Discovery of 5,5′-Methylenedi-2,3-Cresotic Acid as a Potent Inhibitor of the Chemotactic Activity of the HMGB1·CXCL12 Heterocomplex Using Virtual Screening and NMR Validation

HMGB1 is a key molecule that both triggers and sustains inflammation following infection or injury, and is involved in a large number of pathologies, including cancer. HMGB1 participates in the recruitment of inflammatory cells, forming a heterocomplex with the chemokine CXCL12 (HMGB1·CXCL12), thereby activating the G-protein coupled receptor CXCR4. Thus, identification of molecules that disrupt this heterocomplex can offer novel pharmacological opportunities to treat inflammation-related diseases. To identify new HMGB1·CXCL12 inhibitors we have performed a study on the ligandability of the single HMG boxes of HMGB1 followed by a virtual screening campaign on both HMG boxes using Zbc Drugs and three different docking programs (Glide, AutoDock Vina, and AutoDock 4.2.6). The best poses in terms of scoring functions, visual inspection, and predicted ADME properties were further filtered according to a pharmacophore model based on known HMGB1 binders and clustered according to their structures. Eight compounds representative of the clusters were tested for HMGB1 binding by NMR. We identified 5,5′-methylenedi-2,3-cresotic acid (2a) as a binder of both HMGB1 and CXCL12; 2a also targets the HMGB1·CXCL12 heterocomplex. In cell migration assays 2a inhibited the chemotactic activity of HMGB1·CXCL12 with IC50 in the subnanomolar range, the best documented up to now. These results pave the way for future structure activity relationship studies to optimize the pharmacological targeting of HMGB1·CXCL12 for anti-inflammatory purposes.

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Discovery of 5,5’-methylenedi-2,3-cresotic acid as a potent inhibitor of chemotactic activity of the HMGB1•CXCL12 heterocomplex using Virtual Screening and NMR validation

10.1101/2020.08.25.266361 ◽

2020 ◽

Author(s):

F. De Leo ◽

G. Quilici ◽

F. De Marchis ◽

M. V. Mantonico ◽

M. E. Bianchi ◽

...

Keyword(s):

Virtual Screening ◽

Pharmacophore Model ◽

Inflammatory Cells ◽

Chemotactic Activity ◽

Scoring Functions ◽

Autodock Vina ◽

Structure Activity ◽

Chemokine Cxcl12 ◽

Migration Assays ◽

G Protein Coupled

AbstractHMGB1 is a key molecule that both triggers and sustains inflammation following infection or injury, and is involved in a large number of pathologies, including cancer. HMGB1 participates to the recruitment of inflammatory cells forming a heterocomplex with the chemokine CXCL12 (HMGB1•CXCL12), herewith activating the G-protein coupled receptor CXCR4. Thus, identification of molecules that disrupt this heterocomplex can offer novel pharmacological opportunities to treat inflammation related diseases. To identify new HMGB1•CXCL12 inhibitors we have performed a study on the ligandability of the single HMG boxes of HMGB1 followed by a virtual screening campaign on both HMG boxes using Zbc Drugs and three different docking programs (Glide, AutoDock Vina, AutoDock 4.2.6). The best poses in terms of scoring functions, visual inspection and predicted ADME properties were further filtered according to a pharmacophore model based on known HMGB1 binders and clustered according to their structures. Eight compounds representative of the clusters were tested for HMGB1 binding by NMR. We identified 5,5’-methylenedi-2,3-cresotic acid (2a) as binder of both HMGB1 and CXCL12; 2a also targets the HMGB1•CXCL12 heterocomplex. In cell migration assays 2a inhibited the chemotactic activity of HMGB1•CXCL12 with IC50 in the subnanomolar range, the best documented up to now. These results pave the way for future structure activity relationship studies to optimize the pharmacological targeting of HMGB1•CXCL12 for anti-inflammatory purposes.

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Identification of Nonpeptidic Urotensin II Receptor Antagonists by Virtual Screening Based on a Pharmacophore Model Derived from Structure−Activity Relationships and Nuclear Magnetic Resonance Studies on Urotensin II

Journal of Medicinal Chemistry ◽

10.1021/jm0111043 ◽

2002 ◽

Vol 45 (9) ◽

pp. 1799-1805 ◽

Cited By ~ 124

Author(s):

Stefanie Flohr ◽

Michael Kurz ◽

Evi Kostenis ◽

Alexandre Brkovich ◽

Alain Fournier ◽

...

