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Sykora VJ. Automated Virtual Screening. Methods Mol Biol 2024; 2716:137-152. [PMID: 37702938 DOI: 10.1007/978-1-0716-3449-3_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Computational methods in modern drug discovery have become ubiquitous, with methods that cover most of the discovery stages: from hit finding and lead identification to lead optimization. The overall aim of these computational methods is to obtain a more efficient discovery process, by reducing the number of "wet" experiments required to produce therapeutics that have higher probability of succeeding in clinical development and subsequently benefitting end patients by developing highly effective therapeutics having minimal side effects. Virtual Screening is usually applied at the early stage of drug discovery, looking to find chemical matter having desired properties, such as molecular shape, electrostatics, and pharmacophores at desired three-dimensional positions. The aim of this stage is to search in a wide chemical space, including chemistry available from commercial suppliers and virtual databases of predicted reaction products, to identify molecules that would exert a particular biochemical response. This initial stage of the discovery process is very important since the subsequent stages will use the initial chemical motifs that have been found at the hit finding stage, and therefore the most suitable the compound is found, the more likely it is that subsequent stages will be successful and less time and resource consuming. This chapter provides a summary of various Virtual Screening methods, including shape match and molecular docking, and these methods are used in a Virtual Screening workflow that is provided as an example which is described to be run automatically in cloud resources. This automatic in-depth exploration of the chemical space using validated Virtual Screening methods can lead to a more streamlined and efficient discovery process, aiming to deliver chemical matter of high quality and maximizing the required biological effects while minimizing adverse effects. Surely, Virtual Screening pipelines of this nature will continue to play a central role in producing much needed therapeutics for the health challenges of the future.
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2
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Zidar N, Tomašič T, Kikelj D, Durcik M, Tytgat J, Peigneur S, Rogers M, Haworth A, Kirby RW. New aryl and acylsulfonamides as state-dependent inhibitors of Na v1.3 voltage-gated sodium channel. Eur J Med Chem 2023; 258:115530. [PMID: 37329714 DOI: 10.1016/j.ejmech.2023.115530] [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: 09/26/2022] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/19/2023]
Abstract
Voltage-gated sodium channels (Navs) play an essential role in neurotransmission, and their dysfunction is often a cause of various neurological disorders. The Nav1.3 isoform is found in the CNS and upregulated after injury in the periphery, but its role in human physiology has not yet been fully elucidated. Reports suggest that selective Nav1.3 inhibitors could be used as novel therapeutics to treat pain or neurodevelopmental disorders. Few selective inhibitors of this channel are known in the literature. In this work, we report the discovery of a new series of aryl and acylsulfonamides as state-dependent inhibitors of Nav1.3 channels. Using a ligand-based 3D similarity search and subsequent hit optimization, we identified and prepared a series of 47 novel compounds and tested them on Nav1.3, Nav1.5, and a selected subset also on Nav1.7 channels in a QPatch patch-clamp electrophysiology assay. Eight compounds had an IC50 value of less than 1 μM against the Nav1.3 channel inactivated state, with one compound displaying an IC50 value of 20 nM, whereas activity against the inactivated state of the Nav1.5 channel and Nav1.7 channel was approximately 20-fold weaker. None of the compounds showed use-dependent inhibition of the cardiac isoform Nav1.5 at a concentration of 30 μM. Further selectivity testing of the most promising hits was measured using the two-electrode voltage-clamp method against the closed state of the Nav1.1-Nav1.8 channels, and compound 15b displayed small, yet selective, effects against the Nav1.3 channel, with no activity against the other isoforms. Additional selectivity testing of promising hits against the inactivated state of the Nav1.3, Nav1.7, and Nav1.8 channels revealed several compounds with robust and selective activity against the inactivated state of the Nav1.3 channel among the three isoforms tested. Moreover, the compounds were not cytotoxic at a concentration of 50 μM, as demonstrated by the assay in human HepG2 cells (hepatocellular carcinoma cells). The novel state-dependent inhibitors of Nav1.3 discovered in this work provide a valuable tool to better evaluate this channel as a potential drug target.
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Affiliation(s)
- Nace Zidar
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia.
| | - Tihomir Tomašič
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - Danijel Kikelj
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - Martina Durcik
- University of Ljubljana, Faculty of Pharmacy, Aškerčeva cesta 7, 1000, Ljubljana, Slovenia
| | - Jan Tytgat
- University of Leuven (KU Leuven), Toxicology & Pharmacology, O&N2, PO Box 922, Herestraat 49, 3000, Leuven, Belgium
| | - Steve Peigneur
- University of Leuven (KU Leuven), Toxicology & Pharmacology, O&N2, PO Box 922, Herestraat 49, 3000, Leuven, Belgium
| | - Marc Rogers
- Metrion Biosciences Limited, Building 2, Granta Centre, Granta Park, Great Abington, Cambridge, CB21 6AL, UK
| | - Alexander Haworth
- Metrion Biosciences Limited, Building 2, Granta Centre, Granta Park, Great Abington, Cambridge, CB21 6AL, UK
| | - Robert W Kirby
- Metrion Biosciences Limited, Building 2, Granta Centre, Granta Park, Great Abington, Cambridge, CB21 6AL, UK
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3
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Hsieh CJ, Giannakoulias S, Petersson EJ, Mach RH. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development. Pharmaceuticals (Basel) 2023; 16:317. [PMID: 37259459 PMCID: PMC9964981 DOI: 10.3390/ph16020317] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 11/19/2023] Open
Abstract
The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).
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Affiliation(s)
- Chia-Ju Hsieh
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sam Giannakoulias
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - E. James Petersson
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Robert H. Mach
- Division of Nuclear Medicine and Clinical Molecular Imaging, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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4
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García JS, Puertas-Martín S, Redondo JL, Moreno JJ, Ortigosa PM. Improving drug discovery through parallelism. THE JOURNAL OF SUPERCOMPUTING 2023; 79:9538-9557. [PMID: 36687309 PMCID: PMC9842220 DOI: 10.1007/s11227-022-05014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.
