1
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Ibrahim PEGF, Zuccotto F, Zachariae U, Gilbert I, Bodkin M. Accurate prediction of dynamic protein-ligand binding using P-score ranking. J Comput Chem 2024; 45:1762-1778. [PMID: 38647338 DOI: 10.1002/jcc.27370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/25/2024]
Abstract
Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.
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Affiliation(s)
- Peter E G F Ibrahim
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Fabio Zuccotto
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ulrich Zachariae
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Ian Gilbert
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
| | - Mike Bodkin
- Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, University of Dundee, Dundee, UK
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2
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Herbst C, Endres S, Würz R, Sotriffer C. Assessment of fragment docking and scoring with the endothiapepsin model system. Arch Pharm (Weinheim) 2024; 357:e2400061. [PMID: 38631672 DOI: 10.1002/ardp.202400061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/23/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024]
Abstract
Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.
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Affiliation(s)
- Carina Herbst
- Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität, Würzburg, Germany
| | - Sara Endres
- Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität, Würzburg, Germany
| | - Rebecca Würz
- Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität, Würzburg, Germany
| | - Christoph Sotriffer
- Institute of Pharmacy and Food Chemistry, Julius-Maximilians-Universität, Würzburg, Germany
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3
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Schmitz B, Frieg B, Homeyer N, Jessen G, Gohlke H. Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin. Arch Pharm (Weinheim) 2024; 357:e2300612. [PMID: 38319801 DOI: 10.1002/ardp.202300612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/18/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024]
Abstract
Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.
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Affiliation(s)
- Birte Schmitz
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Benedikt Frieg
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
| | - Nadine Homeyer
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Gisela Jessen
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Holger Gohlke
- Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC), and Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, Jülich, Germany
- Institute of Bio- and Geosciences (IBG-4: Bioinformatics), Forschungszentrum Jülich, Jülich, Germany
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4
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Sindt F, Seyller A, Eguida M, Rognan D. Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces. ACS CENTRAL SCIENCE 2024; 10:615-627. [PMID: 38559302 PMCID: PMC10979501 DOI: 10.1021/acscentsci.3c01521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/25/2024] [Accepted: 01/29/2024] [Indexed: 04/04/2024]
Abstract
Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.
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Affiliation(s)
- François Sindt
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | - Anthony Seyller
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
| | | | - Didier Rognan
- Laboratoire d’innovation
thérapeutique, UMR7200 CNRS-Université de Strasbourg, Illkirch 67400, France
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5
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Flachsenberg F, Ehrt C, Gutermuth T, Rarey M. Redocking the PDB. J Chem Inf Model 2024; 64:219-237. [PMID: 38108627 DOI: 10.1021/acs.jcim.3c01573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).
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Affiliation(s)
- Florian Flachsenberg
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Christiane Ehrt
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Torben Gutermuth
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
| | - Matthias Rarey
- Universität Hamburg, ZBH - Center for Bioinformatics, Bundesstraße 43, 20146 Hamburg, Germany
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6
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Zimmermann RA, Fischer TR, Schwickert M, Nidoieva Z, Schirmeister T, Kersten C. Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6. Int J Mol Sci 2023; 24:ijms24076109. [PMID: 37047081 PMCID: PMC10094593 DOI: 10.3390/ijms24076109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/20/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023] Open
Abstract
Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m5C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to KD,app = 37 µM and KD,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed.
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Affiliation(s)
- Robert A Zimmermann
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
| | - Tim R Fischer
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
| | - Marvin Schwickert
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
| | - Zarina Nidoieva
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
| | - Tanja Schirmeister
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
| | - Christian Kersten
- Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany
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7
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Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics. Int J Mol Sci 2023; 24:ijms24054401. [PMID: 36901832 PMCID: PMC10003049 DOI: 10.3390/ijms24054401] [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: 01/27/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023] Open
Abstract
Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.
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8
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Eguida M, Schmitt-Valencia C, Hibert M, Villa P, Rognan D. Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking. J Med Chem 2022; 65:13771-13783. [PMID: 36256484 DOI: 10.1021/acs.jmedchem.2c00931] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Christel Schmitt-Valencia
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Marcel Hibert
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
| | - Pascal Villa
- Plateforme de Chimie Biologique Intégrative de Strasbourg, UAR3286 CNRS-Université de Strasbourg, Institut du Médicament de Strasbourg, F-67400Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, F-67400Illkirch, France
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9
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Alibay I, Magarkar A, Seeliger D, Biggin PC. Evaluating the use of absolute binding free energy in the fragment optimisation process. Commun Chem 2022; 5:105. [PMID: 36697714 PMCID: PMC9814858 DOI: 10.1038/s42004-022-00721-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 08/10/2022] [Indexed: 02/01/2023] Open
Abstract
Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.
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Affiliation(s)
- Irfan Alibay
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
| | - Aniket Magarkar
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany
| | - Daniel Seeliger
- grid.420061.10000 0001 2171 7500Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an de Riß, Germany ,Present Address: Exscientia Inc, Office 400E, 2125 Biscayne Blvd, Miami, FL 33137 USA
| | - Philip Charles Biggin
- grid.4991.50000 0004 1936 8948Department of Biochemistry, The University of Oxford, South Parks Road, OX1 3QU Oxford, UK
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10
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Pavan M, Menin S, Bassani D, Sturlese M, Moro S. Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study. Front Mol Biosci 2022; 9:909499. [PMID: 35874609 PMCID: PMC9301033 DOI: 10.3389/fmolb.2022.909499] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/25/2022] [Indexed: 01/23/2023] Open
Abstract
In the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol.
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11
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Hasan MR, Alsaiari AA, Fakhurji BZ, Molla MHR, Asseri AH, Sumon MAA, Park MN, Ahammad F, Kim B. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules 2022; 27:4169. [PMID: 35807415 PMCID: PMC9268380 DOI: 10.3390/molecules27134169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 01/18/2023] Open
Abstract
The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.
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Affiliation(s)
- Md Rifat Hasan
- Department of Mathematics, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Department of Applied Mathematics, Faculty of Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
| | - Ahad Amer Alsaiari
- College of Applied Medical Science, Clinical Laboratories Science Department, Taif University, Taif 21944, Saudi Arabia;
| | - Burhan Zain Fakhurji
- iGene Medical Training and Molecular Research Center, Jeddah 21589, Saudi Arabia;
| | | | - Amer H. Asseri
- Biochemistry Department, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
- Centre for Artificial Intelligence in Precision Medicines, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia
| | - Md Afsar Ahmed Sumon
- Department of Marine Biology, Faculty of Marine Sciences, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Moon Nyeo Park
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
| | - Foysal Ahammad
- Department of Biological Sciences, Faculty of Science, King Abdul-Aziz University, Jeddah 21589, Saudi Arabia;
| | - Bonglee Kim
- College of Korean Medicine, Kyung Hee University, Hoigidong, Dongdaemungu, Seoul 02453, Korea;
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12
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Kell SR, Wang Z, Ji H. Fragment hopping protocol for the design of small-molecule protein-protein interaction inhibitors. Bioorg Med Chem 2022; 69:116879. [PMID: 35749838 DOI: 10.1016/j.bmc.2022.116879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 05/29/2022] [Accepted: 06/08/2022] [Indexed: 11/02/2022]
Abstract
Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein-protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.
