Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD
Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X recepto...
Gespeichert in:
Veröffentlicht in: | Computers in biology and medicine 2023-05, Vol.157, p.106789-106789, Article 106789 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 106789 |
---|---|
container_issue | |
container_start_page | 106789 |
container_title | Computers in biology and medicine |
container_volume | 157 |
creator | Mitra, Soumya Halder, Amit Kumar Ghosh, Nilanjan Mandal, Subhash C. Cordeiro, M. Natália D.S. |
description | Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.
[Display omitted]
•Partial agonism of FXR is a promising strategy for the treatment of NAFLD.•A series of 3-benzamidobenzoic acid derivatives were used for in silico analyses.•2D- and 3D-QSAR provided crucial information regarding structural requirements.•Ligand-based pharmacophore mapping highlighted important pharmacophore features.•MD simulation depicted crucial ligand |
doi_str_mv | 10.1016/j.compbiomed.2023.106789 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2791369157</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0010482523002548</els_id><sourcerecordid>2791369157</sourcerecordid><originalsourceid>FETCH-LOGICAL-c452t-79e4d71e7d12520c772a420dd48a0cb767468326ed31de318cb272bf7fdb6eca3</originalsourceid><addsrcrecordid>eNqFkc1u1DAUhS0EotPCKyBLbNhk8E9iJ8vSMgVpgA1I7CzHvmk9SuxgOyPR5-CBcZhWSGxY-cr-zj3WOQhhSraUUPH2sDVhmnsXJrBbRhgv10K23RO0oa3sKtLw-inaEEJJVbesOUPnKR0IITXh5Dk646ITnDd8g359WsbsqilYGLHzOLnRmYDNnY7aZIjuXmcXPA4D5lUP_l5PzoZ1CM5gbZzFtlDHQh0hYZ3wrGN2esT6NniXclqlOx09pFDg7ziCgTmHuLrlO8CT9voWJvB5JT9f7vbXL9CzQY8JXj6cF-jb7v3Xqw_V_svNx6vLfWXqhuVKdlBbSUFayhpGjJRM14xYW7eamF4KWYuWMwGWUwuctqZnkvWDHGwvwGh-gd6c9s4x_FggZTW5ZGActYewJMVkR0tUtJEFff0PeghL9OV3f6imFZyQQrUnysSQUoRBzdFNOv5UlKi1OXVQf5tTa3Pq1FyRvnowWPr17VH4WFUB3p0AKIkcHUSVjANvwLoSaVY2uP-7_AbauLCd</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2791586300</pqid></control><display><type>article</type><title>Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD</title><source>MEDLINE</source><source>Elsevier ScienceDirect Journals Complete</source><source>ProQuest Central UK/Ireland</source><creator>Mitra, Soumya ; Halder, Amit Kumar ; Ghosh, Nilanjan ; Mandal, Subhash C. ; Cordeiro, M. Natália D.S.</creator><creatorcontrib>Mitra, Soumya ; Halder, Amit Kumar ; Ghosh, Nilanjan ; Mandal, Subhash C. ; Cordeiro, M. Natália D.S.</creatorcontrib><description>Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.