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Affiliation(s)
- Jerónimo S. García
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Savíns Puertas-Martín
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
- Information School, University of Sheffield, 221, Portobello Street, Sheffield, S1 4DP United Kingdom
| | - Juana L. Redondo
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Juan José Moreno
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
| | - Pilar M. Ortigosa
- Supercomputing - Algorithms Research Group (SAL), Agrifood Campus of International Excellence, University of Almería, Carretera Sacramento s/n, La Cañada de San Urbano, 04120 Almería, Spain
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5
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Puls K, Wolber G. Solving an Old Puzzle: Elucidation and Evaluation of the Binding Mode of Salvinorin A at the Kappa Opioid Receptor. Molecules 2023; 28:molecules28020718. [PMID: 36677775 PMCID: PMC9861206 DOI: 10.3390/molecules28020718] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 01/13/2023] Open
Abstract
The natural product Salvinorin A (SalA) was the first nitrogen-lacking agonist discovered for the opioid receptors and exhibits high selectivity for the kappa opioid receptor (KOR) turning SalA into a promising analgesic to overcome the current opioid crisis. Since SalA's suffers from poor pharmacokinetic properties, particularly the absence of gastrointestinal bioavailability, fast metabolic inactivation, and subsequent short duration of action, the rational design of new tailored analogs with improved clinical usability is highly desired. Despite being known for decades, the binding mode of SalA within the KOR remains elusive as several conflicting binding modes of SalA were proposed hindering the rational design of new analgesics. In this study, we rationally determined the binding mode of SalA to the active state KOR by in silico experiments (docking, molecular dynamics simulations, dynophores) in the context of all available mutagenesis studies and structure-activity relationship (SAR) data. To the best of our knowledge, this is the first comprehensive evaluation of SalA's binding mode since the determination of the active state KOR crystal structure. SalA binds above the morphinan binding site with its furan pointing toward the intracellular core while the C2-acetoxy group is oriented toward the extracellular loop 2 (ECL2). SalA is solely stabilized within the binding pocket by hydrogen bonds (C210ECL2, Y3127.35, Y3137.36) and hydrophobic contacts (V1182.63, I1393.33, I2946.55, I3167.39). With the disruption of this interaction pattern or the establishment of additional interactions within the binding site, we were able to rationalize the experimental data for selected analogs. We surmise the C2-substituent interactions as important for SalA and its analogs to be experimentally active, albeit with moderate frequency within MD simulations of SalA. We further identified the non-conserved residues 2.63, 7.35, and 7.36 responsible for the KOR subtype selectivity of SalA. We are confident that the elucidation of the SalA binding mode will promote the understanding of KOR activation and facilitate the development of novel analgesics that are urgently needed.
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6
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Hönig SMN, Lemmen C, Rarey M. Small molecule superposition: A comprehensive overview on pose scoring of the latest methods. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Sophia M. N. Hönig
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
- BioSolveIT Sankt Augustin Germany
| | | | - Matthias Rarey
- ZBH ‐ Center for Bioinformatics Universität Hamburg Hamburg Germany
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7
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Rana MM, Nguyen DD. EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction. J Chem Inf Model 2022; 62:4329-4341. [DOI: 10.1021/acs.jcim.2c00697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Md Masud Rana
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Duc Duy Nguyen
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
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8
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Grebner C, Matter H, Hessler G. Artificial Intelligence in Compound Design. Methods Mol Biol 2021; 2390:349-382. [PMID: 34731477 DOI: 10.1007/978-1-0716-1787-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.
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Affiliation(s)
- Christoph Grebner
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Hans Matter
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany
| | - Gerhard Hessler
- Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany.
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9
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Permann C, Seidel T, Langer T. Greedy 3-Point Search (G3PS)-A Novel Algorithm for Pharmacophore Alignment. Molecules 2021; 26:7201. [PMID: 34885781 PMCID: PMC8658842 DOI: 10.3390/molecules26237201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022] Open
Abstract
Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.
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Affiliation(s)
- Christian Permann
- Department of Pharmaceutical Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria; (C.P.); (T.L.)
- Inte:Ligand GmbH, Clemens Maria Hofbauer-Gasse 6, 2344 Maria Enzersdorf, Austria
| | - Thomas Seidel
- Department of Pharmaceutical Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria; (C.P.); (T.L.)
| | - Thierry Langer
- Department of Pharmaceutical Sciences, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria; (C.P.); (T.L.)
- Inte:Ligand GmbH, Clemens Maria Hofbauer-Gasse 6, 2344 Maria Enzersdorf, Austria
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10
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Wei H, Zhao Z, Luo R. Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations. J Chem Theory Comput 2021; 17:6214-6224. [PMID: 34516109 DOI: 10.1021/acs.jctc.1c00492] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Implicit solvent models, such as Poisson-Boltzmann models, play important roles in computational studies of biomolecules. A vital step in almost all implicit solvent models is to determine the solvent-solute interface, and the solvent excluded surface (SES) is the most widely used interface definition in these models. However, classical algorithms used for computing SES are geometry-based, so that they are neither suitable for parallel implementations nor convenient for obtaining surface derivatives. To address the limitations, we explored a machine learning strategy to obtain a level set formulation for the SES. The training process was conducted in three steps, eventually leading to a model with over 95% agreement with the classical SES. Visualization of tested molecular surfaces shows that the machine-learned SES overlaps with the classical SES in almost all situations. Further analyses show that the machine-learned SES is incredibly stable in terms of rotational variation of tested molecules. Our timing analysis shows that the machine-learned SES is roughly 2.5 times as efficient as the classical SES routine implemented in Amber/PBSA on a tested central processing unit (CPU) platform. We expect further performance gain on massively parallel platforms such as graphics processing units (GPUs) given the ease in converting the machine-learned SES to a parallel procedure. We also implemented the machine-learned SES into the Amber/PBSA program to study its performance on reaction field energy calculation. The analysis shows that the two sets of reaction field energies are highly consistent with a 1% deviation on average. Given its level set formulation, we expect the machine-learned SES to be applied in molecular simulations that require either surface derivatives or high efficiency on parallel computing platforms.
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Affiliation(s)
- Haixin Wei
- Departments of Materials Science and Engineering, Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California, Irvine, California 92697, United States
| | - Zekai Zhao
- Departments of Materials Science and Engineering, Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California, Irvine, California 92697, United States
| | - Ray Luo
- Departments of Materials Science and Engineering, Molecular Biology and Biochemistry, Chemical and Biomolecular Engineering, and Biomedical Engineering, Graduate Program in Chemical and Materials Physics, University of California, Irvine, California 92697, United States
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11
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Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Finding genes and proteins that interact with a given compound Basic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity search Basic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity search Support Protocol: Computing similarity scores between compounds Basic Protocol 4: Getting the bioactivity data for the hit compounds from substructure search Basic Protocol 5: Finding drugs that target a particular gene Basic Protocol 6: Getting bioactivity data of all chemicals tested against a protein. Basic Protocol 7: Finding compounds annotated with classifications or ontological terms Basic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
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12
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Fatiha Muhammad E, Kumar A, Wahab HA, Zhang KYJ. Identification of 1,2,4-Triazolylthioethanone Scaffold for the Design of New Acetylcholinesterase Inhibitors. Mol Inform 2021; 40:e2100020. [PMID: 34060234 DOI: 10.1002/minf.202100020] [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: 01/22/2021] [Accepted: 05/02/2021] [Indexed: 11/10/2022]
Abstract
Acetylcholinesterase (AChE) inhibitors are the most effective drugs for Alzheimer's disease treatment. However, considering the potential and failure rates of AChE inhibitors, chemical scaffolds targeting cholinesterase specifically are still very limited. Herein, we report a new class of AChE inhibitors identified by employing a virtual screening approach that combines shape similarity with molecular docking calculations. Virtual screening followed by the evaluation of AChE inhibitory activity allowed us to identify 1,2,4-triazolylthioethanones as a novel class of AChE inhibitors. Thirteen compounds with 1,2,4-triazolylthiothanone core and IC50 values in the range of 0.15±0.07 to 3.32±0.92 μM have been reported here. Our findings shed light into a class of AChE inhibitors that could be useful starting point for the development of novel therapeutics to tackle Alzheimer's disease.
