Software for molecular docking: a review

doi:10.1007/S12551-016-0247-1

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Software for molecular docking: a review

Journal Article•DOI•

Software for molecular docking: a review

Nataraj Sekhar Pagadala¹, Khajamohiddin Syed², Jack A. Tuszynski¹, Jack A. Tuszynski³•Institutions (3)

University of Alberta¹, Central University of Technology², Cross Cancer Institute³

16 Jan 2017-Biophysical Reviews (Springer Berlin Heidelberg)-Vol. 9, Iss: 2, pp 91-102

TL;DR: Docking against homology-modeled targets also becomes possible for proteins whose structures are not known, and the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes.

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Abstract: Molecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homology-modeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate.

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“Bioinformatics” 특집을 내면서

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장병탁, 김삼묘, 허철구

01 Aug 2000

TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.

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Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

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4,833 citations

Journal Article•DOI•

Drug repurposing: progress, challenges and recommendations

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Sudeep Pushpakom¹, Francesco Iorio², Patrick A. Eyers¹, K. Jane Escott³, Shirley Hopper⁴, Andrew D. Wells, Andrew J. Doig⁵, Tim Guilliams⁶, Joanna Latimer⁷, Christine J. McNamee¹, Alan Norris¹, Philippe Sanseau⁸, David Cavalla, Munir Pirmohamed¹ - Show less +10 more•Institutions (8)

University of Liverpool¹, Wellcome Trust Sanger Institute², AstraZeneca³, Medicines and Healthcare Products Regulatory Agency⁴, University of Manchester⁵, St John's Innovation Centre⁶, Medical Research Council⁷, GlaxoSmithKline⁸

01 Jan 2019-Nature Reviews Drug Discovery

TL;DR: Approaches used for drug repurposing (also known as drug repositioning) are presented, the challenges faced by the repurpose community are discussed, and innovative ways by which these challenges could be addressed are recommended to help realize the full potential of drugRepurposing.

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Abstract: Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

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2,365 citations

Journal Article•DOI•

The rise of deep learning in drug discovery.

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Hongming Chen¹, Ola Engkvist¹, Yinhai Wang¹, Marcus Olivecrona¹, Thomas Blaschke¹ - Show less +1 more•Institutions (1)

AstraZeneca¹

31 Jan 2018-Drug Discovery Today

TL;DR: The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery.

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1,068 citations

Journal Article•DOI•

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

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Bo Ram Beck, Bonggun Shin¹, Yoon Jung Choi, Sung-Soo Park, Keunsoo Kang² - Show less +1 more•Institutions (2)

Emory University¹, Dankook University²

30 Mar 2020-Computational and structural biotechnology journal

TL;DR: The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase.

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Abstract: The infection of a novel coronavirus found in Wuhan of China (SARS-CoV-2) is rapidly spreading, and the incidence rate is increasing worldwide. Due to the lack of effective treatment options for SARS-CoV-2, various strategies are being tested in China, including drug repurposing. In this study, we used our pre-trained deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI) to identify commercially available drugs that could act on viral proteins of SARS-CoV-2. The result showed that atazanavir, an antiretroviral medication used to treat and prevent the human immunodeficiency virus (HIV), is the best chemical compound, showing an inhibitory potency with Kd of 94.94 nM against the SARS-CoV-2 3C-like proteinase, followed by remdesivir (113.13 nM), efavirenz (199.17 nM), ritonavir (204.05 nM), and dolutegravir (336.91 nM). Interestingly, lopinavir, ritonavir, and darunavir are all designed to target viral proteinases. However, in our prediction, they may also bind to the replication complex components of SARS-CoV-2 with an inhibitory potency with Kd

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573 citations

Cites background from "Software for molecular docking: a r..."

...This deep learning-based approach is particularly useful, since it does not require protein structural information, which can be a bottleneck for identifying drugs targeted for uncharacterized proteins with traditional three-dimensional (3D) structure-based docking approaches [20]....
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Journal Article•DOI•

Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds.