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Affiliation(s)
- Shelby R Kell
- Drug Discovery Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, United States; Department of Chemistry, University of South Florida, Tampa, FL 33620, United States
| | - Zhen Wang
- Drug Discovery Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, United States; Department of Chemistry, University of South Florida, Tampa, FL 33620, United States
| | - Haitao Ji
- Drug Discovery Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, United States; Department of Chemistry, University of South Florida, Tampa, FL 33620, United States.
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13
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Mingione VR, Foda Z, Paung Y, Philipose H, Rangwala AM, Shan Y, Seeliger MA. Validation of an allosteric binding site of Src kinase identified by unbiased ligand binding simulations. J Mol Biol 2022; 434:167628. [PMID: 35595169 DOI: 10.1016/j.jmb.2022.167628] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/13/2022] [Accepted: 05/04/2022] [Indexed: 10/18/2022]
Abstract
Allostery plays a primary role in regulating protein activity, making it an important mechanism in human disease and drug discovery. Identifying allosteric regulatory sites to explore their biological significance and therapeutic potential is invaluable to drug discovery; however, identification remains a challenge. Allosteric sites are often "cryptic" without clear geometric or chemical features. Since allosteric regulatory sites are often less conserved in protein kinases than the orthosteric ATP binding site, allosteric ligands are commonly more specific than ATP competitive inhibitors. We present a generalizable computational protocol to predict allosteric ligand binding sites based on unbiased ligand binding simulation trajectories. We demonstrate the feasibility of this protocol by revisiting our previously published ligand binding simulations using the first identified viral proto-oncogene, Src kinase, as a model system. The binding paths for kinase inhibitor PP1 uncovered three metastable intermediate states before binding the high-affinity ATP-binding pocket, revealing two previously known allosteric sites and one novel site. Herein, we validate the novel site using a combination of virtual screening and experimental assays to identify a v-type allosteric small-molecule inhibitor that targets this novel site with specificity for Src over closely related kinases. This study provides a proof-of-concept for employing unbiased ligand binding simulations to identify cryptic allosteric binding sites and is widely applicable to other protein-ligand systems.
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Affiliation(s)
- Victoria R Mingione
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Zachariah Foda
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - YiTing Paung
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Hannah Philipose
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Aziz M Rangwala
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yibing Shan
- Antidote Health Foundation for Cure of Cancer, Cambridge, MA 02139, USA.
| | - Markus A Seeliger
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY 11794, USA.
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14
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Shanak S, Bassalat N, Barghash A, Kadan S, Ardah M, Zaid H. Drug Discovery of Plausible Lead Natural Compounds That Target the Insulin Signaling Pathway: Bioinformatics Approaches. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:2832889. [PMID: 35356248 PMCID: PMC8958086 DOI: 10.1155/2022/2832889] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 01/16/2022] [Accepted: 02/09/2022] [Indexed: 12/11/2022]
Abstract
The growing smooth talk in the field of natural compounds is due to the ancient and current interest in herbal medicine and their potentially positive effects on health. Dozens of antidiabetic natural compounds were reported and tested in vivo, in silico, and in vitro. The role of these natural compounds, their actions on the insulin signaling pathway, and the stimulation of the glucose transporter-4 (GLUT4) insulin-responsive translocation to the plasma membrane (PM) are all crucial in the treatment of diabetes and insulin resistance. In this review, we collected and summarized a group of available in vivo and in vitro studies which targeted isolated phytochemicals with possible antidiabetic activity. Moreover, the in silico docking of natural compounds with some of the insulin signaling cascade key proteins is also summarized based on the current literature. In this review, hundreds of recent studies on pure natural compounds that alleviate type II diabetes mellitus (type II DM) were revised. We focused on natural compounds that could potentially regulate blood glucose and stimulate GLUT4 translocation through the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. On attempt to point out potential new natural antidiabetic compounds, this review also focuses on natural ingredients that were shown to interact with proteins in the insulin signaling pathway in silico, regardless of their in vitro/in vivo antidiabetic activity. We invite interested researchers to test these compounds as potential novel type II DM drugs and explore their therapeutic mechanisms.
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Affiliation(s)
- Siba Shanak
- Faculty of Sciences, Arab American University, P.O Box 240, Jenin, State of Palestine
| | - Najlaa Bassalat
- Faculty of Sciences, Arab American University, P.O Box 240, Jenin, State of Palestine
- Faculty of Medicine, Arab American University, P.O Box 240, Jenin, State of Palestine
| | - Ahmad Barghash
- Computer Science Department, German Jordanian University, Madaba Street. P.O. Box 35247, Amman 11180, Jordan
| | - Sleman Kadan
- Qasemi Research Center, Al-Qasemi Academic College, P.O Box 124, Baqa El-Gharbia 30100, Israel
| | - Mahmoud Ardah
- Faculty of Sciences, Arab American University, P.O Box 240, Jenin, State of Palestine
| | - Hilal Zaid
- Faculty of Medicine, Arab American University, P.O Box 240, Jenin, State of Palestine
- Qasemi Research Center, Al-Qasemi Academic College, P.O Box 124, Baqa El-Gharbia 30100, Israel
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15
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Bassani D, Pavan M, Bolcato G, Sturlese M, Moro S. Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro. Pharmaceuticals (Basel) 2022; 15:ph15020180. [PMID: 35215293 PMCID: PMC8878732 DOI: 10.3390/ph15020180] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 01/27/2023] Open
Abstract
In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (Mpro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher.
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16
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Ferrari F, Bissaro M, Fabbian S, De Almeida Roger J, Mammi S, Moro S, Bellanda M, Sturlese M. HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR. J Enzyme Inhib Med Chem 2021; 36:1-14. [PMID: 33115279 PMCID: PMC7598995 DOI: 10.1080/14756366.2020.1838499] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 10/13/2020] [Accepted: 10/13/2020] [Indexed: 01/21/2023] Open
Abstract
Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.