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Affiliation(s)
- Erma Fatiha Muhammad
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan.,School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Ashutosh Kumar
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
| | - Habibah A Wahab
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa, 230-0045, Japan
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13
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Wang S, Alexov E, Zhao S. On regularization of charge singularities in solving the Poisson-Boltzmann equation with a smooth solute-solvent boundary. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:1370-1405. [PMID: 33757190 PMCID: PMC9871984 DOI: 10.3934/mbe.2021072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Numerical treatment of singular charges is a grand challenge in solving the Poisson-Boltzmann (PB) equation for analyzing electrostatic interactions between the solute biomolecules and the surrounding solvent with ions. For diffuse interface PB models in which solute and solvent are separated by a smooth boundary, no effective algorithm for singular charges has been developed, because the fundamental solution with a space dependent dielectric function is intractable. In this work, a novel regularization formulation is proposed to capture the singularity analytically, which is the first of its kind for diffuse interface PB models. The success lies in a dual decomposition - besides decomposing the potential into Coulomb and reaction field components, the dielectric function is also split into a constant base plus space changing part. Using the constant dielectric base, the Coulomb potential is represented analytically via Green's functions. After removing the singularity, the reaction field potential satisfies a regularized PB equation with a smooth source. To validate the proposed regularization, a Gaussian convolution surface (GCS) is also introduced, which efficiently generates a diffuse interface for three-dimensional realistic biomolecules. The performance of the proposed regularization is examined by considering both analytical and GCS diffuse interfaces, and compared with the trilinear method. Moreover, the proposed GCS-regularization algorithm is validated by calculating electrostatic free energies for a set of proteins and by estimating salt affinities for seven protein complexes. The results are consistent with experimental data and estimates of sharp interface PB models.
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Affiliation(s)
- Siwen Wang
- Department of Mathematics, University of Alabama, Tuscaloosa, AL 35487,USA
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
| | - Shan Zhao
- Department of Mathematics, University of Alabama, Tuscaloosa, AL 35487,USA
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14
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Jain AN, Cleves AE, Brueckner AC, Lesburg CA, Deng Q, Sherer EC, Reibarkh MY. XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X-ray Electron Density Maps. J Med Chem 2020; 63:10509-10528. [DOI: 10.1021/acs.jmedchem.0c01373] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Ajay N. Jain
- Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143 United States
| | - Ann E. Cleves
- BioPharmics LLC, Santa Rosa, California 95404 United States
| | | | | | - Qiaolin Deng
- Merck and Co., Inc., Kenilworth, New Jersey 07033 United States
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15
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Muraoka K, Chaikittisilp W, Okubo T. Multi-objective de novo molecular design of organic structure-directing agents for zeolites using nature-inspired ant colony optimization. Chem Sci 2020; 11:8214-8223. [PMID: 34094176 PMCID: PMC8163217 DOI: 10.1039/d0sc03075a] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Organic structure-directing agents (OSDAs) are often employed for synthesis of zeolites with desired frameworks. A priori prediction of such OSDAs has mainly relied on the interaction energies between OSDAs and zeolite frameworks, without cost considerations. For practical purposes, the cost of OSDAs becomes a critical issue. Therefore, the development of a computational de novo prediction methodology that can speed up the trial-and-error cycle in the search for less expensive OSDAs is desired. This study utilized a nature-inspired ant colony optimization method to predict physicochemically and/or economically preferable OSDAs, while also taking molecular similarity and heuristics of zeolite synthesis into consideration. The prediction results included experimentally known OSDAs, candidates having structures closely related to known OSDAs, and novel ones, suggesting the applicability of this approach. Inspired by the exploratory methods of ant colonies, adaptive optimization was employed to explore the chemical space for organic molecules that guide zeolite crystallization, giving both physicochemically and economically promising molecules.![]()
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Affiliation(s)
- Koki Muraoka
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
| | - Watcharop Chaikittisilp
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
| | - Tatsuya Okubo
- Department of Chemical System Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo 113-8656 Japan
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16
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Vassetti D, Civalleri B, Labat F. Analytical calculation of the solvent-accessible surface area and its nuclear gradients by stereographic projection: A general approach for molecules, polymers, nanotubes, helices, and surfaces. J Comput Chem 2020; 41:1464-1479. [PMID: 32212337 DOI: 10.1002/jcc.26191] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/07/2020] [Accepted: 03/09/2020] [Indexed: 01/19/2023]
Abstract
In this article, we explore an alternative to the analytical Gauss-Bonnet approach for computing the solvent-accessible surface area (SASA) and its nuclear gradients. These two key quantities are required to evaluate the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models. We extend a previously proposed analytical approach for finite systems based on the stereographic projection technique to infinite periodic systems such as polymers, nanotubes, helices, or surfaces and detail its implementation in the Crystal code. We provide the full derivation of the SASA nuclear gradients, and introduce an iterative perturbation scheme of the atomic coordinates to stabilize the gradients calculation for certain difficult symmetric systems. An excellent agreement of computed SASA with reference analytical values is found for finite systems, while the SASA size-extensivity is verified for infinite periodic systems. In addition, correctness of the analytical gradients is confirmed by the excellent agreement obtained with numerical gradients and by the translational invariance achieved, both for finite and infinite periodic systems. Overall therefore, the stereographic projection approach appears as a general, simple, and efficient technique to compute the key quantities required for the calculation of the nonelectrostatic contribution to the solvation energy and its nuclear gradients in implicit solvation models applicable to both finite and infinite periodic systems.
