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Bathula R, Lanka G, Muddagoni N, Dasari M, Nakkala S, Bhargavi M, Somadi G, Sivan SK, Rajender Potlapally S. Identification of potential Aurora kinase-C protein inhibitors: an amalgamation of energy minimization, virtual screening, prime MMGBSA and AutoDock. J Biomol Struct Dyn 2019; 38:2314-2325. [DOI: 10.1080/07391102.2019.1630318] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Revanth Bathula
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Goverdhan Lanka
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Narasimha Muddagoni
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Mahendar Dasari
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Sravanthi Nakkala
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Manan Bhargavi
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Gururaj Somadi
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Sree Kanth Sivan
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
| | - Sarita Rajender Potlapally
- Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India
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Gueto-Tettay C, Martinez-Consuegra A, Pelaez-Bedoya L, Drosos-Ramirez JC. G-score: A function to solve the puzzle of modeling the protonation states of β-secretase binding pocket. J Mol Graph Model 2018; 85:1-12. [PMID: 30053756 DOI: 10.1016/j.jmgm.2018.07.008] [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: 05/17/2018] [Revised: 07/13/2018] [Accepted: 07/16/2018] [Indexed: 10/28/2022]
Abstract
The population density concept has emerged as a proposal for the analysis of molecular dynamics results, the key characteristic of population density is the evaluation of the simultaneous occurrence of a set of relevant parameters for a system. However, despite its statistical strength, selection of the tolerance level for the comparison of different models may appear as arbitrary. This work introduces the G-score, a function which summarizes and categorizes the results of population density analysis. Additionally, it incorporates parameters based on rmsd and dihedral angles, besides the protein-protein and protein-ligand interatomic distances conventionally used, which complement each other to provide a better description of the behavior of the system. These newly-proposed tools were applied to determine the most probable protonation state of the aspartic dyad of BACE1, Asp93 and Asp289, in the presence of three types of transition state inhibitors namely: reduced amides, tertiary carbinamines and hydroxyethylamines. The results show a full agreement between G-score values and population density charts, with the advantage of allowing a quick and direct comparison among all the considered models. We anticipate that the simplicity of calculating the parameters employed in this study will permit the extensive use of population density and the G-score for other molecular systems.
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Affiliation(s)
- Carlos Gueto-Tettay
- Grupo de Química Bioorgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Cartagena, Campus San Pablo, 130015, Colombia; Lund University, Faculty of Medicine, Department of Clinical Sciences Lund, Division of Infection Medicine, Lund, Sweden.
| | - Alejandro Martinez-Consuegra
- Grupo de Química Bioorgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Cartagena, Campus San Pablo, 130015, Colombia
| | - Luis Pelaez-Bedoya
- Grupo de Química Bioorgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Cartagena, Campus San Pablo, 130015, Colombia
| | - Juan Carlos Drosos-Ramirez
- Grupo de Química Bioorgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Cartagena, Campus San Pablo, 130015, Colombia.
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Kjellgren ER, Glue OES, Reinholdt P, Meyer JE, Kongsted J, Poongavanam V. A comparative study of binding affinities for 6,7-dimethoxy-4-pyrrolidylquinazolines as phosphodiesterase 10A inhibitors using the linear interaction energy method. J Mol Graph Model 2015; 61:44-52. [PMID: 26188794 DOI: 10.1016/j.jmgm.2015.06.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 06/05/2015] [Accepted: 06/20/2015] [Indexed: 01/29/2023]
Abstract
The linear interaction energy (LIE) method was used to estimate the free energies of binding for a set of 27 pyrrolidylquinazoline derivatives as phosphodiesterase 10A inhibitors. Twenty-six X-ray crystal structures of phosphodiesterase 10A and two sampling methods, minimization and Hybrid Monte Carlo, were used to assess the affinity models based on the linear interaction energies. The best model was obtained based on the parameters α=0.16 and β=0.04, which represent non-polar and polar interactions, respectively, with a root mean square error (RMSE) of 0.42kcal/mol (R(2)=0.71) and 0.52kcal/mol (R(2)=0.86) for the training and test sets, respectively. In addition, the applicability domain of the model was investigated. After validation of the models, the best model was subsequently used in a virtual screening process, which resulted in a set of optimized compounds. The models developed in this study could be useful as filter for virtual screening and lead optimization processes for phosphodiesterase 10A drug developments.
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Affiliation(s)
- Erik Rosendahl Kjellgren
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
| | - Oliver Emil Skytte Glue
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
| | - Peter Reinholdt
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
| | - Julie Egeskov Meyer
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
| | - Jacob Kongsted
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Odense M, Denmark
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Ghemtio L, Muzet N. Retrospective molecular docking study of WY-25105 ligand to β-secretase and bias of the three-dimensional structure flexibility. J Mol Model 2013; 19:2971-9. [DOI: 10.1007/s00894-013-1821-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2012] [Accepted: 03/10/2013] [Indexed: 01/04/2023]
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Liu S, Fu R, Cheng X, Chen SP, Zhou LH. Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE). BMC STRUCTURAL BIOLOGY 2012; 12:21. [PMID: 22925713 PMCID: PMC3533579 DOI: 10.1186/1472-6807-12-21] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2011] [Accepted: 08/03/2012] [Indexed: 01/14/2023]
Abstract
BACKGROUND The inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor. RESULTS By considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41. CONCLUSIONS These QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.
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Affiliation(s)
- Shu Liu
- Guangdong Province Key Laboratory of Functional Molecules in Oceanic Microorganism, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
- Department of Anatomy, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
| | - Rao Fu
- Department of Anatomy, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
| | - Xiao Cheng
- Department of Anatomy, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
| | - Sheng-Ping Chen
- Guangdong Province Key Laboratory of Functional Molecules in Oceanic Microorganism, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
| | - Li-Hua Zhou
- Department of Anatomy, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, People’s Republic of China
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Kortagere S, Lill M, Kerrigan J. Role of computational methods in pharmaceutical sciences. Methods Mol Biol 2012; 929:21-48. [PMID: 23007425 DOI: 10.1007/978-1-62703-050-2_3] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Abstract
Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.
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Affiliation(s)
- Sandhya Kortagere
- Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA.
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