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Affiliation(s)
- Francesca Ferrari
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Simone Fabbian
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Jessica De Almeida Roger
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Mammi
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Massimo Bellanda
- Department of Chemical Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
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17
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Stanzione F, Giangreco I, Cole JC. Use of molecular docking computational tools in drug discovery. PROGRESS IN MEDICINAL CHEMISTRY 2021; 60:273-343. [PMID: 34147204 DOI: 10.1016/bs.pmch.2021.01.004] [Citation(s) in RCA: 125] [Impact Index Per Article: 41.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.
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Affiliation(s)
| | - Ilenia Giangreco
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
| | - Jason C Cole
- Cambridge Crystallographic Data Centre, Cambridge, United Kingdom
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18
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Bissaro M, Bolcato G, Pavan M, Bassani D, Sturlese M, Moro S. Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 M pro by Using Supervised Molecular Dynamics (SuMD) Simulations. ChemMedChem 2021; 16:2075-2081. [PMID: 33797868 PMCID: PMC8250706 DOI: 10.1002/cmdc.202100156] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Indexed: 12/19/2022]
Abstract
Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.
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Affiliation(s)
- Maicol Bissaro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Giovanni Bolcato
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35131, Padova, Italy
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19
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Bissaro M, Sturlese M, Moro S. The rise of molecular simulations in fragment-based drug design (FBDD): an overview. Drug Discov Today 2020; 25:1693-1701. [PMID: 32592867 PMCID: PMC7314695 DOI: 10.1016/j.drudis.2020.06.023] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/24/2020] [Accepted: 06/19/2020] [Indexed: 12/31/2022]
Abstract
Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.
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Affiliation(s)
- Maicol Bissaro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section, Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padova, Italy.
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20
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Abstract
Molecular Docking is used to positioning the computer-generated 3D structure of small
ligands into a receptor structure in a variety of orientations, conformations and positions. This
method is useful in drug discovery and medicinal chemistry providing insights into molecular
recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery
(CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment
of targets and ligand. Over the last decade, advances in the field of computational, proteomics and
genomics have also led to the development of different docking methods which incorporate
protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts
for more accurate binding pose predictions and a more rational depiction of protein binding
interactions with the ligand. Protein flexibility has been included by generating protein ensembles
or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also
fully explores the drug-receptor binding and recognition from both energetic and mechanistic point
of view. Though in the fast-paced drug discovery program, dynamic docking is computationally
expensive but is being progressively used for screening of large compound libraries to identify the
potential drugs. In this review, a quick introduction is presented to the available docking methods
and their application and limitations in drug discovery.
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Affiliation(s)
- Ritu Jakhar
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Mehak Dangi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
| | - Alka Khichi
- Center for Bioinformatics, Maharshi Dayanand University, Rohtak, India
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21
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de Souza Neto LR, Moreira-Filho JT, Neves BJ, Maidana RLBR, Guimarães ACR, Furnham N, Andrade CH, Silva FP. In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. Front Chem 2020; 8:93. [PMID: 32133344 PMCID: PMC7040036 DOI: 10.3389/fchem.2020.00093] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 01/30/2020] [Indexed: 12/16/2022] Open
Abstract
Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.
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Affiliation(s)
- Lauro Ribeiro de Souza Neto
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - José Teófilo Moreira-Filho
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil
| | - Bruno Junior Neves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil
- Laboratory of Cheminformatics, Centro Universitário de Anápolis – UniEVANGÉLICA, Anápolis, Brazil
| | - Rocío Lucía Beatriz Riveros Maidana
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
- Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Ana Carolina Ramos Guimarães
- Laboratório de Genômica Funcional e Bioinformática, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Nicholas Furnham
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Carolina Horta Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, Brazil
| | - Floriano Paes Silva
- LaBECFar – Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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22
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Shiref H, Bergman S, Clivio S, Sahai MA. The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations. Methods 2020; 185:3-14. [PMID: 32081744 PMCID: PMC10062712 DOI: 10.1016/j.ymeth.2020.02.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 02/14/2020] [Accepted: 02/14/2020] [Indexed: 10/25/2022] Open
Abstract
Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.
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Affiliation(s)
- Hana Shiref
- Department of Life Sciences, University of Roehampton, London SW15 4JD, UK
| | - Shana Bergman
- Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University (WCMC), New York, NY 10065, USA
| | | | - Michelle A Sahai
- Department of Life Sciences, University of Roehampton, London SW15 4JD, UK.
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23
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Majewski M, Barril X. Structural Stability Predicts the Binding Mode of Protein–Ligand Complexes. J Chem Inf Model 2020; 60:1644-1651. [DOI: 10.1021/acs.jcim.9b01062] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Maciej Majewski
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
| | - Xavier Barril
- Institut de Biomedicina de la Universitat de Barcelona (IBUB) and Facultat de Farmacia, Universitat de Barcelona, Av. Joan XXIII 27-31, Barcelona 08028, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluis Companys 23, Barcelona 08010, Spain
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24
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Understanding the role of hydrogen bonds in destruction of DNA by screening interactions of Flutamide anticancer drug with nucleotides bases: DFT perspective, MD simulation and free energy calculation. ADSORPTION 2019. [DOI: 10.1007/s10450-019-00179-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Jokinen EM, Postila PA, Ahinko M, Niinivehmas S, Pentikäinen OT. Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors. Chem Biol Drug Des 2019; 94:1799-1812. [PMID: 31260165 DOI: 10.1111/cbdd.13584] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 06/12/2019] [Accepted: 06/24/2019] [Indexed: 12/19/2022]
Abstract
A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.
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Affiliation(s)
| | - Pekka A Postila
- Department of Biological and Environmental Science, University of Jyvaskyla, Jyvaskyla, Finland
| | - Mira Ahinko
- Department of Biological and Environmental Science, University of Jyvaskyla, Jyvaskyla, Finland
| | | | - Olli T Pentikäinen
- Institute of Biomedicine, University of Turku, Turku, Finland.,Department of Biological and Environmental Science, University of Jyvaskyla, Jyvaskyla, Finland.,Aurlide Ltd., Lieto, Finland
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26
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Giordanetto F, Jin C, Willmore L, Feher M, Shaw DE. Fragment Hits: What do They Look Like and How do They Bind? J Med Chem 2019; 62:3381-3394. [PMID: 30875465 PMCID: PMC6466478 DOI: 10.1021/acs.jmedchem.8b01855] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
![]()
A “fragment hit”, a
molecule of low molecular weight
that has been validated to bind to a target protein, can be an effective
chemical starting point for a drug discovery project. Our ability
to find and progress fragment hits could potentially be improved by
enhancing our understanding of their binding properties, which to
date has largely been based on tacit knowledge and reports from individual
projects. In the work reported here, we systematically analyzed the
molecular and binding properties of fragment hits using 489 published
protein–fragment complexes. We identified a number of notable
features that these hits tend to have in common, including preferences
in buried surface area upon binding, hydrogen bonding and other directional
interactions with the protein targets, structural topology, functional-group
occurrence, and degree of carbon saturation. In the future, taking
account of these preferences in designing and selecting fragments
to screen against protein targets may increase the chances of success
in fragment screening campaigns.