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Affiliation(s)
- Dario Vassetti
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences, Chemical Theory and Modelling Group, F-75005 Paris, France
| | - Bartolomeo Civalleri
- Department of Chemistry, NIS and INSTM Reference Centre, University of Turin, Via P. Giuria 7, I-10125 Torino, Italy
| | - Frédéric Labat
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences, Chemical Theory and Modelling Group, F-75005 Paris, France
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17
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Design and characterization of a novel structural class of Kv1.3 inhibitors. Bioorg Chem 2020; 98:103746. [PMID: 32199306 DOI: 10.1016/j.bioorg.2020.103746] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/11/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022]
Abstract
The voltage-gated potassium channel Kv1.3 is involved in multiple autoimmune diseases, such as multiple sclerosis, rheumatoid arthritis, diabetes mellitus type 1 and psoriasis. In many auto-immune diseases better treatment options are desired as existing therapies are often ineffective or become less effective over time, for which Kv1.3 inhibitors arise as promising candidates. In this study, five compounds were selected based on a 3D similarity searching methodology and subsequently screened ex vivo on the Kv1.3 channel. The screening resulted in two compounds inhibiting the Kv1.3 channel, of which TVS-12 was the most potent compound, while TVS-06 -although less potent- showed an excellent selectivity for Kv1.3. For both compounds the mechanism of action was investigated by an electrophysiological characterization on the Kv1.3 channel and three Kv1.3 mutants, designed to resemble the pore region of Kv1.2 channels. Structurally, the presence of a benzene ring and/or an oxane ring seems to cause a better interaction with the Kv1.3 channel, resulting in a 20-fold higher potency for TVS-12.
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18
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Bonanno E, Ebejer JP. Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening. Front Pharmacol 2020; 10:1675. [PMID: 32140104 PMCID: PMC7042174 DOI: 10.3389/fphar.2019.01675] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 12/23/2019] [Indexed: 11/13/2022] Open
Abstract
Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known a priori. Our results indicate significant performance gains over a state of the art USR-derived method, ElectroShape 5D, with GMMs obtaining a mean performance up to 430% better than that of ElectroShape 5D in terms of Enrichment Factor with a maximum improvement of up to 940%. Additionally, we demonstrate that our models are capable of maintaining their performance, in terms of enrichment factor, within 10% of the mean as the size of the training dataset is successively reduced. Furthermore, we also demonstrate that running times for retrospective screening using the machine learning models we selected are faster than standard USR, on average by a factor of 10, including the time required for training. Our results show that machine learning techniques can significantly improve the virtual screening performance and efficiency of the USR family of methods.
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Affiliation(s)
- Etienne Bonanno
- Department of Artificial Intelligence, University of Malta, Msida, Malta
| | - Jean-Paul Ebejer
- Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta
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19
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Katigbak J, Li H, Rooklin D, Zhang Y. AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters. J Chem Inf Model 2020; 60:1494-1508. [PMID: 31995373 DOI: 10.1021/acs.jcim.9b00652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.
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Affiliation(s)
- Joseph Katigbak
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Haotian Li
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - David Rooklin
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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20
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21
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A super-Gaussian Poisson-Boltzmann model for electrostatic free energy calculation: smooth dielectric distribution for protein cavities and in both water and vacuum states. J Math Biol 2019; 79:631-672. [PMID: 31030299 PMCID: PMC9841320 DOI: 10.1007/s00285-019-01372-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 12/16/2018] [Indexed: 01/18/2023]
Abstract
Calculations of electrostatic potential and solvation free energy of macromolecules are essential for understanding the mechanism of many biological processes. In the classical implicit solvent Poisson-Boltzmann (PB) model, the macromolecule and water are modeled as two-dielectric media with a sharp border. However, the dielectric property of interior cavities and ion-channels is difficult to model realistically in a two-dielectric setting. In fact, the detection of water molecules in a protein cavity remains to be an experimental challenge. This introduces an uncertainty, which affects the subsequent solvation free energy calculation. In order to compensate this uncertainty, a novel super-Gaussian dielectric PB model is introduced in this work, which devices an inhomogeneous dielectric distribution to represent the compactness of atoms and characterizes empty cavities via a gap dielectric value. Moreover, the minimal molecular surface level set function is adopted so that the dielectric profile remains to be smooth when the protein is transferred from water phase to vacuum. An important feature of this new model is that as the order of super-Gaussian function approaches the infinity, the dielectric distribution reduces to a piecewise constant of the two-dielectric model. Mathematically, an effective dielectric constant analysis is introduced in this work to benchmark the dielectric model and select optimal parameter values. Computationally, a pseudo-time alternative direction implicit (ADI) algorithm is utilized for solving the super-Gaussian PB equation, which is found to be unconditionally stable in a smooth dielectric setting. Solvation free energy calculation of a Kirkwood sphere and various proteins is carried out to validate the super-Gaussian model and ADI algorithm. One macromolecule with both water filled and empty cavities is employed to demonstrate how the cavity uncertainty in protein structure can be bypassed through dielectric modeling in biomolecular electrostatic analysis.
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22
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Abstract
It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.
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Affiliation(s)
- Alexey V Onufriev
- Departments of Computer Science and Physics, Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, Virginia 24060, USA;
| | - David A Case
- Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA;
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23
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Chakravorty A, Gallicchio E, Alexov E. A grid-based algorithm in conjunction with a gaussian-based model of atoms for describing molecular geometry. J Comput Chem 2019; 40:1290-1304. [PMID: 30698861 PMCID: PMC6506848 DOI: 10.1002/jcc.25786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 12/12/2018] [Accepted: 01/06/2019] [Indexed: 11/06/2022]
Abstract
A novel grid-based method is presented, which in conjunction with a smooth Gaussian-based model of atoms, is used to compute molecular volume (MV) and surface area (MSA). The MV and MSA are essential for computing nonpolar component of free energies. The objective of our grid-based approach is to identify solute atom pairs that share overlapping volumes in space. Once completed, this information is used to construct a rooted tree using depth-first method to yield the final volume and SA by using the formulations of the Gaussian model described by Grant and Pickup (J. Phys Chem, 1995, 99, 3503). The method is designed to function uninterruptedly with the grid-based finite-difference method implemented in Delphi, a popular and open-source package used for solving the Poisson-Boltzmann equation (PBE). We demonstrate the time efficacy of the method while also validating its performance in terms of the effect of grid-resolution, positioning of the solute within the grid-map and accuracy in identification of overlapping atom pairs. We also explore and discuss different aspects of the Gaussian model with key emphasis on its physical meaningfulness. This development and its future release with the Delphi package are intended to provide a physically meaningful, fast, robust and comprehensive tool for MM/PBSA based free energy calculations. © 2019 Wiley Periodicals, Inc.