[Display omitted]
•Partial agonism of FXR is a promising strategy for the treatment of NAFLD.•A series of 3-benzamidobenzoic acid derivatives were used for in silico analyses.•2D- and 3D-QSAR provided crucial information regarding structural requirements.•Ligand-based pharmacophore mapping highlighted important pharmacophore features.•MD simulation depicted crucial ligand-receptor interactions for higher activity.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2023.106789</identifier><identifier>PMID: 36963353</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Agonists ; Amino acids ; Bile ; Binding sites ; Biological activity ; Cholesterol ; Datasets ; Farnesoid X receptor ; Fatty liver ; Feature selection ; Genetic algorithms ; Health hazards ; Helices ; Humans ; Hydrogen bonds ; Informatics ; Insulin resistance ; Ligands ; Liver ; Liver cancer ; Liver cirrhosis ; Liver diseases ; Metabolic disorders ; Molecular Docking Simulation ; Molecular dynamics ; Molecular Dynamics Simulation ; Molecular modelling ; Non-alcoholic fatty liver disease ; Non-alcoholic Fatty Liver Disease - drug therapy ; Pharmacology ; Pharmacophore mapping ; QSAR ; Quantitative Structure-Activity Relationship ; Receptors ; Reproducibility of Results ; RNA-Binding Proteins - antagonists & inhibitors ; RNA-Binding Proteins - metabolism ; Structure-activity relationships ; Three dimensional models ; Two dimensional models</subject><ispartof>Computers in biology and medicine, 2023-05, Vol.157, p.106789-106789, Article 106789</ispartof><rights>2023 Elsevier Ltd</rights><rights>Copyright © 2023 Elsevier Ltd. All rights reserved.</rights><rights>2023. Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-79e4d71e7d12520c772a420dd48a0cb767468326ed31de318cb272bf7fdb6eca3</citedby><cites>FETCH-LOGICAL-c452t-79e4d71e7d12520c772a420dd48a0cb767468326ed31de318cb272bf7fdb6eca3</cites><orcidid>0000-0002-0664-0389 ; 0000-0003-3375-8670 ; 0000-0003-0520-0952</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2791586300?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36963353$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mitra, Soumya</creatorcontrib><creatorcontrib>Halder, Amit Kumar</creatorcontrib><creatorcontrib>Ghosh, Nilanjan</creatorcontrib><creatorcontrib>Mandal, Subhash C.</creatorcontrib><creatorcontrib>Cordeiro, M. Natália D.S.</creatorcontrib><title>Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.
[Display omitted]
•Partial agonism of FXR is a promising strategy for the treatment of NAFLD.•A series of 3-benzamidobenzoic acid derivatives were used for in silico analyses.•2D- and 3D-QSAR provided crucial information regarding structural requirements.•Ligand-based pharmacophore mapping highlighted important pharmacophore features.•MD simulation depicted crucial ligand-receptor interactions for higher activity.</description><subject>Agonists</subject><subject>Amino acids</subject><subject>Bile</subject><subject>Binding sites</subject><subject>Biological activity</subject><subject>Cholesterol</subject><subject>Datasets</subject><subject>Farnesoid X receptor</subject><subject>Fatty liver</subject><subject>Feature selection</subject><subject>Genetic algorithms</subject><subject>Health hazards</subject><subject>Helices</subject><subject>Humans</subject><subject>Hydrogen bonds</subject><subject>Informatics</subject><subject>Insulin resistance</subject><subject>Ligands</subject><subject>Liver</subject><subject>Liver cancer</subject><subject>Liver cirrhosis</subject><subject>Liver diseases</subject><subject>Metabolic disorders</subject><subject>Molecular Docking Simulation</subject><subject>Molecular dynamics</subject><subject>Molecular Dynamics Simulation</subject><subject>Molecular modelling</subject><subject>Non-alcoholic fatty liver disease</subject><subject>Non-alcoholic Fatty Liver Disease - drug therapy</subject><subject>Pharmacology</subject><subject>Pharmacophore mapping</subject><subject>QSAR</subject><subject>Quantitative Structure-Activity Relationship</subject><subject>Receptors</subject><subject>Reproducibility of Results</subject><subject>RNA-Binding Proteins - antagonists & inhibitors</subject><subject>RNA-Binding Proteins - metabolism</subject><subject>Structure-activity relationships</subject><subject>Three dimensional models</subject><subject>Two dimensional models</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkc1u1DAUhS0EotPCKyBLbNhk8E9iJ8vSMgVpgA1I7CzHvmk9SuxgOyPR5-CBcZhWSGxY-cr-zj3WOQhhSraUUPH2sDVhmnsXJrBbRhgv10K23RO0oa3sKtLw-inaEEJJVbesOUPnKR0IITXh5Dk646ITnDd8g359WsbsqilYGLHzOLnRmYDNnY7aZIjuXmcXPA4D5lUP_l5PzoZ1CM5gbZzFtlDHQh0hYZ3wrGN2esT6NniXclqlOx09pFDg7ziCgTmHuLrlO8CT9voWJvB5JT9f7vbXL9CzQY8JXj6cF-jb7v3Xqw_V_svNx6vLfWXqhuVKdlBbSUFayhpGjJRM14xYW7eamF4KWYuWMwGWUwuctqZnkvWDHGwvwGh-gd6c9s4x_FggZTW5ZGActYewJMVkR0tUtJEFff0PeghL9OV3f6imFZyQQrUnysSQUoRBzdFNOv5UlKi1OXVQf5tTa3Pq1FyRvnowWPr17VH4WFUB3p0AKIkcHUSVjANvwLoSaVY2uP-7_AbauLCd</recordid><startdate>202305</startdate><enddate>202305</enddate><creator>Mitra, Soumya</creator><creator>Halder, Amit Kumar</creator><creator>Ghosh, Nilanjan</creator><creator>Mandal, Subhash C.</creator><creator>Cordeiro, M. Natália D.S.</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0664-0389</orcidid><orcidid>https://orcid.org/0000-0003-3375-8670</orcidid><orcidid>https://orcid.org/0000-0003-0520-0952</orcidid></search><sort><creationdate>202305</creationdate><title>Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD</title><author>Mitra, Soumya ; Halder, Amit Kumar ; Ghosh, Nilanjan ; Mandal, Subhash C. ; Cordeiro, M. Natália D.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-79e4d71e7d12520c772a420dd48a0cb767468326ed31de318cb272bf7fdb6eca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agonists</topic><topic>Amino acids</topic><topic>Bile</topic><topic>Binding sites</topic><topic>Biological activity</topic><topic>Cholesterol</topic><topic>Datasets</topic><topic>Farnesoid X receptor</topic><topic>Fatty liver</topic><topic>Feature selection</topic><topic>Genetic algorithms</topic><topic>Health hazards</topic><topic>Helices</topic><topic>Humans</topic><topic>Hydrogen bonds</topic><topic>Informatics</topic><topic>Insulin resistance</topic><topic>Ligands</topic><topic>Liver</topic><topic>Liver cancer</topic><topic>Liver cirrhosis</topic><topic>Liver diseases</topic><topic>Metabolic disorders</topic><topic>Molecular Docking Simulation</topic><topic>Molecular dynamics</topic><topic>Molecular Dynamics Simulation</topic><topic>Molecular modelling</topic><topic>Non-alcoholic fatty liver disease</topic><topic>Non-alcoholic Fatty Liver Disease - drug therapy</topic><topic>Pharmacology</topic><topic>Pharmacophore mapping</topic><topic>QSAR</topic><topic>Quantitative Structure-Activity Relationship</topic><topic>Receptors</topic><topic>Reproducibility of Results</topic><topic>RNA-Binding Proteins - antagonists & inhibitors</topic><topic>RNA-Binding Proteins - metabolism</topic><topic>Structure-activity relationships</topic><topic>Three dimensional models</topic><topic>Two dimensional models</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mitra, Soumya</creatorcontrib><creatorcontrib>Halder, Amit Kumar</creatorcontrib><creatorcontrib>Ghosh, Nilanjan</creatorcontrib><creatorcontrib>Mandal, Subhash C.</creatorcontrib><creatorcontrib>Cordeiro, M. Natália D.S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mitra, Soumya</au><au>Halder, Amit Kumar</au><au>Ghosh, Nilanjan</au><au>Mandal, Subhash C.</au><au>Cordeiro, M. Natália D.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2023-05</date><risdate>2023</risdate><volume>157</volume><spage>106789</spage><epage>106789</epage><pages>106789-106789</pages><artnum>106789</artnum><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>Non-alcoholic fatty liver disease (NAFLD) is a pathological condition which is strongly correlated with fat accumulation in the liver that has become a major health hazard globally. So far, limited treatment options are available for the management of NAFLD and partial agonism of Farnesoid X receptor (FXR) has proven to be one of the most promising strategies for treatment of NAFLD. In present work, a range of validated predictive cheminformatics and molecular modeling studies were performed with a series of 3-benzamidobenzoic acid derivatives in order to recognize their structural requirements for possessing higher potency towards FXR. 2D-QSAR