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Affiliation(s)
| | - Chentian Jin
- D. E. Shaw Research , New York , New York 10036 , United States
| | | | - Miklos Feher
- D. E. Shaw Research , New York , New York 10036 , United States
| | - David E Shaw
- D. E. Shaw Research , New York , New York 10036 , United States.,Department of Biochemistry and Molecular Biophysics , Columbia University , New York , New York 10032 , United States
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27
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Abdullah M, Guruprasad L. Computational fragment-based design of Wee1 kinase inhibitors with tricyclic core scaffolds. Struct Chem 2019. [DOI: 10.1007/s11224-018-1176-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Azarhazin E, Izadyar M, Housaindokht MR. Drug–DNA interaction, a joint DFT-D3/MD study on safranal as an anticancer and DNA nanostructure model. CAN J CHEM 2019. [DOI: 10.1139/cjc-2018-0126] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this research, using a combination of quantum mechanics and molecular dynamic (MD) simulations, the interaction of safranal (2,6,6-trimethylcyclohexa-1,3-dien-1-carboxaldehyde) as an anti-cancer drug and Dickerson B-DNA was studied. MD simulations were executed for 35 ns in water. Binding energy analysis in three definite parts of the B-DNA and comparison between different contributions of the binding energy shows that the van der Waals energy part of the interaction is impressive among the standard molecular mechanic energy terms. On the basis of Gibbs energies, it is confirmed that the most important interactions in the safranal complex are related to the A–T and C–G rich regions, which is in agreement with the experimental data. Quantum theory of atoms in molecules and natural bond orbital analyses were applied. A diminution in the electronic chemical potential of the safranal–DNA complex in comparison with the isolated DNA, 0.026 and 0.022 au for the S1 region and 0.012 and 0.017 au for the S2 region, was obtained in the gas phase and water, respectively, which increases the complex stability. An enhancement in the electrophilicity character, during the complexation process, shows the electron charge flux between the safranal and DNA, especially in water. The strengths of the CH⋯O bonds at the center of safranal–DNA interaction were also evaluated. A mean value of 0.06 au for the electron density of the bond critical point of the H⋯O in the complex confirms the H-bond formation during the complexation.
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Affiliation(s)
- Ebrahim Azarhazin
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad Izadyar
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammad Reza Housaindokht
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
- Computational Chemistry Laboratory, Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
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29
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Klein R, Linciano P, Celenza G, Bellio P, Papaioannou S, Blazquez J, Cendron L, Brenk R, Tondi D. In silico identification and experimental validation of hits active against KPC-2 β-lactamase. PLoS One 2018; 13:e0203241. [PMID: 30496182 PMCID: PMC6264499 DOI: 10.1371/journal.pone.0203241] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 11/06/2018] [Indexed: 01/25/2023] Open
Abstract
Bacterial resistance has become a worldwide concern, particularly after the emergence of resistant strains overproducing carbapenemases. Among these, the KPC-2 carbapenemase represents a significant clinical challenge, being characterized by a broad substrate spectrum that includes aminothiazoleoxime and cephalosporins such as cefotaxime. Moreover, strains harboring KPC-type β-lactamases are often reported as resistant to available β-lactamase inhibitors (clavulanic acid, tazobactam and sulbactam). Therefore, the identification of novel non β-lactam KPC-2 inhibitors is strongly necessary to maintain treatment options. This study explored novel, non-covalent inhibitors active against KPC-2, as putative hit candidates. We performed a structure-based in silico screening of commercially available compounds for non-β-lactam KPC-2 inhibitors. Thirty-two commercially available high-scoring, fragment-like hits were selected for in vitro validation and their activity and mechanism of action vs the target was experimentally evaluated using recombinant KPC-2. N-(3-(1H-tetrazol-5-yl)phenyl)-3-fluorobenzamide (11a), in light of its ligand efficiency (LE = 0.28 kcal/mol/non-hydrogen atom) and chemistry, was selected as hit to be directed to chemical optimization to improve potency vs the enzyme and explore structural requirement for inhibition in KPC-2 binding site. Further, the compounds were evaluated against clinical strains overexpressing KPC-2 and the most promising compound reduced the MIC of the β-lactam antibiotic meropenem by four-fold.
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Affiliation(s)
- Raphael Klein
- Institute of Pharmacy and Biochemistry, Johannes Gutenberg University, Mainz, Germany
| | - Pasquale Linciano
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Giuseppe Celenza
- Dipartimento di Scienze Cliniche Applicate e Biotecnologie, Università dell’Aquila,L’Aquila, Italy
| | - Pierangelo Bellio
- Dipartimento di Scienze Cliniche Applicate e Biotecnologie, Università dell’Aquila,L’Aquila, Italy
| | - Sofia Papaioannou
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
| | - Jesus Blazquez
- Department of Microbial Biotechnology, National Center for Biotechnology, Consejo Superior de Investigaciones Científicas (CSIC), Campus de la Universidad Autonoma-Cantoblanco, Madrid, Spain
| | - Laura Cendron
- Dipartimento di Biologia, Università di Padova, Padova, Italy
| | - Ruth Brenk
- Department of Biomedicine, University of Bergen, Bergen, Norway
- * E-mail: (DT); (RB)
| | - Donatella Tondi
- Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, Modena, Italy
- * E-mail: (DT); (RB)
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30
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Wang J, Gong GQ, Zhou Y, Lee WJ, Buchanan CM, Denny WA, Rewcastle GW, Kendall JD, Dickson JMJ, Flanagan JU, Shepherd PR, Yang DH, Wang MW. High-throughput screening campaigns against a PI3Kα isoform bearing the H1047R mutation identified potential inhibitors with novel scaffolds. Acta Pharmacol Sin 2018; 39:1816-1822. [PMID: 29991713 DOI: 10.1038/s41401-018-0057-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 05/30/2018] [Indexed: 12/30/2022] Open
Abstract
The phosphatidylinositol 3-kinase (PI3K) pathway is involved in many cellular functions including cell growth, metabolism, and transformation. Hyperactivation of this pathway contributes to tumorigenesis, therefore, PI3K is a major target for anticancer drug discovery. Since the PI3Kα isoform is implicated mostly in cancer, we conducted a high-throughput screening (HTS) campaign using a 3-step PI3K homogenous time-resolved fluorescence assay against this isoform bearing the H1047R mutation. A total of 288,000 synthetic and natural product-derived compounds were screened and of which, we identified 124 initial hits that were further selected by considering the predicted binding mode, relationship to known pan-assay interference compounds and previous descriptions as a lipid kinase inhibitor. A total of 24 compounds were then tested for concentration-dependent responses. These hit compounds provide novel scaffolds that can potentially be optimized to create novel PI3K inhibitors.