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Affiliation(s)
- Arghya Chakravorty
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634
| | | | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, South Carolina 29634
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24
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Lemurell M, Ulander J, Emtenäs H, Winiwarter S, Broddefalk J, Swanson M, Hayes MA, Prieto Garcia L, Westin Eriksson A, Meuller J, Cassel J, Saarinen G, Yuan ZQ, Löfberg C, Karlsson S, Sundqvist M, Whatling C. Novel Chemical Series of 5-Lipoxygenase-Activating Protein Inhibitors for Treatment of Coronary Artery Disease. J Med Chem 2019; 62:4325-4349. [DOI: 10.1021/acs.jmedchem.8b02012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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25
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Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M, Pujadas G, Garcia-Vallvé S. The Light and Dark Sides of Virtual Screening: What Is There to Know? Int J Mol Sci 2019; 20:E1375. [PMID: 30893780 PMCID: PMC6470506 DOI: 10.3390/ijms20061375] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Revised: 03/11/2019] [Accepted: 03/15/2019] [Indexed: 12/11/2022] Open
Abstract
Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.
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Affiliation(s)
- Aleix Gimeno
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - María José Ojeda-Montes
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - Sarah Tomás-Hernández
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - Adrià Cereto-Massagué
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - Raúl Beltrán-Debón
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - Miquel Mulero
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
| | - Gerard Pujadas
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
- EURECAT, TECNIO, CEICS, Avinguda Universitat, 1, 43204 Reus, Catalonia, Spain.
| | - Santiago Garcia-Vallvé
- Research group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, Campus de Sescelades, 43007 Tarragona, Catalonia, Spain.
- EURECAT, TECNIO, CEICS, Avinguda Universitat, 1, 43204 Reus, Catalonia, Spain.
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26
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Abstract
Virtual screening consists of using computational tools to predict potentially bioactive compounds from files containing large libraries of small molecules. Virtual screening is becoming increasingly popular in the field of drug discovery as in silico techniques are continuously being developed, improved, and made available. As most of these techniques are easy to use, both private and public organizations apply virtual screening methodologies to save resources in the laboratory. However, it is often the case that the techniques implemented in virtual screening workflows are restricted to those that the research team knows. Moreover, although the software is often easy to use, each methodology has a series of drawbacks that should be avoided so that false results or artifacts are not produced. Here, we review the most common methodologies used in virtual screening workflows in order to both introduce the inexperienced researcher to new methodologies and advise the experienced researcher on how to prevent common mistakes and the improper usage of virtual screening methodologies.
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27
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Simões TMC, Gomes AJP. CavVis-A Field-of-View Geometric Algorithm for Protein Cavity Detection. J Chem Inf Model 2019; 59:786-796. [PMID: 30629446 DOI: 10.1021/acs.jcim.8b00572] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Several geometric-based methods have been developed for the last two to three decades to detect and identify cavities (i.e., putative binding sites) on proteins, as needed to study protein-ligand interactions and protein docking. This paper introduces a new protein cavity method, called CavVis, which combines voxelization (i.e., a grid of voxels) and an analytic formulation of Gaussian surfaces that approximates the solvent-excluded surface. This method builds upon visibility of points on protein surface to find its cavities. Specifically, the visibility criterion combines three concepts we borrow from computer graphics, the field-of-view of each surface point, voxel ray casting, and back-face culling.
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Affiliation(s)
- Tiago M C Simões
- Instituto de Telecomunicações , Delegação da Covilhã , 6200-001 Covilhã , Portugal.,Departamento de Informática , Universidade da Beira Interior , 6200-001 Covilhã , Portugal
| | - Abel J P Gomes
- Instituto de Telecomunicações , Delegação da Covilhã , 6200-001 Covilhã , Portugal.,Departamento de Informática , Universidade da Beira Interior , 6200-001 Covilhã , Portugal
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28
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Li X, Yan X, Yang Y, Gu Q, Zhou H, Du Y, Lu Y, Liao J, Xu J. LSA: a local-weighted structural alignment tool for pharmaceutical virtual screening. RSC Adv 2019; 9:3912-3917. [PMID: 35518105 PMCID: PMC9060470 DOI: 10.1039/c8ra08915a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2018] [Accepted: 01/23/2019] [Indexed: 11/21/2022] Open
Abstract
Similar structures having similar activities is a dogma for identifying new functional molecules. However, it is not rare that a minor structural change can cause a significant activity change. Methods to measure the molecular similarity can be classified into two categories of overall three-dimensional shape based methods and local substructure based methods. The former states the relation between overall similarity and activity, and is represented by conventional similarity algorithms. The latter states the relation between local substructure and activity, and is represented by conventional substructure match algorithms. Practically, the similarity of two molecules with similar activity depends on the contributions from both overall similarity and local substructure match. We report a new tool termed as a local-weighted structural alignment (LSA) tool for pharmaceutical virtual screening, which computes the similarity of two molecular structures by considering the contributions of both overall similarity and local substructure match. LSA consists of three steps: (1) mapping a common substructure between two molecular topological structures; (2) superimposing two three-dimensional molecular structures with substructure focus; (3) computing the similarity score based on superimposing. LSA has been validated with 102 testing compound libraries from DUD-E collection with the average AUC (the area under a receiver-operating characteristic curve) value of 0.82 and an average EF1% (the enrichment factor at top 1%) of 27.0, which had consistently better performance than conventional approaches. LSA is implemented in C++ and run on Linux and Windows systems. A local-weighted structural alignment tool by considering the contributions of both overall similarity and local substructure match.![]()
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Affiliation(s)
- Xiuming Li
- Research Center for Drug Discovery
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Xin Yan
- Research Center for Drug Discovery
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Yuedong Yang
- National Supercomputer Center in Guangzhou
- School of Data and Computer Science
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Qiong Gu
- Research Center for Drug Discovery
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Huihao Zhou
- Research Center for Drug Discovery
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Yunfei Du
- National Supercomputer Center in Guangzhou
- School of Data and Computer Science
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Yutong Lu
- National Supercomputer Center in Guangzhou
- School of Data and Computer Science
- Sun Yat-Sen University
- Guangzhou 510006
- China
| | - Jielou Liao
- Department of Chemical Physics
- University of Science and Technology of China
- Hefei 230026
- China
| | - Jun Xu
- Research Center for Drug Discovery
- School of Pharmaceutical Sciences
- Sun Yat-Sen University
- Guangzhou 510006
- China
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29
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Anand DV, Meng Z, Xia K. A complex multiscale virtual particle model based elastic network model (CMVP-ENM) for the normal mode analysis of biomolecular complexes. Phys Chem Chem Phys 2019; 21:4359-4366. [DOI: 10.1039/c8cp07442a] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The CMVP-ENM for virus normal mode analysis. With a special ratio parameter, CMVP-ENM can characterize the multi-material properties of biomolecular complexes and systematically enhance or suppress the modes for different components.