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Anh-Tien Ton¹, Francesco Gentile¹, Michael Hsing¹, Fuqiang Ban¹, Artem Cherkasov¹ - Show less +1 more•Institutions (1)

University of British Columbia¹

01 Aug 2020-Molecular Informatics

TL;DR: Deep Docking is applied to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS‐CoV‐2 Mpro protein, and the compounds are made publicly available for further characterization and development by scientific community.

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Abstract: The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3âbillion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.

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393 citations

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References

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Journal Article•DOI•

Optimization by Simulated Annealing

[...]

Scott Kirkpatrick¹, C. D. Gelatt¹, Mario P. Vecchi²•Institutions (2)

IBM¹, Venezuelan Institute for Scientific Research²

13 May 1983-Science

TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.

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Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

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41,772 citations

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Helen M. Berman¹, John D. Westbrook, Zukang Feng, Gary L. Gilliland, Talapady N. Bhat, Helge Weissig, Ilya N. Shindyalov, Philip E. Bourne - Show less +4 more•Institutions (1)

Rutgers University¹

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TL;DR: The goals of the PDB are described, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource are described.

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Abstract: The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

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34,239 citations

"Software for molecular docking: a r..." refers methods in this paper

...Furthermore, in 2004, eight docking programs were evaluated with 100 protein–ligand complexes (Paul and Rognan 2002) from the Protein Data Bank (PDB) (Berman et al. 2002)....
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...Previous modeling studies on protein–DNA and protein– RNA complexes using NMR data have been shown to be successful (Gu et al. 2015; Bursulaya et al. 2003; Paul and Rognan 2002; Berman et al. 2002)....
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...The docking accuracy of Glide was assessed by redocking ligands from 282 cocrystallized PDB complexes, while GOLD was validated on 100 and 305 complexes (Friesner et al. 2004; Jones et al. 1997)....
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...Furthermore, seven commonly used programs were evaluated on the PDBbind database with 1300 protein complexes (Plewczynski et al. 2011)....
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Journal Article•DOI•

AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

[...]

Oleg Trott¹, Arthur J. Olson¹•Institutions (1)

Scripps Research Institute¹

04 Jun 2009-Journal of Computational Chemistry

TL;DR: AutoDock Vina achieves an approximately two orders of magnitude speed‐up compared with the molecular docking software previously developed in the lab, while also significantly improving the accuracy of the binding mode predictions, judging by tests on the training set used in AutoDock 4 development.

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Abstract: AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user.

...read moreread less

20,059 citations

"Software for molecular docking: a r..." refers background or methods in this paper

...2012), LeDock (Zhao and Caflisch 2013), AutoDock Vina (Trott and Olson 2010), rDock (Ruiz-Carmona et al....
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...Recently, a newly designed and implemented version of the AutoDock program called AutoDock Vina has been released....
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...structurally known protein–ligand complexes with experimentally determined binding constants (Österberg et al. 2002; Trott and Olson 2010)....
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...Both AutoDock and AutoDock Vina were calibrated using the same test set of 30 structurally known protein–ligand complexes with experimentally determined binding constants (Österberg et al. 2002; Trott and Olson 2010)....
[...]
...…Glide (Friesner et al. 2004), Cdocker, LigandFit (Venkatachalam et al. 2003), MCDock, FRED (McGann et al. 2003), MOE-Dock (Corbeil et al. 2012), LeDock (Zhao and Caflisch 2013), AutoDock Vina (Trott and Olson 2010), rDock (Ruiz-Carmona et al. 2014), UCSF Dock (Allen et al. 2015), and many others....
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Journal Article•DOI•

CHARMM: the biomolecular simulation program.