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31
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Erlanson DA, Davis BJ, Jahnke W. Fragment-Based Drug Discovery: Advancing Fragments in the Absence of Crystal Structures. Cell Chem Biol 2018; 26:9-15. [PMID: 30482678 DOI: 10.1016/j.chembiol.2018.10.001] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/12/2018] [Accepted: 09/28/2018] [Indexed: 01/08/2023]
Abstract
Fragment-based drug discovery typically requires an interplay between screening methods, structural methods, and medicinal chemistry. X-ray crystallography is generally the method of choice to obtain three-dimensional structures of the bound ligand/protein complex, but this can sometimes be difficult, particularly for early, low-affinity fragment hits. In this Perspective, we discuss strategies to advance and evolve fragments in the absence of crystal structures of protein-fragment complexes, although the structure of the unliganded protein may be available. The strategies can involve other structural techniques, such as NMR spectroscopy, molecular modeling, or a variety of chemical approaches. Often, these strategies are aimed at guiding evolution of initial fragment hits to a stage where crystal structures can be obtained for further structure-based optimization.
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Affiliation(s)
- Daniel A Erlanson
- Carmot Therapeutics, Inc., 740 Heinz Avenue, Berkeley, CA 94710, USA.
| | - Ben J Davis
- Vernalis (R&D) Ltd, Granta Park, Cambridge, UK.
| | - Wolfgang Jahnke
- Novartis Institutes for Biomedical Research, Chemical Biology and Therapeutics, Novartis Campus, Basel, Switzerland.
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32
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Marchand JR, Caflisch A. In silico fragment-based drug design with SEED. Eur J Med Chem 2018; 156:907-917. [PMID: 30064119 DOI: 10.1016/j.ejmech.2018.07.042] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/11/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022]
Abstract
We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 103-104 fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-102 fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.
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Affiliation(s)
- Jean-Rémy Marchand
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zürich, CH-8057, Zürich, Switzerland.
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33
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NMR-Fragment Based Virtual Screening: A Brief Overview. Molecules 2018; 23:molecules23020233. [PMID: 29370102 PMCID: PMC6017141 DOI: 10.3390/molecules23020233] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 12/12/2017] [Accepted: 12/12/2017] [Indexed: 01/23/2023] Open
Abstract
Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.
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34
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Praharaj AB, Dehury B, Mahapatra N, Kar SK, Behera SK. Molecular dynamics insights into the structure, function, and substrate binding mechanism of mucin desulfating sulfatase of gut microbe
Bacteroides fragilis. J Cell Biochem 2018; 119:3618-3631. [DOI: 10.1002/jcb.26569] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/01/2017] [Indexed: 12/29/2022]
Affiliation(s)
| | - Budheswar Dehury
- Biomedical Informatics CentreICMR‐Regional Medical Research CentreBhubaneswarOdishaIndia
| | - Namita Mahapatra
- Biomedical Informatics CentreICMR‐Regional Medical Research CentreBhubaneswarOdishaIndia
| | - Shantanu Kumar Kar
- Directorate of Medical Research, IMS & SUM HospitalSiksha “O” Anusandhan UniversityBhubaneswarOdishaIndia
| | - Santosh Kumar Behera
- Biomedical Informatics CentreICMR‐Regional Medical Research CentreBhubaneswarOdishaIndia
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35
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Petrelli R, Scortichini M, Belardo C, Boccella S, Luongo L, Capone F, Kachler S, Vita P, Del Bello F, Maione S, Lavecchia A, Klotz KN, Cappellacci L. Structure-Based Design, Synthesis, and In Vivo Antinociceptive Effects of Selective A1 Adenosine Receptor Agonists. J Med Chem 2018; 61:305-318. [DOI: 10.1021/acs.jmedchem.7b01399] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Riccardo Petrelli
- School
of Pharmacy, Medicinal Chemistry Unit, University of Camerino, 62032 Camerino, Italy
| | - Mirko Scortichini
- School
of Pharmacy, Medicinal Chemistry Unit, University of Camerino, 62032 Camerino, Italy
| | - Carmela Belardo
- Section
of Pharmacology “L. Donatelli”, Department of Experimental
Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Serena Boccella
- Section
of Pharmacology “L. Donatelli”, Department of Experimental
Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Livio Luongo
- Section
of Pharmacology “L. Donatelli”, Department of Experimental
Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Fabio Capone
- Department
of Pharmacy, “Drug Discovery” Laboratory, University of Naples Federico II, 80131 Naples, Italy
| | - Sonja Kachler
- Institut
für Pharmakologie and Toxikologie, Universität Würzburg, D-97078 Würzburg, Germany
| | - Patrizia Vita
- School
of Pharmacy, Medicinal Chemistry Unit, University of Camerino, 62032 Camerino, Italy
| | - Fabio Del Bello
- School
of Pharmacy, Medicinal Chemistry Unit, University of Camerino, 62032 Camerino, Italy
| | - Sabatino Maione
- Section
of Pharmacology “L. Donatelli”, Department of Experimental
Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Antonio Lavecchia
- Department
of Pharmacy, “Drug Discovery” Laboratory, University of Naples Federico II, 80131 Naples, Italy
| | - Karl-Norbert Klotz
- Institut
für Pharmakologie and Toxikologie, Universität Würzburg, D-97078 Würzburg, Germany
| | - Loredana Cappellacci
- School
of Pharmacy, Medicinal Chemistry Unit, University of Camerino, 62032 Camerino, Italy
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36
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Śledź P, Caflisch A. Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol 2017; 48:93-102. [PMID: 29149726 DOI: 10.1016/j.sbi.2017.10.010] [Citation(s) in RCA: 312] [Impact Index Per Article: 44.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/05/2017] [Accepted: 10/09/2017] [Indexed: 01/24/2023]
Abstract
Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 106 small molecules followed by scoring based on implicit-solvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.
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Affiliation(s)
- Paweł Śledź
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zürich, Switzerland.
| | - Amedeo Caflisch
- Department of Biochemistry, University of Zurich, Winterthurerstr. 190, 8057 Zürich, Switzerland.