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Affiliation(s)
- D. Vijay Anand
- Division of Mathematical Sciences
- School of Physical and Mathematical Sciences
- Nanyang Technological University
- Singapore
| | - Zhenyu Meng
- School of Biological Sciences
- Nanyang Technological University
- Singapore
| | - Kelin Xia
- Division of Mathematical Sciences
- School of Physical and Mathematical Sciences
- Nanyang Technological University
- Singapore
- School of Biological Sciences
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30
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Kumar A, Zhang KYJ. Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery. Front Chem 2018; 6:315. [PMID: 30090808 PMCID: PMC6068280 DOI: 10.3389/fchem.2018.00315] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022] Open
Abstract
Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.
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Affiliation(s)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
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31
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Schmidt TC, Cosgrove DA, Boström J. ReFlex3D: Refined Flexible Alignment of Molecules Using Shape and Electrostatics. J Chem Inf Model 2018; 58:747-760. [DOI: 10.1021/acs.jcim.7b00618] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Thomas C. Schmidt
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development, AstraZeneca, Pepparedsleden 1, SE 43183 Mölndal, Sweden
| | - David A. Cosgrove
- Cozchemix Limited, 37 Coniston Way, Macclesfield, Cheshire SK11 7XR, United Kingdom
| | - Jonas Boström
- Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development, AstraZeneca, Pepparedsleden 1, SE 43183 Mölndal, Sweden
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32
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Abstract
PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a key chemical information resource, developed and maintained by the US National Institutes of Health. The present chapter describes how to find potential multitarget ligands from PubChem that would be tested in further experiments. While the protocol presented here uses PubChem's Web-based interfaces to allow users to follow it interactively, it can also be implemented in computer software by using programmatic access interfaces to PubChem (such as PUG-REST or E-Utilities).
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33
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34
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Simões T, Lopes D, Dias S, Fernandes F, Pereira J, Jorge J, Bajaj C, Gomes A. Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2017; 36:643-683. [PMID: 29520122 PMCID: PMC5839519 DOI: 10.1111/cgf.13158] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.
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Affiliation(s)
- Tiago Simões
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - Sérgio Dias
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
| | | | - João Pereira
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | - Joaquim Jorge
- INESC-ID Lisboa, Portugal
- Instituto Superior Técnico, Universidade de Lisboa, Portugal
| | | | - Abel Gomes
- Instituto de Telecomunicações, Portugal
- Universidade da Beira Interior, Portugal
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Dias SED, Martins AM, Nguyen QT, Gomes AJP. GPU-based detection of protein cavities using Gaussian surfaces. BMC Bioinformatics 2017; 18:493. [PMID: 29145826 PMCID: PMC5691400 DOI: 10.1186/s12859-017-1913-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2016] [Accepted: 11/01/2017] [Indexed: 11/10/2022] Open
Abstract
Background Protein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity. Results We solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them. Conclusion This article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.
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Affiliation(s)
- Sérgio E D Dias
- Universidade da Beira Interior, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal.,Instituto de Telecomunicações, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal
| | - Ana Mafalda Martins
- Universidade da Beira Interior, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal
| | - Quoc T Nguyen
- Universidade da Beira Interior, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal.,Instituto de Telecomunicações, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal
| | - Abel J P Gomes
- Universidade da Beira Interior, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal. .,Instituto de Telecomunicações, Av. Marques D'Ávila e Bolama, Covilhã, 6200-001, Portugal.
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36
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Taraji M, Haddad PR, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA. Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review. Anal Chim Acta 2017; 1000:20-40. [PMID: 29289311 DOI: 10.1016/j.aca.2017.09.041] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 09/22/2017] [Accepted: 09/24/2017] [Indexed: 02/09/2023]
Abstract
With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.
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Affiliation(s)
- Maryam Taraji
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Paul R Haddad
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia.
| | - Ruth I J Amos
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Roman Szucs
- Pfizer Global Research and Development, CT13 9NJ, Sandwich, UK
| | - John W Dolan
- LC Resources, 1795 NW Wallace Rd., McMinnville, OR 97128, USA
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37
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Lagos CF, Segovia GF, Nuñez-Navarro N, Faúndez MA, Zacconi FC. Novel FXa Inhibitor Identification through Integration of Ligand- and Structure-Based Approaches. Molecules 2017; 22:molecules22101588. [PMID: 28937618 PMCID: PMC6151700 DOI: 10.3390/molecules22101588] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Revised: 09/15/2017] [Accepted: 09/18/2017] [Indexed: 12/31/2022] Open
Abstract
Factor Xa (FXa), a vitamin K-dependent serine protease plays a pivotal role in the coagulation cascade, one of the most interesting targets for the development of new anticoagulants. In the present work, we performed a virtual screening campaign based on ligand-based shape and electrostatic similarity search and protein-ligand docking to discover novel FXa-targeted scaffolds for further development of inhibitors. From an initial set of 260,000 compounds from the NCI Open database, 30 potential FXa inhibitors were identified and selected for in vitro biological evaluation. Compound 5 (NSC635393, 4-(3-methyl-4H-1,4-benzothiazin-2-yl)-2,4-dioxo-N-phenylbutanamide) displayed an IC50 value of 2.02 nM against human FXa. The identified compound may serve as starting point for the development of novel FXa inhibitors.
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Affiliation(s)
- Carlos F Lagos
- Department of Endocrinology, School of Medicine, Pontificia Universidad Católica de Chile, Lira 85, Santiago 8330074, Chile.
- Facultad de Ciencia, Universidad San Sebastián, Campus Los Leones, Lota 2465, Providencia, Santiago 7510157, Chile.
| | - Gerardine F Segovia
- Departamento de Química Orgánica, Facultad de Química, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
| | - Nicolás Nuñez-Navarro
- Departamento de Química Orgánica, Facultad de Química, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
| | - Mario A Faúndez
- Departamento de Farmacia, Facultad de Química, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
| | - Flavia C Zacconi
- Departamento de Química Orgánica, Facultad de Química, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
- Centro de Investigación en Nanotecnología y Materiales Avanzados, CIEN-UC, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile.