Keyword(s):

Nuclear Magnetic Resonance ◽

Magnetic Resonance ◽

Virtual Screening ◽

Pharmacophore Model ◽

Urotensin Ii ◽

Receptor Antagonists ◽

Magnetic Resonance Studies ◽

Structure Activity Relationships ◽

Structure Activity ◽

Nuclear Magnetic

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Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets

International Journal of Medicinal Chemistry ◽

10.1155/2018/3829307 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Jamal Shamsara

Keyword(s):

Virtual Screening ◽

Binding Site ◽

Center Of Mass ◽

Scoring Function ◽

Scoring Functions ◽

Autodock Vina ◽

Protein Targets ◽

Shape Complementarity ◽

Screening Performance ◽

Consensus Scoring

Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

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Target-Bound Generated Pharmacophore Model to Improve the Pharmacophore-Based Virtual Screening: Identification of G-Protein Coupled Human CCR2 Receptors Inhibitors as Anti-Inflammatory Drugs

Cellular and Molecular Bioengineering ◽

10.1007/s12195-013-0304-9 ◽

2013 ◽

Vol 7 (1) ◽

pp. 45-57 ◽

Cited By ~ 8

Author(s):

Adam A. Skelton ◽

Yushir R. Maharaj ◽

Mahmoud E. S. Soliman

Keyword(s):

Virtual Screening ◽

G Protein ◽

Pharmacophore Model ◽

Inflammatory Drugs ◽

Anti Inflammatory ◽

G Protein Coupled

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Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557520666200429102334 ◽

2020 ◽

Vol 20 (14) ◽

pp. 1375-1388 ◽

Cited By ~ 2

Author(s):

Patnala Ganga Raju Achary

Keyword(s):

Machine Learning ◽

Drug Discovery ◽

Virtual Screening ◽

Model Building ◽

Chemical Space ◽

Qsar Model ◽

Quantitative Structure ◽

Efficient Manner ◽

Qsar Analysis ◽

Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

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Toward the Identification of Novel Carbonic Anhydrase XIV Inhibitors using 3D-QSAR Pharmacophore Model, Virtual Screening and Molecular Docking Study

Letters in Drug Design & Discovery ◽

10.2174/15701808113106660083 ◽

2014 ◽

Vol 11 (4) ◽

pp. 403-412 ◽

Cited By ~ 1

Author(s):

Tao Liu ◽

Lu Zhou ◽

Taijin Wang ◽

Lufen He ◽

Xiangyang Tang

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Carbonic Anhydrase ◽

Pharmacophore Model ◽

3D Qsar ◽

Docking Study ◽

Molecular Docking Study

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Point-Substitution of Phenylalanine Residues of 26RFa Neuropeptide: A Structure-Activity Relationship Study

Molecules ◽

10.3390/molecules26144312 ◽

2021 ◽

Vol 26 (14) ◽

pp. 4312

Author(s):

Benjamin Lefranc ◽

Karima Alim ◽

Cindy Neveu ◽

Olivier Le Marec ◽

Christophe Dubessy ◽

...

Keyword(s):

Receptor Activation ◽

Pharmacological Profile ◽

Acid Moiety ◽

Structural Determinants ◽

Physiological Processes ◽

Structure Activity ◽

Peptide Derivatives ◽

Targeting Peptide ◽

G Protein Coupled

26RFa is a neuropeptide that activates the rhodopsin-like G protein-coupled receptor QRFPR/GPR103. This peptidergic system is involved in the regulation of a wide array of physiological processes including feeding behavior and glucose homeostasis. Herein, the pharmacological profile of a homogenous library of QRFPR-targeting peptide derivatives was investigated in vitro on human QRFPR-transfected cells with the aim to provide possible insights into the structural determinants of the Phe residues to govern receptor activation. Our work advocates to include in next generations of 26RFa(20–26)-based QRFPR agonists effective substitutions for each Phe unit, i.e., replacement of the Phe22 residue by a constrained 1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid moiety, and substitution of both Phe24 and Phe26 by their para-chloro counterpart. Taken as a whole, this study emphasizes that optimized modifications in the C-terminal part of 26RFa are mandatory to design selective and potent peptide agonists for human QRFPR.

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Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Molecules ◽

10.3390/molecules26092600 ◽

2021 ◽

Vol 26 (9) ◽

pp. 2600

Author(s):

Fábio G. Martins ◽

André Melo ◽

Sérgio F. Sousa

Keyword(s):

Virtual Screening ◽

Quorum Sensing ◽

In Silico ◽

Bacterial Infections ◽

Extracellular Polymeric Substances ◽

Free Energy Calculations ◽

Ligand Docking ◽

Screening Tests ◽

Scoring Functions ◽

Potential Inhibitors

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.

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