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37
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Yasmin S, Capone F, Laghezza A, Piaz FD, Loiodice F, Vijayan V, Devadasan V, Mondal SK, Atlı Ö, Baysal M, Pattnaik AK, Jayaprakash V, Lavecchia A. Novel Benzylidene Thiazolidinedione Derivatives as Partial PPARγ Agonists and their Antidiabetic Effects on Type 2 Diabetes. Sci Rep 2017; 7:14453. [PMID: 29089569 PMCID: PMC5663708 DOI: 10.1038/s41598-017-14776-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 10/12/2017] [Indexed: 12/22/2022] Open
Abstract
Peroxisome proliferator-activated receptor γ (PPARγ) has received significant attention as a key regulator of glucose and lipid homeostasis. In this study, we synthesized and tested a library of novel 5-benzylidene-thiazolidin-2,4-dione (BTZD) derivatives bearing a substituent on nitrogen of TZD nucleus (compounds 1a-1k, 2i-10i, 3a, 6a, and 8a-10a). Three compounds (1a, 1i, and 3a) exhibited selectivity towards PPARγ and were found to be weak to moderate partial agonists. Surface Plasmon Resonance (SPR) results demonstrated binding affinity of 1a, 1i and 3a towards PPARγ. Furthermore, docking experiments revealed that BTZDs interact with PPARγ through a distinct binding mode, forming primarily hydrophobic contacts with the ligand-binding pocket (LBD) without direct H-bonding interactions to key residues in H12 that are characteristic of full agonists. In addition, 1a, 1i and 3a significantly improved hyperglycemia and hyperlipidaemia in streptozotocin-nicotinamide (STZ-NA)-induced diabetic rats at a dose of 36 mg/kg/day administered orally for 15 days. Histopathological investigations revealed that microscopic architecture of pancreatic and hepatic cells improved in BTZDs-treated diabetic rats. These findings suggested that 1a, 1i and 3a are very promising pharmacological agents by selectively targeting PPARγ for further development in the clinical treatment of type 2 diabetes mellitus.
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Affiliation(s)
- Sabina Yasmin
- Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835 215, India
| | - Fabio Capone
- Department of Pharmacy, "Drug Discovery" Laboratory, University of Napoli "Federico II", Via D. Montesano, 49, 80131, Napoli, Italy
| | - Antonio Laghezza
- Department of Pharmacy & Drug Sciences, University of Bari "Aldo Moro", via Orabona 4, 70125, Bari, Italy
| | - Fabrizio Dal Piaz
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Italy
| | - Fulvio Loiodice
- Department of Pharmacy & Drug Sciences, University of Bari "Aldo Moro", via Orabona 4, 70125, Bari, Italy
| | - Viswanathan Vijayan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Maraimalai (Guindy) Campus, 600 025, Chennai, India
| | - Velmurugan Devadasan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Maraimalai (Guindy) Campus, 600 025, Chennai, India
| | - Susanta K Mondal
- TCG Lifesciences Ltd, Block-EP&GP, BIPL, Tower-B, Saltlake, Sector-V, Kolkata, 700091, West Bengal, India
| | - Özlem Atlı
- Department of Pharmaceutical Toxicology, Faculty of Pharmacy, Anadolu University, Yunus Emre Kampüsü, 26470, Eskişehir, Turkey
| | - Merve Baysal
- Department of Pharmaceutical Toxicology, Faculty of Pharmacy, Anadolu University, Yunus Emre Kampüsü, 26470, Eskişehir, Turkey
| | - Ashok K Pattnaik
- Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835 215, India
| | - Venkatesan Jayaprakash
- Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835 215, India.
| | - Antonio Lavecchia
- Department of Pharmacy, "Drug Discovery" Laboratory, University of Napoli "Federico II", Via D. Montesano, 49, 80131, Napoli, Italy.
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38
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Rudling A, Gustafsson R, Almlöf I, Homan E, Scobie M, Warpman Berglund U, Helleday T, Stenmark P, Carlsson J. Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space. J Med Chem 2017; 60:8160-8169. [PMID: 28929756 DOI: 10.1021/acs.jmedchem.7b01006] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC50 values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.
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Affiliation(s)
- Axel Rudling
- Department of Biochemistry and Biophysics, Stockholm University , SE-106 91 Stockholm, Sweden
| | - Robert Gustafsson
- Department of Biochemistry and Biophysics, Stockholm University , SE-106 91 Stockholm, Sweden
| | - Ingrid Almlöf
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Box 1031, SE-171 21 Solna, Sweden
| | - Evert Homan
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Box 1031, SE-171 21 Solna, Sweden
| | - Martin Scobie
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Box 1031, SE-171 21 Solna, Sweden
| | - Ulrika Warpman Berglund
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Box 1031, SE-171 21 Solna, Sweden
| | - Thomas Helleday
- Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet , Box 1031, SE-171 21 Solna, Sweden
| | - Pål Stenmark
- Department of Biochemistry and Biophysics, Stockholm University , SE-106 91 Stockholm, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, BMC, Uppsala University , Box 596, SE-751 24 Uppsala, Sweden
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39
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Matricon P, Ranganathan A, Warnick E, Gao ZG, Rudling A, Lambertucci C, Marucci G, Ezzati A, Jaiteh M, Dal Ben D, Jacobson KA, Carlsson J. Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site. Sci Rep 2017; 7:6398. [PMID: 28743961 PMCID: PMC5526870 DOI: 10.1038/s41598-017-04905-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 05/23/2017] [Indexed: 12/19/2022] Open
Abstract
Fragment-based lead discovery is becoming an increasingly popular strategy for drug discovery. Fragment screening identifies weakly binding compounds that require optimization to become high-affinity leads. As design of leads from fragments is challenging, reliable computational methods to guide optimization would be invaluable. We evaluated using molecular dynamics simulations and the free energy perturbation method (MD/FEP) in fragment optimization for the A2A adenosine receptor, a pharmaceutically relevant G protein-coupled receptor. Optimization of fragments exploring two binding site subpockets was probed by calculating relative binding affinities for 23 adenine derivatives, resulting in strong agreement with experimental data (R2 = 0.78). The predictive power of MD/FEP was significantly better than that of an empirical scoring function. We also demonstrated the potential of the MD/FEP to assess multiple binding modes and to tailor the thermodynamic profile of ligands during optimization. Finally, MD/FEP was applied prospectively to optimize three nonpurine fragments, and predictions for 12 compounds were evaluated experimentally. The direction of the change in binding affinity was correctly predicted in a majority of the cases, and agreement with experiment could be improved with rigorous parameter derivation. The results suggest that MD/FEP will become a powerful tool in structure-driven optimization of fragments to lead candidates.
