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Fukunishi Y, Higo J, Kasahara K. Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles. Biophys Rev 2022; 14:1423-1447. [PMID: 36465086 PMCID: PMC9703445 DOI: 10.1007/s12551-022-01015-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/06/2022] [Indexed: 11/29/2022] Open
Abstract
Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.
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Affiliation(s)
- Yoshifumi Fukunishi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26, Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Junichi Higo
- Graduate School of Information Science, University of Hyogo, 7-1-28 Minatojima Minamimachi, Chuo-Ku, Kobe, Hyogo 650-0047 Japan ,Research Organization of Science and Technology, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
| | - Kota Kasahara
- College of Life Sciences, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577 Japan
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2
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Gan JL, Kumar D, Chen C, Taylor BC, Jagger BR, Amaro RE, Lee CT. Benchmarking ensemble docking methods in D3R Grand Challenge 4. J Comput Aided Mol Des 2022; 36:87-99. [PMID: 35199221 PMCID: PMC8907095 DOI: 10.1007/s10822-021-00433-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 11/16/2021] [Indexed: 11/30/2022]
Abstract
The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.
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Affiliation(s)
- Jessie Low Gan
- San Diego Jewish Academy, San Diego, 92130, CA, USA.,California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dhruv Kumar
- Rancho Bernardo High School, San Diego, CA, 92128, USA.,University of California Berkeley, Berkeley, CA, USA
| | - Cynthia Chen
- California Institute of Technology, Pasadena, CA, 91125, USA.,Canyon Crest Academy, San Diego, CA, 92130, USA
| | - Bryn C Taylor
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Discovery Sciences, Janssen Research and Development, San Diego, CA, 92121, USA
| | - Benjamin R Jagger
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.,Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
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3
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Tendulkar R, Chouhan A, Gupta A, Chaudhary A, Dubey C, Shukla S. Structure-Based Drug Design and Development of Novel Synthetic Compounds with Anti-Viral Property against SARS-COV-2. Curr Drug Discov Technol 2022; 19:e280122200663. [PMID: 35088672 DOI: 10.2174/1570163819666220128145724] [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: 08/20/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 11/22/2022]
Abstract
• Background : The world is suffering from health and economic devastation due to the Corona Virus Disease-2019 (COVID-19) pandemic. Given the number of people affected and also the death rate, the virus is definitely a serious threat to humanity. By analogy with previous reports on the Severe Acute Respiratory Syndrome (SARS-CoV-2) virus, the novel replication mechanism of the coronavirus is likely well understood. • Objective : The antiviral activity of various compounds of the flavonoid class was checked against SARS-COVID-19 using diverse tools and software. • Method : From the flavonoid compound class, 100 synthetic compounds with potential antiviral activity were selected and improved for screening and induced fit docking, which was reduced to 25 compounds with good docking score and docking energy. In addition to the apparent match of the molecule with the shape of the binding pocket, a full analysis of the non-covalent interactions in the active site was assessed. • Results : Compounds (nol26), (fla37-fl40), (an32), (an39) showed a maximum docking score, which shows essential interactions for a tight bond. Now, all compounds are synthetic with beneficial drug-like properties. • Conclusion : During the docking study, an increased lipophilic interaction of compounds due to the presence of chlorine in (nol26), (fla37-fl40), (an32), (an39) was discovered. (fla37-fla40) can be investigated as lead molecules against SARS-COV-2 in futuristic drug development.
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4
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Chen QH, Krishnan VV. Identification of ligand binding sites in intrinsically disordered proteins with a differential binding score. Sci Rep 2021; 11:22583. [PMID: 34799573 PMCID: PMC8604960 DOI: 10.1038/s41598-021-00869-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
Screening ligands directly binding to an ensemble of intrinsically disordered proteins (IDP) to discover potential hits or leads for new drugs is an emerging but challenging area as IDPs lack well-defined and ordered 3D-protein structures. To explore a new IDP-based rational drug discovery strategy, a differential binding score (DIBS) is defined. The basis of DIBS is to quantitatively determine the binding preference of a ligand to an ensemble of conformations specified by IDP versus such preferences to an ensemble of random coil conformations of the same protein. Ensemble docking procedures performed on repeated sampling of conformations, and the results tested for statistical significance determine the preferential ligand binding sites of the IDP. The results of this approach closely reproduce the experimental data from recent literature on the binding of the ligand epigallocatechin gallate (EGCG) to the intrinsically disordered N-terminal domain of the tumor suppressor p53. Combining established approaches in developing a new method to screen ligands against IDPs could be valuable as a screening tool for IDP-based drug discovery.
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Affiliation(s)
- Qiao-Hong Chen
- Department of Chemistry and Biochemistry, California State University Fresno, Fresno, CA, 93740, USA
| | - V V Krishnan
- Department of Chemistry and Biochemistry, California State University Fresno, Fresno, CA, 93740, USA. .,Department of Pathology and Molecular Medicine, University of California Davis, Davis, CA, 95616, USA.
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5
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Saranyadevi S. Multifaceted targeting strategies in cancer against the human notch 3 protein: a computational study. In Silico Pharmacol 2021; 9:53. [PMID: 34631360 DOI: 10.1007/s40203-021-00112-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 09/20/2021] [Indexed: 11/30/2022] Open
Abstract
Notch receptors play a significant role in the development and the regulation of cell-fate in several multicellular organisms. For normal differentiation, genomes are essential as their regular roles and play a role in cancer is dysregulated. Notch 3 has been shown to play a major role in lung cancer function and therefore, inhibition of notch 3 protein activation represents a clear plan for cancer treatment. This study accomplished a combined structure- and ligand-based pharmacophore hypothesis to explore novel notch 3 inhibitors. The analysis identified common lead molecule ZINC000013449462 that showed better XP GScore and binding energy score than the reference inhibitor DAPT. The identified lead compound that passed all the druggable characteristics exhibited stable binding. Furthermore, the lead molecule can also form hydrogen and salt bridge interactions with binding site residues Asp1621 and Arg1465 residues, respectively of the active pockets of notch 3 protein. In essence, the inhibitory activity of the hit was validated across 109 NSCLC cell lines by employing a deep neural network algorithm. Our study proposes that ZINC000013449462 would be a possible prototype molecule towards the notch 3 target and further examined by clinical studies to combat NSCLC.
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Affiliation(s)
- S Saranyadevi
- Department of Nanotechnology, Nanodot Research Private Limited, Nagercoil, Kanyakumari, 629001 India
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6
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Manoliu LCE, Martin EC, Milac AL, Spiridon L. Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor. Molecules 2021; 26:4894. [PMID: 34443478 PMCID: PMC8399775 DOI: 10.3390/molecules26164894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/02/2021] [Accepted: 08/09/2021] [Indexed: 11/17/2022] Open
Abstract
Alzheimer's disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand-binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs.