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38
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Schmidt TC, Eriksson PO, Gustafsson D, Cosgrove D, Frølund B, Boström J. Discovery and Evaluation of Anti-Fibrinolytic Plasmin Inhibitors Derived from 5-(4-Piperidyl)isoxazol-3-ol (4-PIOL). J Chem Inf Model 2017; 57:1703-1714. [DOI: 10.1021/acs.jcim.7b00255] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Thomas C. Schmidt
- Cardiovascular
and Metabolic Diseases, Innovative Medicines and Early Development, AstraZeneca, Pepparedsleden 1, SE 43183 Mölndal, Sweden
| | - Per-Olof Eriksson
- Structure
and Biophysics, Discovery Science, Innovative Medicines and Early
Development, AstraZeneca, Pepparedsleden 1, SE 43183 Mölndal, Sweden
| | - David Gustafsson
- Emeriti Pharma, AB, AZ Bioventure Hub, Pepparedsleden 1, SE 43183 Mölndal, Sweden
| | - David Cosgrove
- Discovery
Sciences, Chemistry Innovation Centre, Mereside 30S391, Alderley Park, Macclesfield SK10 4TG, United Kingdom
| | - Bente Frølund
- Department
of Drug Design and Pharmacology, University of Copenhagen, DK 2100 Copenhagen, Denmark
| | - Jonas Boström
- Cardiovascular
and Metabolic Diseases, Innovative Medicines and Early Development, AstraZeneca, Pepparedsleden 1, SE 43183 Mölndal, Sweden
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39
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Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen. Sci Rep 2017; 7:45053. [PMID: 28338016 PMCID: PMC5364466 DOI: 10.1038/srep45053] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Accepted: 02/13/2017] [Indexed: 11/08/2022] Open
Abstract
We describe here a superposition free method for comparing the surfaces of antibody binding sites based on the Zernike moments and show that they can be used to quickly compare and cluster sets of antibodies. The clusters provide information about the nature of the bound antigen that, when combined with a method for predicting the number of direct antibody antigen contacts, allows the discrimination between protein and non-protein binding antibodies with an accuracy of 76%. This is of relevance in several aspects of antibody science, for example to select the framework to be used for a combinatorial antibody library.
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40
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Zhang B, Kilburg D, Eastman P, Pande VS, Gallicchio E. Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units. J Comput Chem 2017; 38:740-752. [PMID: 28160511 DOI: 10.1002/jcc.24745] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 01/05/2017] [Accepted: 01/08/2017] [Indexed: 11/07/2022]
Abstract
We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Baofeng Zhang
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210
| | - Denise Kilburg
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
| | - Peter Eastman
- Department of Bioengineering, Stanford University, Stanford, California, 94035
| | - Vijay S Pande
- Department of Chemistry, Stanford University, Stanford, California, 94035
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College of the City University of New York, 2900 Bedford Avenue, Brooklyn, New York, 11210.,Ph.D. Program in Chemistry, The Graduate Center of the City University of New York, New York, New York, 10016
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42
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Abstract
Background Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand. Results To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome. Conclusions We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Mapping binding site space. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users.
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43
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Kim S, Bolton EE, Bryant SH. Similar compounds versus similar conformers: complementarity between PubChem 2-D and 3-D neighboring sets. J Cheminform 2016; 8:62. [PMID: 27872662 PMCID: PMC5097428 DOI: 10.1186/s13321-016-0163-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 09/05/2016] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND PubChem is a public repository for biological activities of small molecules. For the efficient use of its vast amount of chemical information, PubChem performs 2-dimensional (2-D) and 3-dimensional (3-D) neighborings, which precompute "neighbor" relationships between molecules in the PubChem Compound database, using the PubChem subgraph fingerprints-based 2-D similarity and the Gaussian-shape overlay-based 3-D similarity, respectively. These neighborings allow PubChem to provide the user with immediate access to the list of 2-D and 3-D neighbors (also called "Similar Compounds" and "Similar Conformers", respectively) for each compound in PubChem. However, because 3-D neighboring is much more time-consuming than 2-D neighboring, how different the results of the two neighboring schemes are is an important question, considering limited computational resources. RESULTS The present study analyzed the complementarity between the PubChem 2-D and 3-D neighbors. When all compounds in PubChem were considered, the overlap between 2-D and 3-D neighbors was only 2% of the total neighbors. For the data sets containing compounds with annotated information, the overlap increased as the data sets became smaller. However, it did not exceed 31% and substantial fractions of neighbors were still recognized by either PubChem 2-D or 3-D similarity, but not by both. The Neighbor Preference Index (NPI) of a molecule for a given data set was introduced, which quantified whether a molecule had more 2-D or 3-D neighbors in the data set. The NPI histogram for all PubChem compounds had a bimodal shape with two maxima at NPI = ±1 and a minimum at NPI = 0. However, the NPI histograms for the subsets containing compounds with annotated information had a greater fraction of compounds with a strong preference for one neighboring method to the other (at NPI = ±1) as well as compounds with a neutral preference (at NPI = 0). CONCLUSION The results of our study indicate that, for the majority of the compounds in PubChem, their structural similarity to other compounds can be recognized predominantly by either 2-D or 3-D neighborings, but not by both, showing a strong complementarity between 2-D and 3-D neighboring results. Therefore, despite its heavy requirements for computational resources, 3-D neighboring provides an alternative way in which the user can instantly access structurally similar molecules that cannot be detected if only 2-D neighboring is used.Graphical AbstractThe binned distribution of the neighbor preference indices (NPIs) for all compounds in PubChem (left) has a bimodal shape with two maxima at NPI = ±1 and a minimum at NPI = 0, indicating that structural similarity between compounds in PubChem can be recognized predominantly by either 2-D or 3-D neighborings, but not by both. The NPI histogram for the drug space (right) has a greater fraction of compounds with a strong preference for one neighboring method to the other (at NPI ≈ ±1) as well as compounds with a neutral preference (at NPI ≈ 0), indicating that the drug space is very different from the PubChem space.
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Stephen H. Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
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44
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Wang C, Nguyen PH, Pham K, Huynh D, Le TBN, Wang H, Ren P, Luo R. Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis. J Comput Chem 2016; 37:2436-46. [PMID: 27510546 PMCID: PMC5018451 DOI: 10.1002/jcc.24467] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 07/13/2016] [Indexed: 11/07/2022]
Abstract
Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) methods have become widely adopted in estimating protein-ligand binding affinities due to their efficiency and high correlation with experiment. Here different computational alternatives were investigated to assess their impact to the agreement of MMPBSA calculations with experiment. Seven receptor families with both high-quality crystal structures and binding affinities were selected. First the performance of nonpolar solvation models was studied and it was found that the modern approach that separately models hydrophobic and dispersion interactions dramatically reduces RMSD's of computed relative binding affinities. The numerical setup of the Poisson-Boltzmann methods was analyzed next. The data shows that the impact of grid spacing to the quality of MMPBSA calculations is small: the numerical error at the grid spacing of 0.5 Å is already small enough to be negligible. The impact of different atomic radius sets and different molecular surface definitions was further analyzed and weak influences were found on the agreement with experiment. The influence of solute dielectric constant was also analyzed: a higher dielectric constant generally improves the overall agreement with experiment, especially for highly charged binding pockets. The data also showed that the converged simulations caused slight reduction in the agreement with experiment. Finally the direction of estimating absolute binding free energies was briefly explored. Upon correction of the binding-induced rearrangement free energy and the binding entropy lost, the errors in absolute binding affinities were also reduced dramatically when the modern nonpolar solvent model was used, although further developments were apparently necessary to further improve the MMPBSA methods. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Changhao Wang
- Chemical and Materials Physics Graduate Program, Irvine, California, 92697
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697
- Department of Physics and Astronomy, University of California, Irvine, California, 92697
| | - Peter H Nguyen
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697
| | - Kevin Pham
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697
| | - Danielle Huynh
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697
| | | | - Hongli Wang
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697
| | - Pengyu Ren
- Department of Biomedical Engineering, University of Texas, Austin, Texas, 78712
| | - Ray Luo
- Chemical and Materials Physics Graduate Program, Irvine, California, 92697.
