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Jabeen A, Vijayram R, Ranganathan S. A two-stage computational approach to predict novel ligands for a chemosensory receptor. Curr Res Struct Biol 2021; 2:213-221. [PMID: 34235481 PMCID: PMC8244491 DOI: 10.1016/j.crstbi.2020.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 09/29/2020] [Accepted: 10/03/2020] [Indexed: 11/01/2022] Open
Abstract
Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the apo and holo forms. The refined model in the apo form was selected for SBVS. Four novel small molecules were identified as strong binders to this olfactory receptor on the basis of computed binding energies.
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Key Words
- APF, Atomic property field
- Amber, Assisted model Building with Energy Refinement
- Atomic property field
- Binding free energy calculation
- CSF, Cerebrospinal fluid
- ECL, Extracellular loop
- GPCR, G protein coupled receptor
- HCMV, Human cytomegalovirus
- HMDB, Human metabolome database
- Hydrophobicity correspondence
- LBVS, Ligand based virtual screening
- LC, Lung carcinoids
- MD, Molecular dynamics
- MMGBSA, Molecular mechanics generalized born surface area
- MMPBSA, Molecular mechanics Poisson–Boltzmann surface area
- Molecular dynamics
- NAFLD, Non-alcoholic fatty liver disease
- NASH, Nonalcoholic steatohepatitis
- OR, olfactory receptor
- OR1A2
- Olfactory receptor
- PMEMD, Particle-Mesh Ewald Molecular Dynamics
- POPC, 1-palmitoyl-2-oleoyl-sn-glycero- 3-phosphatidylcholine
- RMSD, Root mean square deviation
- RMSF, Root mean square fluctuation
- SBVS, Structure based virtual screening
- SSD, Sum of squared difference
- TM, Transmembrane
- Virtual ligand screening
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Affiliation(s)
- Amara Jabeen
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Ramya Vijayram
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamilnadu, India
| | - Shoba Ranganathan
- Department of Molecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 33:1057-1069. [DOI: 10.1007/s10822-019-00225-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Accepted: 09/17/2019] [Indexed: 01/07/2023]
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3
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Shukla A, Tyagi R, Meena S, Datta D, Srivastava SK, Khan F. 2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line. J Biomol Struct Dyn 2019; 38:168-185. [PMID: 30686140 DOI: 10.1080/07391102.2019.1570868] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure-activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r2), LOO-based internal regression (q2) and external test set regression (pred_r2) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aparna Shukla
- Metabolic and Structural Biology Department, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, Uttar Pradesh, India
| | - Rekha Tyagi
- Medicinal Chemistry Division, Central Institute of Medicinal and Aromatic Plants, Lucknow, Uttar Pradesh, India
| | - Sanjeev Meena
- Biochemistry Division, CSIR-Central Drug Research Institute (CDRI), Lucknow, Uttar Pradesh, India
| | - Dipak Datta
- Biochemistry Division, CSIR-Central Drug Research Institute (CDRI), Lucknow, Uttar Pradesh, India
| | - Santosh Kumar Srivastava
- Medicinal Chemistry Division, Central Institute of Medicinal and Aromatic Plants, Lucknow, Uttar Pradesh, India
| | - Feroz Khan
- Metabolic and Structural Biology Department, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, Uttar Pradesh, India
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Lam PCH, Abagyan R, Totrov M. Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3. J Comput Aided Mol Des 2018; 33:35-46. [PMID: 30094533 DOI: 10.1007/s10822-018-0139-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/23/2018] [Indexed: 12/11/2022]
Abstract
In context of D3R Grand Challenge 3 we have investigated several ligand activity prediction protocols that combined elements of a physics-based energy function (ICM VLS score) and the knowledge-based Atomic Property Field 3D QSAR approach. Activity prediction models utilized poses produced by ICM-Dock with ligand bias and 4D receptor conformational ensembles (LigBEnD). Hybrid APF/P (APF/Physics) models were superior to pure physics- or knowledge-based models in our preliminary tests using rigorous three-fold clustered cross-validation and later proved successful in the blind prediction for D3R GC3 sets, consistently performing well across four different targets. The results demonstrate that knowledge-based and physics-based inputs into the machine-learning activity model can be non-redundant and synergistic.
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Affiliation(s)
- Polo C-H Lam
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, CA, 92121, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Maxim Totrov
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, CA, 92121, USA.
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Basith S, Cui M, Macalino SJY, Park J, Clavio NAB, Kang S, Choi S. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design. Front Pharmacol 2018; 9:128. [PMID: 29593527 PMCID: PMC5854945 DOI: 10.3389/fphar.2018.00128] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 02/06/2018] [Indexed: 01/14/2023] Open
Abstract
The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.