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Affiliation(s)
| | | | | | - Laurentiu Spiridon
- Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independenţei 296, 060031 Bucharest, Romania; (L.C.E.M.); (E.C.M.); (A.L.M.)
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7
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Dvorácskó S, Lázár L, Fülöp F, Palkó M, Zalán Z, Penke B, Fülöp L, Tömböly C, Bogár F. Novel High Affinity Sigma-1 Receptor Ligands from Minimal Ensemble Docking-Based Virtual Screening. Int J Mol Sci 2021; 22:8112. [PMID: 34360878 PMCID: PMC8347176 DOI: 10.3390/ijms22158112] [Citation(s) in RCA: 3] [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: 07/04/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Sigma-1 receptor (S1R) is an intracellular, multi-functional, ligand operated protein that also acts as a chaperone. It is considered as a pluripotent drug target in several pathologies. The publication of agonist and antagonist bound receptor structures has paved the way for receptor-based in silico drug design. However, recent studies on this subject payed no attention to the structural differences of agonist and antagonist binding. In this work, we have developed a new ensemble docking-based virtual screening protocol utilizing both agonist and antagonist bound S1R structures. This protocol was used to screen our in-house compound library. The S1R binding affinities of the 40 highest ranked compounds were measured in competitive radioligand binding assays and the sigma-2 receptor (S2R) affinities of the best S1R binders were also determined. This way three novel high affinity S1R ligands were identified and one of them exhibited a notable S1R/S2R selectivity.
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Affiliation(s)
- Szabolcs Dvorácskó
- Biological Research Centre, Institute of Biochemistry, Eötvös Loránd Research Network (ELKH), H-6726 Szeged, Hungary; (S.D.); (C.T.)
| | - László Lázár
- Institute of Pharmaceutical Chemistry, University of Szeged, H-6720 Szeged, Hungary; (L.L.); (F.F.); (M.P.); (Z.Z.)
| | - Ferenc Fülöp
- Institute of Pharmaceutical Chemistry, University of Szeged, H-6720 Szeged, Hungary; (L.L.); (F.F.); (M.P.); (Z.Z.)
| | - Márta Palkó
- Institute of Pharmaceutical Chemistry, University of Szeged, H-6720 Szeged, Hungary; (L.L.); (F.F.); (M.P.); (Z.Z.)
| | - Zita Zalán
- Institute of Pharmaceutical Chemistry, University of Szeged, H-6720 Szeged, Hungary; (L.L.); (F.F.); (M.P.); (Z.Z.)
| | - Botond Penke
- Department of Medical Chemistry, University of Szeged, H-6720 Szeged, Hungary;
| | - Lívia Fülöp
- Department of Medical Chemistry, University of Szeged, H-6720 Szeged, Hungary;
| | - Csaba Tömböly
- Biological Research Centre, Institute of Biochemistry, Eötvös Loránd Research Network (ELKH), H-6726 Szeged, Hungary; (S.D.); (C.T.)
| | - Ferenc Bogár
- Department of Medical Chemistry, University of Szeged, H-6720 Szeged, Hungary;
- MTA-SZTE Biomimetic Systems Research Group, Eötvös Loránd Research Network (ELKH), H-6720 Szeged, Hungary
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8
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Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
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9
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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10
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Shi M, Zhao M, Wang L, Liu K, Li P, Liu J, Cai X, Chen L, Xu D. Exploring the stability of inhibitor binding to SIK2 using molecular dynamics simulation and binding free energy calculation. Phys Chem Chem Phys 2021; 23:13216-13227. [PMID: 34086021 DOI: 10.1039/d1cp00717c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Salt inducible kinase 2 (SIK2) is a calcium/calmodulin-dependent protein kinase-like kinase that is implicated in a variety of biological phenomena, including cellular metabolism, growth, and apoptosis. SIK2 is the key target for various cancers, including ovarian, breast, prostate, and lung cancers. Although potent inhibitors of SIK2 are being developed, their binding stability and functional role are not presently known. In this work, we studied the detailed interactions between SIK2 and four of its inhibitors, HG-9-91-01, KIN112, MRT67307, and MRT199665, using molecular docking, molecular dynamics simulation, binding free energy calculation, and interaction fingerprint analysis. Intermolecular interactions revealed that HG-9-91-01 and KIN112 have stronger interactions with SIK2 than those of MRT199665 and MRT67307. The key residues involved in binding with SIK2 are conserved among all four inhibitors. Our results explain the detailed interaction of SIK2 with its inhibitors at the molecular level, thus paving the way for the development of targeted efficient anti-cancer drugs.
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Affiliation(s)
- Mingsong Shi
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Min Zhao
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lun Wang
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Kongjun Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Penghui Li
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China.
| | - Jiang Liu
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Xiaoying Cai
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Lijuan Chen
- State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.
| | - Dingguo Xu
- College of Chemistry, MOE Key Laboratory of Green Chemistry and Technology, Sichuan University, Chengdu, Sichuan 610064, China. and Research Center for Material Genome Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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11
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Privat C, Granadino-Roldán JM, Bonet J, Santos Tomas M, Perez JJ, Rubio-Martinez J. Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites. Phys Chem Chem Phys 2021; 23:3123-3134. [PMID: 33491698 DOI: 10.1039/d0cp05471b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 μs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.
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Affiliation(s)
- Cristian Privat
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - José M Granadino-Roldán
- Departamento de Química Física y Analítica, Facultad de Ciencias Experimentales, Universidad de Jaén, Campus "Las Lagunillas" s/n, 23071, Jaén, Spain
| | - Jordi Bonet
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
| | - Maria Santos Tomas
- Department of Architecture Technology, Universitat Politecnica de Catalunya, Av. Diagonal 649, 08028 Barcelona, Spain
| | - Juan J Perez
- Deparment of Chemical Engineering, Universitat Politecnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain
| | - Jaime Rubio-Martinez
- Departament de Ciència dels Materials i Química Física, Universitat de Barcelona (UB) and the Institut de Quimica Teorica i Computacional (IQTCUB), Martí i Franqués 1, 08028 Barcelona, Spain.
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12
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Straub JE. New and notable: A multiscale coarse-grained model of the SARS-CoV-2 virion. Biophys J 2021; 120:975-976. [PMID: 33577762 PMCID: PMC7837621 DOI: 10.1016/j.bpj.2020.12.032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 12/30/2020] [Indexed: 11/24/2022] Open
Affiliation(s)
- John E Straub
- Department of Chemistry, Boston University, Boston, Massachusetts.
