaches (e.g., molecular dynamics) are more time consuming and expensive to conduct. As the attention for drugs' HLA liability is on the rise, we believe this work participates in encouraging the use of molecular modeling for reliably studying and predicting HLA-drug interactions. Graphical abstract.
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Affiliation(s)
- George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC USA
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13
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Fourches D, Muratov E, Tropsha A. Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J Chem Inf Model 2016; 56:1243-52. [PMID: 27280890 PMCID: PMC5657146 DOI: 10.1021/acs.jcim.6b00129] [Citation(s) in RCA: 181] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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14
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Use of bacteriophage to target bacterial surface structures required for virulence: a systematic search for antibiotic alternatives. Curr Genet 2016; 62:753-757. [PMID: 27113766 DOI: 10.1007/s00294-016-0603-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 04/11/2016] [Indexed: 01/21/2023]
Abstract
Bacteriophages (phage) that infect pathogenic bacteria often attach to surface receptors that are coincidentally required for virulence. Receptor loss or modification through mutation renders mutants both attenuated and phage resistant. Such attenuated mutants frequently have no apparent laboratory growth defects, but in the host, they fail to exhibit properties needed to produce disease such as mucosal colonization or survival within professional phagocytic cells. The connection between attenuation and phage resistance has been exploited in experimental demonstrations of phage therapy. In such experiments, phage resistant mutants that arise naturally during therapy are inconsequential because of their attenuated status. A more contemporary approach to exploiting this connection involves identifying small effector molecules, identified in high-throughput screens, that inhibit one or more of the steps needed to produce a functioning phage receptor. Since such biosynthetic steps are unique to bacteria, inhibitors can be utilized therapeutically, in lieu of antibiotics. Also, since the inhibitor is specific to a particular bacterium or group of bacteria, no off-target resistance is generated in the host's commensal bacterial population. This brief review covers examples of how mutations that confer phage resistance produce attenuation, and how this coincidental relationship can be exploited in the search for the next generation of therapeutic agents for bacterial diseases.
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15
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Politi R, Convertino M, Popov K, Dokholyan NV, Tropsha A. Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise. J Chem Inf Model 2016; 56:1032-41. [DOI: 10.1021/acs.jcim.5b00751] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Regina Politi
- Laboratory for Molecular Modeling,
Division of Chemical Biology and
Medicinal Chemistry, UNC Eshelman School of Pharmacy,
and ‡Department of Biochemistry
and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Marino Convertino
- Laboratory for Molecular Modeling,
Division of Chemical Biology and
Medicinal Chemistry, UNC Eshelman School of Pharmacy,
and ‡Department of Biochemistry
and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Konstantin Popov
- Laboratory for Molecular Modeling,
Division of Chemical Biology and
Medicinal Chemistry, UNC Eshelman School of Pharmacy,
and ‡Department of Biochemistry
and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Nikolay V. Dokholyan
- Laboratory for Molecular Modeling,
Division of Chemical Biology and
Medicinal Chemistry, UNC Eshelman School of Pharmacy,
and ‡Department of Biochemistry
and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling,
Division of Chemical Biology and
Medicinal Chemistry, UNC Eshelman School of Pharmacy,
and ‡Department of Biochemistry
and Biophysics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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16
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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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Huang SY, Li M, Wang J, Pan Y. HybridDock: A Hybrid Protein-Ligand Docking Protocol Integrating Protein- and Ligand-Based Approaches. J Chem Inf Model 2015; 56:1078-87. [PMID: 26317502 DOI: 10.1021/acs.jcim.5b00275] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Structure-based molecular docking and ligand-based similarity search are two commonly used computational methods in computer-aided drug design. Structure-based docking tries to utilize the structural information on a drug target like protein, and ligand-based screening takes advantage of the information on known ligands for a target. Given their different advantages, it would be desirable to use both protein- and ligand-based approaches in drug discovery when information for both the protein and known ligands is available. Here, we have presented a general hybrid docking protocol, referred to as HybridDock, to utilize both the protein structures and known ligands by combining the molecular docking program MDock and the ligand-based similarity search method SHAFTS, and evaluated our hybrid docking protocol on the CSAR 2013 and 2014 exercises. The results showed that overall our hybrid docking protocol significantly improved the performance in both binding affinity and binding mode predictions, compared to the sole MDock program. The efficacy of the hybrid docking protocol was further confirmed using the combination of DOCK and SHAFTS, suggesting an alternative docking approach for modern drug design/discovery.
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Affiliation(s)
- Sheng-You Huang
- Research Support Computing, University of Missouri Bioinformatics Consortium, and Department of Computer Science, University of Missouri , Columbia, Missouri 65211, United States
| | - Min Li
- School of Information Science and Engineering, Central South University , Changsha, Hunan 410083, China
| | - Jianxin Wang
- School of Information Science and Engineering, Central South University , Changsha, Hunan 410083, China
| | - Yi Pan
- School of Information Science and Engineering, Central South University , Changsha, Hunan 410083, China.,Department of Computer Science, Georgia State University , Atlanta, Georgia 30302, United States
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Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015; 28:581-604. [PMID: 25808539 DOI: 10.1002/jmr.2471] [Citation(s) in RCA: 159] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Revised: 01/16/2015] [Accepted: 02/05/2015] [Indexed: 12/11/2022]
Abstract
Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.
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Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
| | - Jessica Holien
- ACRF Rational Drug Discovery Centre and Structural Biology Laboratory, St. Vincent's Institute of Medical Research, Fitzroy, Victoria, 3065, Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute, Melbourne, Victoria, 3004, Australia.,Department of Surgery Austin Health, University of Melbourne, Melbourne, Victoria, 3084, Australia.,Department of Immunology, Monash University, Alfred Medical Research and Education Precinct, Melbourne, Victoria, 3004, Australia.,School of Biomedical Sciences, CHIRI Biosciences, Curtin University, Perth, Western Australia, 6845, Australia
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19
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Fourches D, Politi R, Tropsha A. Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark. J Chem Inf Model 2014; 55:63-71. [DOI: 10.1021/ci500519w] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Denis Fourches
- Laboratory for Molecular
Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Regina Politi
- Laboratory for Molecular
Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular
Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC
Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
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