- Department of Molecular Biology and Biochemistry, Irvine, California, 92697.
- Department of Chemical Engineering and Materials Science, Irvine, California, 92697.
- Department of Biomedical Engineering, University of California, Irvine, California, 92697.
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45
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Nguyen DD, Wei GW. The impact of surface area, volume, curvature, and Lennard-Jones potential to solvation modeling. J Comput Chem 2016; 38:24-36. [PMID: 27718270 DOI: 10.1002/jcc.24512] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 08/17/2016] [Accepted: 08/30/2016] [Indexed: 12/24/2022]
Abstract
This article explores the impact of surface area, volume, curvature, and Lennard-Jones (LJ) potential on solvation free energy predictions. Rigidity surfaces are utilized to generate robust analytical expressions for maximum, minimum, mean, and Gaussian curvatures of solvent-solute interfaces, and define a generalized Poisson-Boltzmann (GPB) equation with a smooth dielectric profile. Extensive correlation analysis is performed to examine the linear dependence of surface area, surface enclosed volume, maximum curvature, minimum curvature, mean curvature, and Gaussian curvature for solvation modeling. It is found that surface area and surfaces enclosed volumes are highly correlated to each other's, and poorly correlated to various curvatures for six test sets of molecules. Different curvatures are weakly correlated to each other for six test sets of molecules, but are strongly correlated to each other within each test set of molecules. Based on correlation analysis, we construct twenty six nontrivial nonpolar solvation models. Our numerical results reveal that the LJ potential plays a vital role in nonpolar solvation modeling, especially for molecules involving strong van der Waals interactions. It is found that curvatures are at least as important as surface area or surface enclosed volume in nonpolar solvation modeling. In conjugation with the GPB model, various curvature-based nonpolar solvation models are shown to offer some of the best solvation free energy predictions for a wide range of test sets. For example, root mean square errors from a model constituting surface area, volume, mean curvature, and LJ potential are less than 0.42 kcal/mol for all test sets. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Duc D Nguyen
- Department of Mathematics, Michigan State University, Michigan, 48824
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Michigan, 48824.,Department of Electrical and Computer Engineering, Michigan State University, Michigan, 48824.,Department of Biochemistry and Molecular Biology, Michigan State University, Michigan, 48824
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46
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Kearnes S, Pande V. ROCS-derived features for virtual screening. J Comput Aided Mol Des 2016; 30:609-17. [PMID: 27624668 DOI: 10.1007/s10822-016-9959-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 08/31/2016] [Indexed: 10/21/2022]
Abstract
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into color components and color atom overlaps, novel color similarity features that can be weighted in a system-specific manner by machine learning algorithms. In cross-validation experiments, these additional features significantly improve virtual screening performance relative to standard ROCS.
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Affiliation(s)
- Steven Kearnes
- Stanford University, 318 Campus Dr. S296, Stanford, CA, 94305, USA. .,Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA.
| | - Vijay Pande
- Stanford University, 318 Campus Dr. S296, Stanford, CA, 94305, USA
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47
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Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J Chem Inf Model 2016; 56:1399-404. [PMID: 27391578 DOI: 10.1021/acs.jcim.6b00174] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small molecules. Screenable compounds include drugs, bioactive and commercial molecules, as well as 205 million of virtual compounds readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field. SwissSimilarity is accessible free of charge or login at http://www.swisssimilarity.ch .
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Affiliation(s)
- Vincent Zoete
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Antoine Daina
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Christophe Bovigny
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Olivier Michielin
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland.,Department of Oncology, University of Lausanne and Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland
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48
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Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH. PubChem Substance and Compound databases. Nucleic Acids Res 2016; 44:D1202-13. [PMID: 26400175 PMCID: PMC4702940 DOI: 10.1093/nar/gkv951] [Citation(s) in RCA: 2730] [Impact Index Per Article: 341.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 09/10/2015] [Accepted: 09/11/2015] [Indexed: 11/13/2022] Open
Abstract
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.
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Affiliation(s)
- Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Jie Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Gang Fu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Asta Gindulyte
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Lianyi Han
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Jane He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Siqian He
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Benjamin A Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Jiyao Wang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Bo Yu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
| | - Stephen H Bryant
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20894, USA
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49
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Wang B, Wei GW. Parameter optimization in differential geometry based solvation models. J Chem Phys 2015; 143:134119. [PMID: 26450304 PMCID: PMC4602332 DOI: 10.1063/1.4932342] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 09/22/2015] [Indexed: 01/01/2023] Open
Abstract
Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.
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Affiliation(s)
- Bao Wang
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, USA
| | - G W Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, USA
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50
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Wickstrom L, Deng N, He P, Mentes A, Nguyen C, Gilson MK, Kurtzman T, Gallicchio E, Levy RM. Parameterization of an effective potential for protein-ligand binding from host-guest affinity data. J Mol Recognit 2015; 29:10-21. [PMID: 26256816 DOI: 10.1002/jmr.2489] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 06/06/2015] [Accepted: 06/07/2015] [Indexed: 12/13/2022]
Abstract
Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Lauren Wickstrom
- Borough of Manhattan Community College, Department of Science, The City University of New York, New York, NY, 10007, USA
| | - Nanjie Deng
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Peng He
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Ahmet Mentes
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
| | - Crystal Nguyen
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093-0736, USA
| | - Tom Kurtzman
- Department of Chemistry, Lehman College, The City University of New York, Bronx, NY, 10468, USA
| | - Emilio Gallicchio
- Department of Chemistry, Brooklyn College, The City University of New York, Brooklyn, NY, 11210, USA
| | - Ronald M Levy
- Center for Biophysics and Computational Biology/ICMS, Department of Chemistry, Temple University, Philadelphia, PA, 19122, USA
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