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13
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Lee W, Park JW, Go YJ, Kim WJ, Rhee YM. Considering both small and large scale motions of vascular endothelial growth factor (VEGF) is crucial for reliably predicting its binding affinities to DNA aptamers. RSC Adv 2021; 11:9315-9326. [PMID: 35423456 PMCID: PMC8695334 DOI: 10.1039/d0ra10106k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/23/2021] [Indexed: 11/21/2022] Open
Abstract
Considering both small and large scale motions of VEGF is crucial to predict its relative binding affinities to DNA aptamer variants with docking.
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Affiliation(s)
- Wook Lee
- Department of Chemistry
- Pohang University of Science and Technology (POSTECH)
- Pohang 37673
- Korea
- Department of Chemistry
| | - Jae Whee Park
- Department of Chemistry
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Korea
| | - Yeon Ju Go
- Department of Chemistry
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Korea
| | - Won Jong Kim
- Department of Chemistry
- Pohang University of Science and Technology (POSTECH)
- Pohang 37673
- Korea
| | - Young Min Rhee
- Department of Chemistry
- Korea Advanced Institute of Science and Technology (KAIST)
- Daejeon 34141
- Korea
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14
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Acharya A, Agarwal R, Baker M, Baudry J, Bhowmik D, Boehm S, Byler KG, Chen S, Coates L, Cooper C, Demerdash O, Daidone I, Eblen J, Ellingson S, Forli S, Glaser J, Gumbart JC, Gunnels J, Hernandez O, Irle S, Kneller D, Kovalevsky A, Larkin J, Lawrence T, LeGrand S, Liu SH, Mitchell J, Park G, Parks J, Pavlova A, Petridis L, Poole D, Pouchard L, Ramanathan A, Rogers D, Santos-Martins D, Scheinberg A, Sedova A, Shen Y, Smith J, Smith M, Soto C, Tsaris A, Thavappiragasam M, Tillack A, Vermaas J, Vuong V, Yin J, Yoo S, Zahran M, Zanetti-Polzi L. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19. J Chem Inf Model 2020; 60:5832-5852. [PMID: 33326239 PMCID: PMC7754786 DOI: 10.1021/acs.jcim.0c01010] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Indexed: 01/18/2023]
Abstract
We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
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Affiliation(s)
- A. Acharya
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - R. Agarwal
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - M. Baker
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - J. Baudry
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - D. Bhowmik
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - S. Boehm
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - K. G. Byler
- The University of Alabama in Huntsville, Department of Biological Sciences. 301 Sparkman Drive, Huntsville, AL 35899, USA
| | - S.Y. Chen
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - L. Coates
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C.J. Cooper
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - O. Demerdash
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - I. Daidone
- Department of Physical and Chemical Sciences, University of L’Aquila, I-67010 L’Aquila, Italy
| | - J.D. Eblen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - S. Ellingson
- University of Kentucky, Division of Biomedical Informatics, College of Medicine, UK Medical Center MN 150, Lexington KY, 40536, USA
| | - S. Forli
- Scripps Research, La Jolla, CA, 92037, USA
| | - J. Glaser
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - J. C. Gumbart
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - J. Gunnels
- HPC Engineering, Amazon Web Services, Seattle, WA 98121, USA
| | - O. Hernandez
- Computer Science and Mathematics Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. Irle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - D.W. Kneller
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - A. Kovalevsky
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - J. Larkin
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - T.J. Lawrence
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - S. LeGrand
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - S.-H. Liu
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - J.C. Mitchell
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - G. Park
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - J.M. Parks
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - A. Pavlova
- School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - L. Petridis
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - D. Poole
- NVIDIA Corporation, Santa Clara, CA 95051, USA
| | - L. Pouchard
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Ramanathan
- Data Science and Learning Division, Argonne National Lab, Lemont, IL 60439, USA
| | - D. Rogers
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - A. Sedova
- Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA
| | - Y. Shen
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
| | - J.C. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - M.D. Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, TN, 37830, USA
- The University of Tennessee, Knoxville. Department of Biochemistry & Cellular and Molecular Biology, 309 Ken and Blaire Mossman Bldg. 1311 Cumberland Avenue Knoxville, TN, 37996, USA
| | - C. Soto
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - A. Tsaris
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | | | | | - J.V. Vermaas
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - V.Q. Vuong
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, TN 37996, USA
| | - J. Yin
- National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - S. Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - M. Zahran
- Department of Biological Sciences, New York City College of Technology, The City University of New York (CUNY), Brooklyn, NY 11201, USA
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15
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Bhattarai A, Wang J, Miao Y. Retrospective ensemble docking of allosteric modulators in an adenosine G-protein-coupled receptor. Biochim Biophys Acta Gen Subj 2020; 1864:129615. [PMID: 32298791 DOI: 10.1016/j.bbagen.2020.129615] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 02/26/2020] [Accepted: 04/08/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR. METHODS Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR. RESULTS Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased. CONCLUSIONS Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking. GENERAL SIGNIFICANCE Ensemble docking is a promising approach for drug discovery targeting flexible receptors.
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Affiliation(s)
- Apurba Bhattarai
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA
| | - Jinan Wang
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA
| | - Yinglong Miao
- Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA.
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16
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Fan N, Bauer CA, Stork C, de Bruyn Kops C, Kirchmair J. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance. Mol Inform 2019; 39:e1900103. [PMID: 31663691 PMCID: PMC7187304 DOI: 10.1002/minf.201900103] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/28/2019] [Indexed: 01/16/2023]
Abstract
Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single-structure docking runs, ensemble docking and a similarity-based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure-based virtual screening of malleable proteins, including kinases, some viral enzymes and anti-targets.