models were able to extract the most significant structural attributes determining the higher activity towards the receptor. Ligand-based pharmacophore model was created with a novel and less-explored open access tool named QPhAR to acquire information regarding important 3D-pharmacophoric features that lead to higher agonistic potential towards the FXR. The alignment of the dataset compounds based on pharmacophore mapping led to 3D-QSAR models that pointed out the most crucial steric and electrostatic influence. Molecular dynamics (MD) simulation performed with the most potent and the least potent derivatives of the current dataset helped us to understand how to link the structural interpretations obtained from 2D-QSAR, 3D-QSAR and pharmacophore models with the involvement of specific amino acid residues in the FXR protein. The current study revealed that hydrogen bond interactions with carboxylate group of the ligands play an important role in the ligand receptor binding but higher stabilization of different helices close to the binding site of FXR (e.g., H5, H6 and H8) through aromatic scaffolds of the ligands should lead to higher activity for these ligands. The present work affords important guidelines towards designing novel FXR partial agonists for new therapeutic options in the management of NAFLD. Moreover, we relied mainly on open-access tools to develop the in-silico models in order to ensure their reproducibility as well as utilization.
[Display omitted]
•Partial agonism of FXR is a promising strategy for the treatment of NAFLD.•A series of 3-benzamidobenzoic acid derivatives were used for in silico analyses.•2D- and 3D-QSAR provided crucial information regarding structural requirements.•Ligand-based pharmacophore mapping highlighted important pharmacophore features.•MD simulation depicted crucial ligand-receptor interactions for higher activity.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>36963353</pmid><doi>10.1016/j.compbiomed.2023.106789</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-0664-0389</orcidid><orcidid>https://orcid.org/0000-0003-3375-8670</orcidid><orcidid>https://orcid.org/0000-0003-0520-0952</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0010-4825 |
ispartof | Computers in biology and medicine, 2023-05, Vol.157, p.106789-106789, Article 106789 |
issn | 0010-4825 1879-0534 |
language | eng |
recordid | cdi_proquest_miscellaneous_2791369157 |
source | MEDLINE; Elsevier ScienceDirect Journals Complete; ProQuest Central UK/Ireland |
subjects | Agonists Amino acids Bile Binding sites Biological activity Cholesterol Datasets Farnesoid X receptor Fatty liver Feature selection Genetic algorithms Health hazards Helices Humans Hydrogen bonds Informatics Insulin resistance Ligands Liver Liver cancer Liver cirrhosis Liver diseases Metabolic disorders Molecular Docking Simulation Molecular dynamics Molecular Dynamics Simulation Molecular modelling Non-alcoholic fatty liver disease Non-alcoholic Fatty Liver Disease - drug therapy Pharmacology Pharmacophore mapping QSAR Quantitative Structure-Activity Relationship Receptors Reproducibility of Results RNA-Binding Proteins - antagonists & inhibitors RNA-Binding Proteins - metabolism Structure-activity relationships Three dimensional models Two dimensional models |
title | Multi-model in silico characterization of 3-benzamidobenzoic acid derivatives as partial agonists of Farnesoid X receptor in the management of NAFLD |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T15%3A12%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multi-model%20in%20silico%20characterization%20of%203-benzamidobenzoic%20acid%20derivatives%20as%20partial%20agonists%20of%20Farnesoid%20X%20receptor%20in%20the%20management%20of%20NAFLD&rft.jtitle=Computers%20in%20biology%20and%20medicine&rft.au=Mitra,%20Soumya&rft.date=2023-05&rft.volume=157&rft.spage=106789&rft.epage=106789&rft.pages=106789-106789&rft.artnum=106789&rft.issn=0010-4825&rft.eissn=1879-0534&rft_id=info:doi/10.1016/j.compbiomed.2023.106789&rft_dat=%3Cproquest_cross%3E2791369157%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2791586300&rft_id=info:pmid/36963353&rft_els_id=S0010482523002548&rfr_iscdi=true |