Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking
Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type-II arginine enzyme crucial for many cellular processes and associated with many cancer diseases. In this study, 2D QSAR study, pharmacophore-based 3D QSAR modeling, virtual screening, and binding free e...
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Veröffentlicht in: | Structural chemistry 2022-08, Vol.33 (4), p.1223-1239 |
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description | Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type-II arginine enzyme crucial for many cellular processes and associated with many cancer diseases. In this study, 2D QSAR study, pharmacophore-based 3D QSAR modeling, virtual screening, and binding free energy studies were carried out from a set of 61 reported potent compounds being inhibitors of PRMT5 protein. A five-point pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-dimensional quantitative structure–activity relationship (3D QSAR). The obtained 3D QSAR model has better correlation coefficient (
R
2
= 0.91), cross-validation coefficient (
Q
2
= 0.82),
F
value (140.3), low RMSE (0.47), and Pearson
R
value (0.91). A library of 329,825 molecules (ChEMBL database) is screened with the pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Furthermore, we calculated ADME and toxicity properties using the QikProp module and pkCSM server. The lead molecules were prioritized by glide scores and binding free energy studies. Finally, we have evaluated the stability of protein–ligand complexes by performing an all-atom MD simulation in explicit solvent. MD results further strengthen our findings. |
doi_str_mv | 10.1007/s11224-022-01918-y |
format | Article |
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R
2
= 0.91), cross-validation coefficient (
Q
2
= 0.82),
F
value (140.3), low RMSE (0.47), and Pearson
R
value (0.91). A library of 329,825 molecules (ChEMBL database) is screened with the pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Furthermore, we calculated ADME and toxicity properties using the QikProp module and pkCSM server. The lead molecules were prioritized by glide scores and binding free energy studies. Finally, we have evaluated the stability of protein–ligand complexes by performing an all-atom MD simulation in explicit solvent. MD results further strengthen our findings.</description><identifier>ISSN: 1040-0400</identifier><identifier>EISSN: 1572-9001</identifier><identifier>DOI: 10.1007/s11224-022-01918-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Binding energy ; Cancer ; Chemistry ; Chemistry and Materials Science ; Computer Applications in Chemistry ; Coordination compounds ; Correlation coefficients ; Force and energy ; Free energy ; Methyltransferases ; Molecular docking ; Oncology, Experimental ; Original Research ; Pharmacology ; Physical Chemistry ; Proteins ; Screening ; Stability analysis ; Theoretical and Computational Chemistry ; Three dimensional models ; Toxicity</subject><ispartof>Structural chemistry, 2022-08, Vol.33 (4), p.1223-1239</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</rights><rights>COPYRIGHT 2022 Springer</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-83b6746c93b26f763660a10d06fa7cff5abd9c691ed3998e9c6782a1e8b7da6f3</citedby><cites>FETCH-LOGICAL-c360t-83b6746c93b26f763660a10d06fa7cff5abd9c691ed3998e9c6782a1e8b7da6f3</cites><orcidid>0000-0002-6453-9705</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11224-022-01918-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11224-022-01918-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Bathula, Revanth</creatorcontrib><creatorcontrib>Lanka, Goverdhan</creatorcontrib><creatorcontrib>Chakravarty, Madhulika</creatorcontrib><creatorcontrib>Somadi, Gururaj</creatorcontrib><creatorcontrib>Sivan, Sree Kanth</creatorcontrib><creatorcontrib>Jain, Alok</creatorcontrib><creatorcontrib>Potlapally, Sarita Rajender</creatorcontrib><title>Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking</title><title>Structural chemistry</title><addtitle>Struct Chem</addtitle><description>Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type-II arginine enzyme crucial for many cellular processes and associated with many cancer diseases. In this study, 2D QSAR study, pharmacophore-based 3D QSAR modeling, virtual screening, and binding free energy studies were carried out from a set of 61 reported potent compounds being inhibitors of PRMT5 protein. A five-point pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-dimensional quantitative structure–activity relationship (3D QSAR). The obtained 3D QSAR model has better correlation coefficient (
R
2
= 0.91), cross-validation coefficient (
Q
2
= 0.82),
F
value (140.3), low RMSE (0.47), and Pearson
R
value (0.91). A library of 329,825 molecules (ChEMBL database) is screened with the pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Furthermore, we calculated ADME and toxicity properties using the QikProp module and pkCSM server. The lead molecules were prioritized by glide scores and binding free energy studies. Finally, we have evaluated the stability of protein–ligand complexes by performing an all-atom MD simulation in explicit solvent. MD results further strengthen our findings.