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Affiliation(s)
- Ningning Fan
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Christoph A Bauer
- University of Bergen, Department of Chemistry, N-5020, Bergen, Norway.,University of Bergen, Computational Biology Unit (CBU), N-5020, Bergen, Norway
| | - Conrad Stork
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Christina de Bruyn Kops
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany
| | - Johannes Kirchmair
- Universität Hamburg, Faculty of Mathematics, Informatics and Natural Sciences, Department of Informatics, Center for Bioinformatics, 20146, Hamburg, Germany.,University of Bergen, Department of Chemistry, N-5020, Bergen, Norway.,University of Bergen, Computational Biology Unit (CBU), N-5020, Bergen, Norway
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17
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Rohini K, Ramanathan K, Shanthi V. Multi-Dimensional Screening Strategy for Drug Repurposing with Statistical Framework—A New Road to Influenza Drug discovery. Cell Biochem Biophys 2019; 77:319-333. [DOI: 10.1007/s12013-019-00887-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 09/16/2019] [Indexed: 12/17/2022]
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18
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Rohini K, Roy R, Ramanathan K, Shanthi V. E-pharmacophore hypothesis strategy to discover potent inhibitor for influenza treatment. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2019. [DOI: 10.1142/s0219633619500214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The surface protein of Influenza virus, Neuraminidase (NA), is believed to play a critical role in the release of new viral particle and thus spreads infection. It has been recognized as a valid drug target for anti-influenza therapy. Despite the number of available approved drugs for the influenza infection treatment, the emergence of resistant variants with novel mutations are the foremost challenges for the currently used NA inhibitors. Thus, the current investigation was carried out to ascertain potent inhibitors using computational strategies such as e-pharmacophore based virtual screening and docking approach. A three-dimensional e-pharmacophore hypothesis was generated based on the chemical features of complexes of the drugs and NA protein using PHASE module of Schrödinger suite. The generated hypothesis consisted of one hydrogen bond acceptor (A), two hydrogen bond donors (D), one negatively charged group (N) and one aromatic ring (R), ADDNR. The hypothesis was further evaluated for its integrity using enrichment analysis and used to filter out molecules with similar pharmacophoric features from approved, investigational and experimental subsets of DrugBank and ZINC database. In addition, ligand filtration was performed to curb down the molecules to an efficient collection of hit molecules by using Lipinski “rule of five and ADME analysis by using Qikprop module. Overall, the results from our analysis suggest that compound lisinopril and formoterol could serve as potent antiviral compounds for the treatment of influenza A virus infection. It is worth mentioning that the results correlate well with literature evidences.
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Affiliation(s)
- K. Rohini
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Roosha Roy
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - K. Ramanathan
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - V. Shanthi
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
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19
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Choudhury C, Narahari Sastry G. Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2019. [DOI: 10.1007/978-3-030-05282-9_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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20
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Chen J, Peng C, Wang J, Zhu W. Exploring molecular mechanism of allosteric inhibitor to relieve drug resistance of multiple mutations in HIV-1 protease by enhanced conformational sampling. Proteins 2018; 86:1294-1305. [PMID: 30260044 DOI: 10.1002/prot.25610] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 09/02/2018] [Accepted: 09/23/2018] [Indexed: 12/12/2022]
Abstract
Recently, allosteric regulations of HIV-1 protease (PR) are suggested as a promising approach to relieve drug resistance of mutations toward inhibitors targeting the active site of PR. Replica-exchange molecular dynamics (REMD) simulations and normal mode analysis (NMA) are integrated to enhance conformational sampling of PR. Molecular mechanics generalized Born surface area (MM-GBSA) method was applied to calculate binding free energies of three inhibitors APV, DRV, and NIT to the wild-type (WT) and multidrug resistance (MDR) PRs. The results suggest that binding free energies of APV and DRV are decreased in the MDR PR relative to the WT PR, suggesting drug resistance of mutations on these two inhibitors. However, the binding ability of the allosteric inhibitor NIT is not impaired in the MDR PR. In addition, internal dynamics analysis based on REMD simulations proves that mutations hardly produce obvious effect on the conformation of the MDR PR in comparison to the WT PR. Scanning of hydrophobic contacts and hydrogen bond contacts of inhibitors with residues of PRs on the concatenated trajectories of REMD demonstrates that mutations change the symmetric interaction networks of APV and DRV with PR, but do not generate obvious influence on the asymmetric interaction network of NIT with PR. In summary, allosteric inhibitor NIT can adapt the MDR PR better than those inhibitors toward the active site of PR, thus allosteric inhibitors of PR may be a possible channel to overcome drug resistance of PR.
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Affiliation(s)
- Jianzhong Chen
- School of Science, Shandong Jiaotong University, Jinan, China.,Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Cheng Peng
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.,School of Chemistry, University of Chinese Academy of Sciences, Beijing, China
| | - Jinan Wang
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Weiliang Zhu
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
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21
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Rohini K, Agarwal P, Preethi B, Shanthi V, Ramanathan K. Exploring the Lead Compounds for Zika Virus NS2B-NS3 Protein: an e-Pharmacophore-Based Approach. Appl Biochem Biotechnol 2018; 187:194-210. [PMID: 29911269 DOI: 10.1007/s12010-018-2814-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 06/05/2018] [Indexed: 12/19/2022]
Abstract
The rapid spread of the Zika virus and its association with the abnormal brain development constitute a global health emergency. With a continuing spread of the mosquito vector, the exposure is expected to accelerate in the coming years. Despite number of efforts, there is still no proper vaccine or medicine to combat this virus. Of note, the NS2B-NS3 protein is proven to be the potential target for the Zika virus therapeutics. Hence, e-pharmacophore-based drug design strategy was employed to identify potent inhibitors of NS2B-NS3 protein from ASINEX database consisting of 467,802 molecules. A 3D e-pharmacophore model was generated using PHASE module of Schrödinger Suite. The generated model consists of one hydrogen bond acceptor (A), two hydrogen bond donors (D), and two aromatic rings (R), ADDRR. The model was further evaluated for its ability to screen actives using enrichment analysis. Subsequently, high-throughput virtual screening protocol was employed, and the resultant hit molecules were also examined for its binding free energies and ADME properties using Prime MM-GBSA and Qikprop module of Schrodinger packages, respectively. Finally, the screened hit molecule was subjected to molecular dynamics simulation to examine its stability. Overall, the results from our analysis suggest that compound BAS 19192837 could be a potent inhibitor for the NS2B-NS3 protein of the Zika virus. It is also noteworthy to mention that our results are in good agreement with literature evidences. We hope that this result is of immense importance in designing potential drug molecules to combat the spread of Zika virus in the near future.
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Affiliation(s)
- K Rohini
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India
| | - Pratika Agarwal
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India
| | - B Preethi
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India
| | - V Shanthi
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India
| | - K Ramanathan
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamilnadu, 632014, India.
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22
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Amaro RE, Baudry J, Chodera J, Demir Ö, McCammon JA, Miao Y, Smith JC. Ensemble Docking in Drug Discovery. Biophys J 2018; 114:2271-2278. [PMID: 29606412 DOI: 10.1016/j.bpj.2018.02.038] [Citation(s) in RCA: 263] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 02/13/2018] [Accepted: 02/20/2018] [Indexed: 12/11/2022] Open
Abstract
Ensemble docking corresponds to the generation of an "ensemble" of drug target conformations in computational structure-based drug discovery, often obtained by using molecular dynamics simulation, that is used in docking candidate ligands. This approach is now well established in the field of early-stage drug discovery. This review gives a historical account of the development of ensemble docking and discusses some pertinent methodological advances in conformational sampling.
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Affiliation(s)
- Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California
| | - Jerome Baudry
- University of Alabama at Huntsville, Huntsville, Alabama
| | - John Chodera
- University of California, Berkeley, Berkeley, California
| | - Özlem Demir
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California
| | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California
| | - Yinglong Miao
- Department of Computational Biology and Molecular Biosciences, University of Kansas, Lawrence, Kansas
| | - Jeremy C Smith
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, Tennessee; Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, Tennessee.