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Affiliation(s)
| | | | | | | | | | - Soosung Kang
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sun Choi
- College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
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Lam PCH, Abagyan R, Totrov M. Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach. J Comput Aided Mol Des 2017; 32:187-198. [PMID: 28887659 PMCID: PMC5767200 DOI: 10.1007/s10822-017-0058-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 08/30/2017] [Indexed: 11/29/2022]
Abstract
Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.
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Affiliation(s)
- Polo C-H Lam
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, CA, 92121, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA
| | - Maxim Totrov
- Molsoft L.L.C., 11199 Sorrento Valley Road, S209, San Diego, CA, 92121, USA.
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Chen YC, Totrov M, Abagyan R. Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping. Future Med Chem 2014; 6:1741-55. [PMID: 25407367 PMCID: PMC4285145 DOI: 10.4155/fmc.14.113] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock). RESULTS The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity. CONCLUSION Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.
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Affiliation(s)
- Yu-Chen Chen
- Bioinformatics, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
| | - Max Totrov
- Molsoft LLC, 11199 Sorrento Valley Road, S209, San Diego, CA 92121, USA
| | - Ruben Abagyan
- Skaggs School of Pharmacy & Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, MC 0747, La Jolla, CA 92093-0747, USA
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Hartsel JA, Wong DM, Mutunga JM, Ma M, Anderson TD, Wysinski A, Islam R, Wong EA, Paulson SL, Li J, Lam PCH, Totrov MM, Bloomquist JR, Carlier PR. Re-engineering aryl methylcarbamates to confer high selectivity for inhibition of Anopheles gambiae versus human acetylcholinesterase. Bioorg Med Chem Lett 2012; 22:4593-8. [PMID: 22738634 PMCID: PMC3389130 DOI: 10.1016/j.bmcl.2012.05.103] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2012] [Revised: 05/18/2012] [Accepted: 05/29/2012] [Indexed: 10/28/2022]
Abstract
To identify potential human-safe insecticides against the malaria mosquito we undertook an investigation of the structure-activity relationship of aryl methylcarbamates inhibitors of acetylcholinesterase (AChE). Compounds bearing a β-branched 2-alkoxy or 2-thioalkyl group were found to possess good selectivity for inhibition of Anopheles gambiae AChE over human AChE; up to 530-fold selectivity was achieved with carbamate 11d. A 3D QSAR model is presented that is reasonably consistent with log inhibition selectivity of 34 carbamates. Toxicity of these compounds to live Anopheles gambiae was demonstrated using both tarsal contact (filter paper) and topical application protocols.
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Affiliation(s)
- Joshua A Hartsel
- Department of Chemistry, Virginia Tech, Blacksburg, VA 24061, USA
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Abstract
Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to "de-orphanize" drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration-approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest.
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Saranya N, Selvaraj S. An alphabetic code based atomic level molecular similarity search in databases. Bioinformation 2012; 8:498-503. [PMID: 22829718 PMCID: PMC3398777 DOI: 10.6026/97320630008498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2012] [Accepted: 05/27/2012] [Indexed: 11/23/2022] Open
Abstract
Atomic level molecular similarity and diversity studies have gained considerable importance through their wide application in Bioinformatics and Chemo-informatics for drug design. The availability of large volumes of data on chemical compounds requires new methodologies for efficient and effective searching of its archives in less time with optimal computational power. We describe an alphabetic algorithm for similarity searching based on atom-atom bonding preference for ligands. We represented 170 cyclindependent kinase 2 inhibitors using strings of pre-defined alphabets for searching using known protein sequence alignment tools. Thus, a common pattern was extracted using this set of compounds for database searching to retrieve similar active compounds. Area under the receiver operating characteristic (ROC) curve was used for the discrimination of similar and dissimilar compounds in the databases. An average retrieval rate of about 60% is obtained in cross-validation using the home-grown dataset and the directory of useful decoys (DUD, formally known as the ZINC database) data. This will help in the effective retrieval of similar compounds using database search.
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Affiliation(s)
- Nallusamy Saranya
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirapalli – 620024, Tamilnadu, India
| | - Samuel Selvaraj
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirapalli – 620024, Tamilnadu, India
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Totrov M. Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites. BMC Bioinformatics 2011; 12 Suppl 1:S35. [PMID: 21342566 PMCID: PMC3044291 DOI: 10.1186/1471-2105-12-s1-s35] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB.
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Affiliation(s)
- Maxim Totrov
- Molsoft LLC,3366 N Torrey Pines Ct, La Jolla, CA 92037, USA.
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