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Affiliation(s)
- Pierre Matricon
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-75124, Uppsala, Sweden
| | - Anirudh Ranganathan
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691, Stockholm, Sweden
| | - Eugene Warnick
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, United States
| | - Zhan-Guo Gao
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, United States
| | - Axel Rudling
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691, Stockholm, Sweden
| | - Catia Lambertucci
- Scuola di Scienze del Farmaco e dei Prodotti della Salute, Università degli Studi di Camerino, Via S. Agostino 1, 62032, Camerino (MC), Italy
| | - Gabriella Marucci
- Scuola di Scienze del Farmaco e dei Prodotti della Salute, Università degli Studi di Camerino, Via S. Agostino 1, 62032, Camerino (MC), Italy
| | - Aitakin Ezzati
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, SE-10691, Stockholm, Sweden
| | - Mariama Jaiteh
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-75124, Uppsala, Sweden
| | - Diego Dal Ben
- Scuola di Scienze del Farmaco e dei Prodotti della Salute, Università degli Studi di Camerino, Via S. Agostino 1, 62032, Camerino (MC), Italy
| | - Kenneth A Jacobson
- Molecular Recognition Section, Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, 20892, United States
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-75124, Uppsala, Sweden.
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40
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Noha SM, Schmidhammer H, Spetea M. Molecular Docking, Molecular Dynamics, and Structure-Activity Relationship Explorations of 14-Oxygenated N-Methylmorphinan-6-ones as Potent μ-Opioid Receptor Agonists. ACS Chem Neurosci 2017; 8:1327-1337. [PMID: 28125215 PMCID: PMC5481819 DOI: 10.1021/acschemneuro.6b00460] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
![]()
Among opioids, morphinans
are of major importance as the most effective
analgesic drugs acting primarily via μ-opioid receptor (μ-OR)
activation. Our long-standing efforts in the field of opioid analgesics
from the class of morphinans led to N-methylmorphinan-6-ones
differently substituted at positions 5 and 14 as μ-OR agonists
inducing potent analgesia and fewer undesirable effects. Herein we
present the first thorough molecular modeling study and structure–activity
relationship (SAR) explorations aided by docking and molecular dynamics
(MD) simulations of 14-oxygenated N-methylmorphinan-6-ones
to gain insights into their mode of binding to the μ-OR and
interaction mechanisms. The structure of activated μ-OR provides
an essential model for how ligand/μ-OR binding is encoded within
small chemical differences in otherwise structurally similar morphinans.
We reveal important molecular interactions that these μ-agonists
share and distinguish them. The molecular docking outcomes indicate
the crucial role of the relative orientation of the ligand in the
μ-OR binding site, influencing the propensity of critical non-covalent
interactions that are required to facilitate ligand/μ-OR interactions
and receptor activation. The MD simulations point out minor differences
in the tendency to form hydrogen bonds by the 4,5α-epoxy group,
along with the tendency to affect the 3–7 lock switch. The
emerged SARs reveal the subtle interplay between the substituents
at positions 5 and 14 in the morphinan scaffold by enabling the identification
of key structural elements that determine the distinct pharmacological
profiles. This study provides a significant structural basis for understanding
ligand binding and μ-OR activation by the 14-oxygenated N-methylmorphinan-6-ones, which should be useful for guiding
drug design.
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Affiliation(s)
- Stefan M. Noha
- Computer-Aided
Molecular Design (CAMD) Group, Department of Pharmaceutical Chemistry,
Institute of Pharmacy and Center for Molecular Biosciences Innsbruck
(CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Helmut Schmidhammer
- Opioid
Research Group, Department of Pharmaceutical Chemistry, Institute
of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Mariana Spetea
- Opioid
Research Group, Department of Pharmaceutical Chemistry, Institute
of Pharmacy and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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Petrelli R, Scortichini M, Kachler S, Boccella S, Cerchia C, Torquati I, Del Bello F, Salvemini D, Novellino E, Luongo L, Maione S, Jacobson KA, Lavecchia A, Klotz KN, Cappellacci L. Exploring the Role of N 6-Substituents in Potent Dual Acting 5'-C-Ethyltetrazolyladenosine Derivatives: Synthesis, Binding, Functional Assays, and Antinociceptive Effects in Mice ∇. J Med Chem 2017; 60:4327-4341. [PMID: 28447789 DOI: 10.1021/acs.jmedchem.7b00291] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Structural determinants of affinity of N6-substituted-5'-C-(ethyltetrazol-2-yl)adenosine and 2-chloroadenosine derivatives at adenosine receptor (AR) subtypes were studied with binding and molecular modeling. Small N6-cycloalkyl and 3-halobenzyl groups furnished potent dual acting A1AR agonists and A3AR antagonists. 4 was the most potent dual acting human (h) A1AR agonist (Ki = 0.45 nM) and A3AR antagonist (Ki = 0.31 nM) and highly selective versus A2A; 11 and 26 were most potent at both h and rat (r) A3AR. All N6-substituted-5'-C-(ethyltetrazol-2-yl)adenosine derivatives proved to be antagonists at hA3AR but agonists at the rA3AR. Analgesia of 11, 22, and 26 was evaluated in the mouse formalin test (A3AR antagonist blocked and A3AR agonist strongly potentiated). N6-Methyl-5'-C-(ethyltetrazol-2-yl)adenosine (22) was most potent, inhibiting both phases, as observed combining A1AR and A3AR agonists. This study demonstrated for the first time the advantages of a single molecule activating two AR pathways both leading to benefit in this acute pain model.