</description><subject>Binding energy</subject><subject>Cancer</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Computer Applications in Chemistry</subject><subject>Coordination compounds</subject><subject>Correlation coefficients</subject><subject>Force and energy</subject><subject>Free energy</subject><subject>Methyltransferases</subject><subject>Molecular docking</subject><subject>Oncology, Experimental</subject><subject>Original Research</subject><subject>Pharmacology</subject><subject>Physical Chemistry</subject><subject>Proteins</subject><subject>Screening</subject><subject>Stability analysis</subject><subject>Theoretical and Computational Chemistry</subject><subject>Three dimensional models</subject><subject>Toxicity</subject><issn>1040-0400</issn><issn>1572-9001</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9UV2L1TAULKLgevUP-BTwdbPmo03bx8u6q8Iuiq7PIU1O2qxtck1StX_O32bWK6yCSEjOcJiZc8hU1XNKzigh7ctEKWM1JoxhQnva4e1BdUKbluGeEPqwYFITXC55XD1J6bY0qeDNSfXjY46rzmtUM3I-uXHKpeaA3n-4vmkKnNzgcogJ5SmGdZyQWtQ8lic7P6LDpOKidDhMIQIeVAKDvrqY12KXdATwhXWK9q-uL1AO3512eTtFyhs0OG_uHMBDHDeU8moclCkBOQM-O7shD9_-XGDY0BJm0OusIjJBfy7yp9Ujq-YEz37XXfXp8uLm_A2-evf67fn-CmsuSMYdH0RbC93zgQnbCi4EUZQYIqxqtbWNGkyvRU_B8L7voOC2Y4pCN7RGCct31Yuj7yGGLyukLG_DGn0ZKZko1o2ou_qeNaoZpPM25Kj04pKW-5bUgjNefn1Xnf2DVY6BxengwbrS_0vAjgIdQ0oRrDxEt6i4SUrkXfzyGL8s8ctf8cutiPhRlArZjxDvN_6P6ie6kbbZ</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Bathula, Revanth</creator><creator>Lanka, Goverdhan</creator><creator>Chakravarty, Madhulika</creator><creator>Somadi, Gururaj</creator><creator>Sivan, Sree Kanth</creator><creator>Jain, Alok</creator><creator>Potlapally, Sarita Rajender</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-6453-9705</orcidid></search><sort><creationdate>20220801</creationdate><title>Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking</title><author>Bathula, Revanth ; Lanka, Goverdhan ; Chakravarty, Madhulika ; Somadi, Gururaj ; Sivan, Sree Kanth ; Jain, Alok ; Potlapally, Sarita Rajender</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-83b6746c93b26f763660a10d06fa7cff5abd9c691ed3998e9c6782a1e8b7da6f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Binding energy</topic><topic>Cancer</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Computer Applications in Chemistry</topic><topic>Coordination compounds</topic><topic>Correlation coefficients</topic><topic>Force and energy</topic><topic>Free energy</topic><topic>Methyltransferases</topic><topic>Molecular docking</topic><topic>Oncology, Experimental</topic><topic>Original Research</topic><topic>Pharmacology</topic><topic>Physical Chemistry</topic><topic>Proteins</topic><topic>Screening</topic><topic>Stability analysis</topic><topic>Theoretical and Computational Chemistry</topic><topic>Three dimensional models</topic><topic>Toxicity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bathula, Revanth</creatorcontrib><creatorcontrib>Lanka, Goverdhan</creatorcontrib><creatorcontrib>Chakravarty, Madhulika</creatorcontrib><creatorcontrib>Somadi, Gururaj</creatorcontrib><creatorcontrib>Sivan, Sree Kanth</creatorcontrib><creatorcontrib>Jain, Alok</creatorcontrib><creatorcontrib>Potlapally, Sarita Rajender</creatorcontrib><collection>CrossRef</collection><jtitle>Structural chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bathula, Revanth</au><au>Lanka, Goverdhan</au><au>Chakravarty, Madhulika</au><au>Somadi, Gururaj</au><au>Sivan, Sree Kanth</au><au>Jain, Alok</au><au>Potlapally, Sarita Rajender</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking</atitle><jtitle>Structural chemistry</jtitle><stitle>Struct Chem</stitle><date>2022-08-01</date><risdate>2022</risdate><volume>33</volume><issue>4</issue><spage>1223</spage><epage>1239</epage><pages>1223-1239</pages><issn>1040-0400</issn><eissn>1572-9001</eissn><abstract>Protein arginine methyltransferase 5 (PRMT5) is a member of the methyltransferases family, a type-II arginine enzyme crucial for many cellular processes and associated with many cancer diseases. In this study, 2D QSAR study, pharmacophore-based 3D QSAR modeling, virtual screening, and binding free energy studies were carried out from a set of 61 reported potent compounds being inhibitors of PRMT5 protein. A five-point pharmacophore model (AADHR) was generated and this model is used to generate an atom-based 3-dimensional quantitative structure–activity relationship (3D QSAR). The obtained 3D QSAR model has better correlation coefficient (
R
2
= 0.91), cross-validation coefficient (
Q
2
= 0.82),
F
value (140.3), low RMSE (0.47), and Pearson
R
value (0.91). A library of 329,825 molecules (ChEMBL database) is screened with the pharmacophore model to retrieve hit molecules that are further subjected for molecular docking to identify best fit-active conformations binding at the receptor site of PRMT5 protein. Furthermore, we calculated ADME and toxicity properties using the QikProp module and pkCSM server. The lead molecules were prioritized by glide scores and binding free energy studies. Finally, we have evaluated the stability of protein–ligand complexes by performing an all-atom MD simulation in explicit solvent. MD results further strengthen our findings.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11224-022-01918-y</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-6453-9705</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Binding energy Cancer Chemistry Chemistry and Materials Science Computer Applications in Chemistry Coordination compounds Correlation coefficients Force and energy Free energy Methyltransferases Molecular docking Oncology, Experimental Original Research Pharmacology Physical Chemistry Proteins Screening Stability analysis Theoretical and Computational Chemistry Three dimensional models Toxicity |
title | Structural insight into PRMT5 inhibitors through amalgamating pharmacophore-based virtual screening, ADME toxicity, and binding energy studies to identify new inhibitors by molecular docking |
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