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23
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Ghanakota P, Carlson HA. Comparing pharmacophore models derived from crystallography and NMR ensembles. J Comput Aided Mol Des 2017; 31:979-993. [PMID: 29047011 DOI: 10.1007/s10822-017-0077-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Accepted: 10/12/2017] [Indexed: 10/18/2022]
Abstract
NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109-1065, USA.
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24
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Abstract
Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.
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25
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Ghanakota P, Carlson HA. Driving Structure-Based Drug Discovery through Cosolvent Molecular Dynamics. J Med Chem 2016; 59:10383-10399. [PMID: 27486927 DOI: 10.1021/acs.jmedchem.6b00399] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Identifying binding hotspots on protein surfaces is of prime interest in structure-based drug discovery, either to assess the tractability of pursuing a protein target or to drive improved potency of lead compounds. Computational approaches to detect such regions have traditionally relied on energy minimization of probe molecules onto static protein conformations in the absence of the natural aqueous environment. Advances in high performance computing now allow us to assess hotspots using molecular dynamics (MD) simulations. MD simulations integrate protein flexibility and the complicated role of water, thereby providing a more realistic assessment of the complex kinetics and thermodynamics at play. In this review, we describe the evolution of various cosolvent-based MD techniques and highlight a myriad of potential applications for such technologies in computational drug development.
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Affiliation(s)
- Phani Ghanakota
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
| | - Heather A Carlson
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
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26
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Ghasemi JB, Abdolmaleki A, Shiri F. Molecular Docking Challenges and Limitations. ADVANCES IN MEDICAL TECHNOLOGIES AND CLINICAL PRACTICE 2016. [DOI: 10.4018/978-1-5225-0362-0.ch003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Today, the development of new drugs is a challenging task of science. Researchers already applied molecular docking in the drug design field to simulate ligand- receptor interactions. Docking is a term used for computational schemes that attempt to find the “best” matching between two molecules in a complex formed from constituent molecules. It has a wide range of uses and applications in drug discovery. However, some defects still exist; the accuracy and speed of docking calculation is a challenge to explore and these methods can be enhanced as a solution to docking problem. The molecular docking problem can be defined as follows: Given the atomic coordinates of two molecules, predict their “correct” bound association. The chapter discusses common challenges critical aspects of docking method such as ligand- and receptor- conformation, flexibility and cavity detection, etc. It emphasis to the challenges and inadequacies with the theories behind as well as the examples.
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27
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Wong CF. Conformational transition paths harbor structures useful for aiding drug discovery and understanding enzymatic mechanisms in protein kinases. Protein Sci 2016; 25:192-203. [PMID: 26032746 PMCID: PMC4815305 DOI: 10.1002/pro.2716] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2015] [Revised: 05/24/2015] [Accepted: 05/25/2015] [Indexed: 12/24/2022]
Abstract
This short article examines the usefulness of fast simulations of conformational transition paths in elucidating enzymatic mechanisms and guiding drug discovery for protein kinases. It applies the transition path method in the MOIL software package to simulate the paths of conformational transitions between six pairs of structures from the Protein Data Bank. The structures along the transition paths were found to resemble experimental structures that mimic transient structures believed to form during enzymatic catalysis or conformational transitions, or structures that have drug candidates bound. These findings suggest that such simulations could provide quick initial insights into the enzymatic mechanisms or pathways of conformational transitions of proteins kinases, or could provide structures useful for aiding structure-based drug design.
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Affiliation(s)
- Chung F Wong
- Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-Saint Louis, Saint Louis, Missouri, 63121
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28
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Abstract
Over the past two decades, solvent mapping has emerged as a useful tool for identifying hot spots within binding sites on proteins for drug-like molecules and suggesting properties of potential binders. While the experimental technique requires solving multiple crystal structures of a protein in different solvents, computational solvent mapping allows for fast analysis of a protein for potential binding sites and their druggability. Recent advances in genomics, systems biology and interactomics provide a multitude of potential targets for drug development and solvent mapping can provide useful information to help prioritize targets for drug discovery projects. Here, we review various approaches to computational solvent mapping, highlight some key advances and provide our opinion on future directions in the field.
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29
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Choudhury C, Priyakumar UD, Sastry GN. Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. J Chem Inf Model 2015; 55:848-60. [PMID: 25751016 DOI: 10.1021/ci500737b] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The therapeutic challenges in the treatment of tuberculosis demand multidisciplinary approaches for the identification of potential drug targets as well as fast and accurate techniques to screen huge chemical libraries. Mycobacterial cyclopropane synthase (CmaA1) has been shown to be essential for the survival of the bacteria due to its critical role in the synthesis of mycolic acids. The present study proposes pharmacophore models based on the structure of CmaA1 taking into account its various states in the cyclopropanation process, and their dynamic nature as assessed using molecular dynamics (MD) simulations. The qualities of these pharmacophore models were validated by mapping 23 molecules that have been previously reported to exhibit inhibitory activities on CmaA1. Additionally, 1398 compounds that have been shown to be inactive for tuberculosis were collected from the ChEMBL database and were screened against the models for validation. The models were further validated by comparing the results from pharmacophore mapping with the results obtained from docking these molecules with the respective protein structures. The best models are suggested by validating all the models based on their screening abilities and by comparing with docking results. The models generated from the MD trajectories were found to perform better than the one generated based on the crystal structure demonstrating the importance of incorporating receptor flexibility in drug design.
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Affiliation(s)
- Chinmayee Choudhury
- †Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information and Technology, Hyderabad 500032, India
- ‡Centre for Molecular Modeling, Indian Institute of Chemical Technology, Hyderabad 500007, India
| | - U Deva Priyakumar
- †Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information and Technology, Hyderabad 500032, India
| | - G Narahari Sastry
- ‡Centre for Molecular Modeling, Indian Institute of Chemical Technology, Hyderabad 500007, India
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30
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Kallubai M, Rachamallu A, Yeggoni DP, Subramanyam R. Comparative binding mechanism of lupeol compounds with plasma proteins and its pharmacological importance. MOLECULAR BIOSYSTEMS 2015; 11:1172-83. [DOI: 10.1039/c4mb00635f] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Binding of lupeol compounds with plasma proteins.