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Affiliation(s)
- Riccardo Petrelli
- School of Pharmacy, Medicinal Chemistry Unit, University of Camerino , Via S. Agostino 1, 62032 Camerino, Italy
| | - Mirko Scortichini
- School of Pharmacy, Medicinal Chemistry Unit, University of Camerino , Via S. Agostino 1, 62032 Camerino, Italy
| | - Sonja Kachler
- Institut für Pharmakologie and Toxikologie, Universität Würzburg , D-97078 Würzburg, Germany
| | - Serena Boccella
- Section of Pharmacology "L. Donatelli", Department of Experimental Medicine, University of Campania "L. Vanvitelli" , 80138 Naples, Italy
| | - Carmen Cerchia
- Department of Pharmacy, "Drug Discovery" Laboratory, University of Naples Federico II , 80131 Naples, Italy
| | - Ilaria Torquati
- School of Pharmacy, Medicinal Chemistry Unit, University of Camerino , Via S. Agostino 1, 62032 Camerino, Italy
| | - Fabio Del Bello
- School of Pharmacy, Medicinal Chemistry Unit, University of Camerino , Via S. Agostino 1, 62032 Camerino, Italy
| | - Daniela Salvemini
- Department of Pharmacology and Physiology, Saint Louis University School of Medicine , St. Louis, Missouri 63104, United States
| | - Ettore Novellino
- Department of Pharmacy, "Drug Discovery" Laboratory, University of Naples Federico II , 80131 Naples, Italy
| | - Livio Luongo
- Section of Pharmacology "L. Donatelli", Department of Experimental Medicine, University of Campania "L. Vanvitelli" , 80138 Naples, Italy
| | - Sabatino Maione
- Section of Pharmacology "L. Donatelli", Department of Experimental Medicine, University of Campania "L. Vanvitelli" , 80138 Naples, Italy
| | - Kenneth A Jacobson
- Laboratory of Bioorganic Chemistry, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health , Bethesda, Maryland 20892, United States
| | - Antonio Lavecchia
- Department of Pharmacy, "Drug Discovery" Laboratory, University of Naples Federico II , 80131 Naples, Italy
| | - Karl-Norbert Klotz
- Institut für Pharmakologie and Toxikologie, Universität Würzburg , D-97078 Würzburg, Germany
| | - Loredana Cappellacci
- School of Pharmacy, Medicinal Chemistry Unit, University of Camerino , Via S. Agostino 1, 62032 Camerino, Italy
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Azarhazin E, Izadyar M, Housaindokht MR. Molecular dynamic simulation and DFT study on the Drug-DNA interaction; Crocetin as an anti-cancer and DNA nanostructure model. J Biomol Struct Dyn 2017; 36:1063-1074. [PMID: 28330413 DOI: 10.1080/07391102.2017.1310060] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In this research, the interaction of Crocetin as an anti-cancer drug and a Dickerson DNA has been investigated. 25 ns molecular dynamic simulations of Crocetin and DNA composed of 12 base pairs and a sequence of d(CGCGAATTCGCG)2 were done in water. Three definite parts of the B-DNA were considered in analyzing the best interactive site from the thermodynamic point of view. Binding energy analysis showed that van der Waals interaction is the most important part related to the reciprocal O and H atoms of the Crocetin and DNA. Stabilizing interactions, obtained by ΔG calculations, showed that maximum and minimum interactions are related to the S1 and S3 regions, respectively. This means that the most probable van der Waals interaction site of the Dickerson B-DNA and Crocetin is located in the minor groove of DNA. Two sharp peaks at 2.55 and 1.75 Å in radial distribution functions of the PO⋯HO and NH⋯OC parts are related to new hydrogen bonds between the Crocetin and DNA in the complex which can be considered as the driving force of the anti-cancer mechanism of the Crocetin. Average values of 0.3 au and zero for the electron densities of the H⋯O bonds for DNA and complex, obtained by Quantum theory of atoms in molecules (QTAIM), means that the origin of DNA instability after complexation may be related to the H-bond denaturation by Crocetin. Finally, the evaluation of the dispersion interactions using the dispersion functional, -148.76 kcal.mol-1, confirmed the importance of the dispersion interaction in drug-DNA complex.
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Affiliation(s)
- Ebrahim Azarhazin
- a Faculty of Sciences, Department of Chemistry , Ferdowsi University of Mashhad , Mashhad , Iran
| | - Mohammad Izadyar
- a Faculty of Sciences, Department of Chemistry , Ferdowsi University of Mashhad , Mashhad , Iran
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Abstract
Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.
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Affiliation(s)
- Weilin Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Jianfeng Pei
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China
| | - Luhua Lai
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies (AAIS), Peking University , Beijing 100871, People's Republic of China.,BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University , Beijing 100871, People's Republic of China
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Piemontese L, Cerchia C, Laghezza A, Ziccardi P, Sblano S, Tortorella P, Iacobazzi V, Infantino V, Convertini P, Dal Piaz F, Lupo A, Colantuoni V, Lavecchia A, Loiodice F. New diphenylmethane derivatives as peroxisome proliferator-activated receptor alpha/gamma dual agonists endowed with anti-proliferative effects and mitochondrial activity. Eur J Med Chem 2017; 127:379-397. [DOI: 10.1016/j.ejmech.2016.12.047] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 12/21/2016] [Accepted: 12/23/2016] [Indexed: 12/18/2022]
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45
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Structure-Based Discovery of Small Molecules Binding to RNA. TOPICS IN MEDICINAL CHEMISTRY 2017. [DOI: 10.1007/7355_2016_29] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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46
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Identification of noncovalent proteasome inhibitors with high selectivity for chymotrypsin-like activity by a multistep structure-based virtual screening. Eur J Med Chem 2016; 121:578-591. [DOI: 10.1016/j.ejmech.2016.05.049] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 05/04/2016] [Accepted: 05/21/2016] [Indexed: 02/03/2023]
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47
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Verdonk ML, Ludlow RF, Giangreco I, Rathi PC. Protein–Ligand Informatics Force Field (PLIff): Toward a Fully Knowledge Driven “Force Field” for Biomolecular Interactions. J Med Chem 2016; 59:6891-902. [DOI: 10.1021/acs.jmedchem.6b00716] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Marcel L. Verdonk
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - R. Frederick Ludlow
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Ilenia Giangreco
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
- Dipartimento
Farmaco-Chimico, University of Bari, Via Orabona 4, I-70125 Bari, Italy
| | - Prakash Chandra Rathi
- Astex Pharmaceuticals, 436
Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
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48
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Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci Rep 2016; 6:28288. [PMID: 27339552 PMCID: PMC4919634 DOI: 10.1038/srep28288] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 05/26/2016] [Indexed: 12/20/2022] Open
Abstract
The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.
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Moretti L, Sartori L. Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines. Mol Inform 2016; 35:382-90. [DOI: 10.1002/minf.201501037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 05/19/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Loris Moretti
- Drug Discovery Program, Department of Experimental Oncology; European Institute of Oncology; Via Adamello 16 20139 Milan Italy
- Nuevolution A/S; Rønnegade 8 DK-2100 Copenhagen Denmark
| | - Luca Sartori
- Drug Discovery Program, Department of Experimental Oncology; European Institute of Oncology; Via Adamello 16 20139 Milan Italy
- Experimental Therapeutics Unit; IFOM - The FIRC Institute of Molecular Oncology; Via Adamello 16 20139 Milan Italy
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The antiproliferative and proapoptotic effects of cladosporols A and B are related to their different binding mode as PPARγ ligands. Biochem Pharmacol 2016; 108:22-35. [DOI: 10.1016/j.bcp.2016.03.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/11/2016] [Indexed: 11/20/2022]
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