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Bernard R. Brooks¹, Charles L. Brooks², Alexander D. MacKerell³, Lennart Nilsson⁴, Robert J. Petrella⁵, Benoît Roux⁶, Youngdo Won⁷, Georgios Archontis¹, Christian Bartels¹, Stefan Boresch¹, Amedeo Caflisch¹, Leo S. D. Caves¹, Qiang Cui¹, Aaron R. Dinner¹, Michael Feig¹, Stefan Fischer¹, Jiali Gao¹, Milan Hodošček¹, Wonpil Im¹, K. Kuczera¹, Themis Lazaridis¹, Jianpeng Ma¹, V. Ovchinnikov¹, Emanuele Paci¹, Richard W. Pastor¹, Carol Beth Post¹, Jingzhi Pu¹, M. Schaefer¹, Bruce Tidor¹, Richard M. Venable¹, H. L. Woodcock¹, Xiongwu Wu¹, Wei Yang¹, Darrin M. York¹, Martin Karplus⁸, Martin Karplus⁵ - Show less +32 more•Institutions (8)

National Institutes of Health¹, University of Michigan², University of Maryland, Baltimore³, Karolinska Institutet⁴, Harvard University⁵, University of Chicago⁶, Hanyang University⁷, University of Strasbourg⁸

30 Jul 2009-Journal of Computational Chemistry

TL;DR: An overview of the CHARMM program as it exists today is provided with an emphasis on developments since the publication of the original CHARMM article in 1983.

...read moreread less

Abstract: CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecu- lar simulation program. It has been developed over the last three decades with a primary focus on molecules of bio- logical interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estima- tors, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numer- ous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983.

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7,035 citations

"Software for molecular docking: a r..." refers background or methods in this paper

...molecular-mechanics energy function such as in the CHARMM package yields only limited improvement (Brooks et al. 2009)....
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...Introducing local minimization of a molecular-mechanics energy function such as in the CHARMM package yields only limited improvement (Brooks et al. 2009)....
[...]

Journal Article•DOI•

Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

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Richard A. Friesner¹, Jay L. Banks¹, Robert B. Murphy¹, Thomas A. Halgren¹, Jasna Klicic¹, Daniel T. Mainz¹, Matthew P. Repasky¹, Eric H. Knoll¹, Mee Shelley¹, Jason K. Perry¹, David E. Shaw¹, Perry Francis¹, Peter S Shenkin¹ - Show less +9 more•Institutions (1)

D. E. Shaw Research¹

27 Feb 2004-Journal of Medicinal Chemistry

TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.

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Abstract: Unlike other methods for docking ligands to the rigid 3D structure of a known protein receptor, Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand In this search, an initial rough positioning and scoring phase that dramatically narrows the search space is followed by torsionally flexible energy optimization on an OPLS-AA nonbonded potential grid for a few hundred surviving candidate poses The very best candidates are further refined via a Monte Carlo sampling of pose conformation; in some cases, this is crucial to obtaining an accurate docked pose Selection of the best docked pose uses a model energy function that combines empirical and force-field-based terms Docking accuracy is assessed by redocking ligands from 282 cocrystallized PDB complexes starting from conformationally optimized ligand geometries that bear no memory of the correctly docked pose Errors in geometry for the top-ranked pose are less than 1 A in nearly ha

...read moreread less

6,828 citations

"Software for molecular docking: a r..." refers methods in this paper

...2003), Glide (Friesner et al. 2004), Cdocker, LigandFit (Venkatachalam et al....
[...]
...The docking accuracy of Glide was assessed by redocking ligands from 282 cocrystallized PDB complexes, while GOLD was validated on 100 and 305 complexes (Friesner et al. 2004; Jones et al. 1997)....
[...]
...The best possible conformation is further refined using Monte Carlo sampling (Friesner et al. 2004)....
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...…2003) AutoDock (Österberg et al. 2002), FlexX (Rarey et al. 1996), Surflex (Jain 2003), GOLD (Jones et al. 1997), ICM (Schapira et al. 2003), Glide (Friesner et al. 2004), Cdocker, LigandFit (Venkatachalam et al. 2003), MCDock, FRED (McGann et al. 2003), MOE-Dock (Corbeil et al. 2012), LeDock…...
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