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Affiliation(s)
- Monika Kallubai
- Department of Plant Sciences
- School of Life Sciences
- University of Hyderabad
- Hyderabad 500046
- India
| | - Aparna Rachamallu
- National Institute of Animal Biotechnology
- Axis Clinicals Building
- Hyderabad
- India
| | | | - Rajagopal Subramanyam
- Department of Plant Sciences
- School of Life Sciences
- University of Hyderabad
- Hyderabad 500046
- India
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31
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Mittal A, Johnson ME. Conformational diversity of bacterial FabH: implications for molecular recognition specificity. J Mol Graph Model 2014; 55:115-22. [PMID: 25437098 DOI: 10.1016/j.jmgm.2014.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2014] [Revised: 09/22/2014] [Accepted: 11/07/2014] [Indexed: 11/25/2022]
Abstract
The molecular basis of variable substrate and inhibitor specificity of the highly conserved bacterial fatty acid synthase enzyme, FabH, across different bacterial species remains poorly understood. In the current work, we explored the conformational diversity of FabH enzymes to understand the determinants of diverse interaction specificity across Gram-positive and Gram-negative bacteria. Atomistic molecular dynamics simulations reveal that FabH from E. coli and E. faecalis exhibit distinct native state conformational ensembles and dynamic behaviors. Despite strikingly similar substrate binding pockets, hot spot assessment using computational solvent mapping identified quite different favorable binding interactions between the two homologs. Our data suggest that FabH utilizes protein dynamics and seemingly minor sequence and structural differences to modulate its molecular recognition and substrate specificity across bacterial species. These insights will potentially facilitate the rational design and development of antibacterial inhibitors against FabH enzymes.
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Affiliation(s)
- Anuradha Mittal
- Center for Pharmaceutical Biotechnology, University of Illinois at Chicago, 900 S. Ashland Ave-m/c 870, Chicago, IL 60607-7173, USA
| | - Michael E Johnson
- Center for Pharmaceutical Biotechnology, University of Illinois at Chicago, 900 S. Ashland Ave-m/c 870, Chicago, IL 60607-7173, USA.
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32
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Ung PMU, Dunbar JB, Gestwicki JE, Carlson HA. An allosteric modulator of HIV-1 protease shows equipotent inhibition of wild-type and drug-resistant proteases. J Med Chem 2014; 57:6468-78. [PMID: 25062388 PMCID: PMC4136727 DOI: 10.1021/jm5008352] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
![]()
NMR
and MD simulations have demonstrated that the flaps of HIV-1 protease
(HIV-1p) adopt a range of conformations that are coupled with its
enzymatic activity. Previously, a model was created for an allosteric
site located between the flap and the core of HIV-1p, called the Eye
site (2008, 89, 643−65218381626). Here, results from our first study were
combined with a ligand-based, lead-hopping method to identify a novel
compound (NIT). NIT inhibits HIV-1p, independent of the presence of
an active-site inhibitor such as pepstatin A. Assays showed that NIT
acts on an allosteric site other than the dimerization interface.
MD simulations of the ligand–protein complex show that NIT
stably binds in the Eye site and restricts the flaps. That bound state
of NIT is consistent with a crystal structure of similar fragments
bound in the Eye site (2010, 75, 257−26820659109). Most importantly,
NIT is equally potent against wild-type and a multidrug-resistant
mutant of HIV-1p, which highlights the promise of allosteric inhibitors
circumventing existing clinical resistance.
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Affiliation(s)
- Peter M-U Ung
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan , 428 Church Street, Ann Arbor, Michigan 48109-1065, United States
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33
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Lexa KW, Goh GB, Carlson HA. Parameter choice matters: validating probe parameters for use in mixed-solvent simulations. J Chem Inf Model 2014; 54:2190-9. [PMID: 25058662 PMCID: PMC4144759 DOI: 10.1021/ci400741u] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Probe mapping is a common approach
for identifying potential binding
sites in structure-based drug design; however, it typically relies
on energy minimizations of probes in the gas phase and a static protein
structure. The mixed-solvent molecular dynamics (MixMD) approach was
recently developed to account for full protein flexibility and solvation
effects in hot-spot mapping. Our first study used only acetonitrile
as a probe, and here, we have augmented the set of functional group
probes through careful testing and parameter validation. A diverse
range of probes are needed in order to map complex binding interactions.
A small variation in probe parameters can adversely effect mixed-solvent
behavior, which we highlight with isopropanol. We tested 11 solvents
to identify six with appropriate behavior in TIP3P water to use as
organic probes in the MixMD method. In addition to acetonitrile and
isopropanol, we have identified acetone, N-methylacetamide,
imidazole, and pyrimidine. These probe solvents will enable MixMD
studies to recover hydrogen-bonding sites, hydrophobic pockets, protein–protein
interactions, and aromatic hotspots. Also, we show that ternary-solvent
systems can be incorporated within a single simulation. Importantly,
these binary and ternary solvents do not require artificial repulsion
terms like other methods. Within merely 5 ns, layered solvent boxes
become evenly mixed for soluble probes. We used radial distribution
functions to evaluate solvent behavior, determine adequate mixing,
and confirm the absence of phase separation. We recommend that radial
distribution functions should be used to assess adequate sampling
in all mixed-solvent techniques rather than the current practice of
examining the solvent ratios at the edges of the solvent box.
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Affiliation(s)
- Katrina W Lexa
- Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor , 428 Church St., Ann Arbor, Michigan 48109-1065, United States
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34
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Fatima S, Jatavath MB, Bathini R, Sivan SK, Manga V. Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors. J Recept Signal Transduct Res 2014; 34:417-30. [DOI: 10.3109/10799893.2014.917323] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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35
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Honarparvar B, Govender T, Maguire GEM, Soliman MES, Kruger HG. Integrated Approach to Structure-Based Enzymatic Drug Design: Molecular Modeling, Spectroscopy, and Experimental Bioactivity. Chem Rev 2013; 114:493-537. [DOI: 10.1021/cr300314q] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Bahareh Honarparvar
- Catalysis
and Peptide Research Unit and ‡School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa
| | - Thavendran Govender
- Catalysis
and Peptide Research Unit and ‡School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa
| | - Glenn E. M. Maguire
- Catalysis
and Peptide Research Unit and ‡School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa
| | - Mahmoud E. S. Soliman
- Catalysis
and Peptide Research Unit and ‡School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa
| | - Hendrik G. Kruger
- Catalysis
and Peptide Research Unit and ‡School of Health Sciences, University of KwaZulu Natal, Durban 4001, South Africa
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36
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Abstract
Structure-based drug design has become an essential tool for rapid lead discovery and optimization. As available structural information has increased, researchers have become increasingly aware of the importance of protein flexibility for accurate description of the native state. Typical protein-ligand docking efforts still rely on a single rigid receptor, which is an incomplete representation of potential binding conformations of the protein. These rigid docking efforts typically show the best performance rates between 50 and 75%, while fully flexible docking methods can enhance pose prediction up to 80-95%. This review examines the current toolbox for flexible protein-ligand docking and receptor surface mapping. Present limitations and possibilities for future development are discussed.
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Affiliation(s)
- Katrina W. Lexa
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
| | - Heather A. Carlson
- Department of Medicinal Chemistry, University of Michigan, 428 Church Street, Ann Arbor, MI 48109-1065, USA
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37
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Genoni A, Pennati M, Morra G, Zaffaroni N, Colombo G. Ligand selection from the analysis of protein conformational substates: new leads targeting the N-terminal domain of Hsp90. RSC Adv 2012. [DOI: 10.1039/c2ra00911k] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
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38
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Flores SC, Gerstein MB. Predicting protein ligand binding motions with the conformation explorer. BMC Bioinformatics 2011; 12:417. [PMID: 22032721 PMCID: PMC3354956 DOI: 10.1186/1471-2105-12-417] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2011] [Accepted: 10/27/2011] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods. RESULTS We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins. CONCLUSIONS We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure.
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Affiliation(s)
- Samuel C Flores
- Department of Cell and Molecular Biology, Uppsala University, BMC Box 596, Uppsala, 75124, Sweden
| | - Mark B Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, PO Box 208114 MBB, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, PO Box 208114 MBB, New Haven, CT, 06520, USA
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39
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40
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Sivan SK, Manga V. Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors. J Mol Model 2011; 18:569-82. [PMID: 21547550 DOI: 10.1007/s00894-011-1048-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2010] [Accepted: 03/18/2011] [Indexed: 11/25/2022]
Abstract
Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.
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Affiliation(s)
- Sree Kanth Sivan
- Department of Chemistry, Nizam College, Osmania University, Hyderabad 500001, India
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41
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Mitchell FL, Miles SM, Neres J, Bichenkova EV, Bryce RA. Tryptophan as a molecular shovel in the glycosyl transfer activity of Trypanosoma cruzi trans-sialidase. Biophys J 2010; 98:L38-40. [PMID: 20441732 DOI: 10.1016/j.bpj.2010.01.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2009] [Revised: 01/05/2010] [Accepted: 01/13/2010] [Indexed: 10/19/2022] Open
Abstract
Molecular dynamics investigations into active site plasticity of Trypanosoma cruzi trans-sialidase, a protein implicated in Chagas disease, suggest that movement of the Trp(312) loop plays an important role in the enzyme's sialic acid transfer mechanism. The observed Trp(312) flexibility equates to a molecular shovel action, which leads to the expulsion of the donor aglycone leaving group from the catalytic site. These computational simulations provide detailed structural insights into sialyl transfer by the trans-sialidase and may aid the design of inhibitors effective against this neglected tropical disease.
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Affiliation(s)
- Felicity L Mitchell
- School of Pharmacy and Pharmaceutical Sciences, University of Manchester, Manchester, United Kingdom
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42
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43
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Paulsen JL, Anderson AC. Scoring ensembles of docked protein:ligand interactions for virtual lead optimization. J Chem Inf Model 2010; 49:2813-9. [PMID: 19950979 DOI: 10.1021/ci9003078] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Ensembles of protein structures to simulate protein flexibility are widely used throughout several applications including virtual lead optimization where they have been shown to improve ligand ranking. Yet, there is no established convention for weighting individual scores generated from ensemble members. To investigate the best method for weighting ensemble scores for proper ligand ranking, a series of dihydrofolate reductase inhibitors was docked to ensembles of Candida albicans dihydrofolate reductase (CaDHFR) structures created from a molecular dynamics (MD) simulation. From a single MD simulation, two ensemble collections were generated, one of which was subjected to a minimization procedure to create a group of structures of equal probability. As expected, ligand ranking accuracy was significantly improved when Boltzmann weighting was applied to the energies of the ensemble without structural minimization (60%), relative to that achieved with averaging (36%). However, accuracy was further improved (72%) by averaging docking scores across a minimized ensemble. To examine whether this accuracy results from structural variation in the single trajectory versus the possibility that error is minimized by averaging, a third collection of receptor structures was created in which each member was taken from an independent molecular dynamics simulation after minimization. Comparison of the docking accuracy results from the single trajectory (72%) to this third collection (61%) showed decreased accuracy, suggesting that ligands are more accurately oriented and assessed when docked to the minimized ensemble from a single MD trajectory, an effect that is more than simply error minimization. Averaging docking scores over a minimized ensemble of another target, influenza A neuraminidase, yielded a ligand ranking accuracy of 83%, representing a 24% improvement over other methods tested.
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Affiliation(s)
- Janet L Paulsen
- Department of Pharmaceutical Sciences, University of Connecticut, 69 North Eagleville Road, Storrs, Connecticut 06269, USA.
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44
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Ekonomiuk D, Su XC, Ozawa K, Bodenreider C, Lim SP, Otting G, Huang D, Caflisch A. Flaviviral protease inhibitors identified by fragment-based library docking into a structure generated by molecular dynamics. J Med Chem 2009; 52:4860-8. [PMID: 19572550 DOI: 10.1021/jm900448m] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Fragment-based docking was used to select a conformation for virtual screening from a molecular dynamics trajectory of the West Nile virus nonstructural 3 protease. This conformation was chosen from an ensemble of 100 molecular dynamics snapshots because it optimally accommodates benzene, the most common ring in known drugs, and two positively charged fragments (methylguanidinium and 2-phenylimidazoline). The latter fragments were used as probes because of the large number of hydrogen bond acceptors in the substrate binding site of the protease. Upon high-throughput docking of a diversity set of 18,694 molecules and pose filtering, only five compounds were chosen for experimental validation, and two of them are active in the low micromolar range in an enzymatic assay and a tryptophan fluorescence quenching assay. Evidence for specific binding to the protease active site is provided by nuclear magnetic resonance spectroscopy. The two inhibitors have different scaffolds (diphenylurea and diphenyl ester) and are promising lead candidates because they have a molecular weight of about 300 Da.
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Affiliation(s)
- Dariusz Ekonomiuk
- Department of Biochemistry, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
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45
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Taufer M, Armen R, Chen J, Teller P, Brooks C. Computational multiscale modeling in protein--ligand docking. ACTA ACUST UNITED AC 2009; 28:58-69. [PMID: 19349252 DOI: 10.1109/memb.2009.931789] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In biological systems, the binding of small molecule ligands to proteins is a crucial process for almost every aspect of biochemistry and molecular biology. Enzymes are proteins that function by catalyzing specific biochemical reactions that convert reactants into products. Complex organisms are typically composed of cells in which thousands of enzymes participate in complex and interconnected biochemical pathways. Some enzymes serve as sequential steps in specific pathways (such as energy metabolism), while others function to regulate entire pathways and cellular functions [1]. Small molecule ligands can be designed to bind to a specific enzyme and inhibit the biochemical reaction. Inhibiting the activity of key enzymes may result in the entire biochemical pathways being turned on or off [2], [3]. Many small molecule drugs marketed today function in this generic way as enzyme inhibitors. If research identifies a specific enzyme as being crucial to the progress of disease, then this enzyme may be targeted with an inhibitor, which may slow down or reverse the progress of disease. In this way, enzymes are targeted from specific pathogens (e.g., virus, bacteria, fungi) for infectious diseases [4], [5], and human enzymes are targeted for noninfectious diseases such as cardiovascular disease, cancer, diabetes, and neurodegenerative diseases [6].
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Affiliation(s)
- Michela Taufer
- Department of Computer and Information Sciences, University of Delaware, Newark, 19716, USA.
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46
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Bolstad ESD, Anderson AC. In pursuit of virtual lead optimization: pruning ensembles of receptor structures for increased efficiency and accuracy during docking. Proteins 2009; 75:62-74. [PMID: 18781587 DOI: 10.1002/prot.22214] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Representing receptors as ensembles of protein conformations during docking is a powerful method to approximate protein flexibility and increase the accuracy of the resulting ranked list of compounds. Unfortunately, docking compounds against a large number of ensemble members can increase computational cost and time investment. In this article, we present an efficient method to evaluate and select the most contributive ensemble members prior to docking for targets with a conserved core of residues that bind a ligand moiety. We observed that ensemble members that preserve the geometry of the active site core are most likely to place ligands in the active site with a conserved orientation, generally rank ligands correctly and increase interactions with the receptor. A relative distance approach is used to quantify the preservation of the three-dimensional interatomic distances of the conserved ligand-binding atoms and prune large ensembles quickly. In this study, we investigate dihydrofolate reductase as an example of a protein with a conserved core; however, this method for accurately selecting relevant ensemble members a priori can be applied to any system with a conserved ligand-binding core, including HIV-1 protease, kinases, and acetylcholinesterase. Representing a drug target as a pruned ensemble during in silico screening should increase the accuracy and efficiency of high-throughput analyses of lead analogs.
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Affiliation(s)
- Erin S D Bolstad
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, USA
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47
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Bolstad ESD, Anderson AC. In pursuit of virtual lead optimization: the role of the receptor structure and ensembles in accurate docking. Proteins 2008; 73:566-80. [PMID: 18473360 DOI: 10.1002/prot.22081] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Accurate ranking during in silico lead optimization is critical to drive the generation of new ligands with higher affinity, yet it is especially difficult because of the subtle changes between analogs. In order to assess the role of the structure of the receptor in delivering accurate lead ranking results, we docked a set of forty related inhibitors to structures of one species of dihydrofolate reductase (DHFR) derived from crystallographic, NMR solution data, and homology models. In this study, the crystal structures yielded the superior results: the compounds were placed in the active site in the conserved orientation and the docking scores for 80% percent of the compounds clustered into the same bins as the measured affinity. Single receptor structures derived from NMR data or homology models did not serve as accurate docking receptors. To our knowledge, these are the first experiments that assess ranking of homologous lead compounds using a variety of receptor structures. We then extended the study to investigate whether ensembles, either computationally or experimentally derived, of all of the single starting structures aid, hinder or have no effect on the performance of the starting template. Impressively, when ensembles of receptor structures derived from NMR data or homology models were employed, docking accuracy improved to a level equal to that of the high resolution crystal structures. The same experiments using a second species of DHFR and set of ligands confirm the results. A comparison of the structures of the individual ensemble members to the starting structures shows that the effect of the ensembles can be ascribed to protein flexibility in addition to absorption of computational error.
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Affiliation(s)
- Erin S D Bolstad
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, Connecticut 06269, USA
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48
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May A, Zacharias M. Protein−Ligand Docking Accounting for Receptor Side Chain and Global Flexibility in Normal Modes: Evaluation on Kinase Inhibitor Cross Docking. J Med Chem 2008; 51:3499-506. [DOI: 10.1021/jm800071v] [Citation(s) in RCA: 73] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andreas May
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 6, D-28759 Bremen, Germany
| | - Martin Zacharias
- School of Engineering and Science, Jacobs University Bremen, Campus Ring 6, D-28759 Bremen, Germany
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49
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Molecular dynamics simulations of ligand-induced backbone conformational changes in the binding site of the periplasmic lysine-, arginine-, ornithine-binding protein. J Comput Aided Mol Des 2008; 22:799-814. [PMID: 18415021 DOI: 10.1007/s10822-008-9215-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2007] [Accepted: 04/01/2008] [Indexed: 10/22/2022]
Abstract
The periplasmic lysine-, arginine-, ornithine-binding protein (LAOBP) traps its ligands by a large hinge bending movement between two globular domains. The overall geometry of the binding site remains largely unchanged between the open (unliganded) and closed (liganded) forms, with only a small number of residues exhibiting limited movement of their side chains. However, in the case of the ornithine-bound structure, the backbone peptide bond between Asp11 and Thr12 undergoes a large rotation. Molecular dynamics simulations have been used to investigate the origin and mechanism of this backbone movement. Simulations allowing flexibility of a limited region and of the whole binding site, with and without bound ligands, suggest that this conformational change is induced by the binding of ornithine, leading to the stabilisation of an energetically favourable alternative conformation.
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50
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May A, Zacharias M. Energy minimization in low‐frequency normal modes to efficiently allow for global flexibility during systematic protein–protein docking. Proteins 2008; 70:794-809. [PMID: 17729269 DOI: 10.1002/prot.21579] [Citation(s) in RCA: 83] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Protein-protein association can frequently involve significant backbone conformational changes of the protein partners. A computationally rapid method has been developed that allows to approximately account for global conformational changes during systematic protein-protein docking starting from many thousands of start configurations. The approach employs precalculated collective degrees of freedom as additional variables during protein-protein docking minimization. The global collective degrees of freedom are obtained from normal mode analysis using a Gaussian network model for the protein. Systematic docking searches were performed on 10 test systems that differed in the degree of conformational change associated with complex formation and in the degree of overlap between observed conformational changes and precalculated flexible degrees of freedom. The results indicate that in case of docking searches that minimize the influence of local side chain conformational changes inclusion of global flexibility can significantly improve the agreement of the near-native docking solutions with the corresponding experimental structures. For docking of unbound protein partners in several cases an improved ranking of near native docking solutions was observed. This was achieved at a very modest ( approximately 2-fold) increase of computational demands compared to rigid docking. For several test cases the number of docking solutions close to experiment was also significantly enhanced upon inclusion of soft collective degrees of freedom. This result indicates that inclusion of global flexibility can facilitate in silico protein-protein association such that a greater number of different start configurations results in favorable complex formation.
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Affiliation(s)
- Andreas May
- School of Engineering and Science, Jacobs University Bremen, D-28759 Bremen, Germany
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