Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches
Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation−π and amino−π interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on th...
Gespeichert in:
Veröffentlicht in: | Journal of chemical information and modeling 2006-03, Vol.46 (2), p.884-893 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 893 |
---|---|
container_issue | 2 |
container_start_page | 884 |
container_title | Journal of chemical information and modeling |
container_volume | 46 |
creator | Gilis, Dimitri Biot, Christophe Buisine, Eric Dehouck, Yves Rooman, Marianne |
description | Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation−π and amino−π interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on the propensity of residue types to be separated by a certain spatial distance or to have a given relative orientation. Several such potentials, describing different kinds of correlations between residue types, distances, and orientations, are derived and combined in a way that maximizes their information content and minimizes their redundancy. To test the ability of these potentials to describe cation−π and amino−π systems, we compare their energies with those computed with the CHARMM molecular mechanics force field and with quantum chemistry calculations at the Hartree−Fock level (HF) and at the second order of the Møller−Plesset perturbation theory (MP2). The latter calculations are performed in the gas phase and in acetone, in order to mimic the average dielectric constant of protein environments. The energies computed with the best of our statistical potentials and with gas-phase HF or MP2 show correlation coefficients up to 0.96 when considering one side-chain degree of freedom in the statistical potentials and up to 0.94 when using a totally simplified model excluding all side-chain degrees of freedom. These potentials perform as well as, or better than, the CHARMM molecular mechanics force field that uses a much more detailed protein representation. The good performance of our cation−π statistical potentials suggests their utility in protein structure and stability prediction and in protein design. |
doi_str_mv | 10.1021/ci050395b |
format | Article |
fullrecord | <record><control><sourceid>proquest_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_00100309v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>67791691</sourcerecordid><originalsourceid>FETCH-LOGICAL-a412t-aed15f227d23c022250fec2b9be6841b69cef60a8abedce1dc63aa38475d21593</originalsourceid><addsrcrecordid>eNplkUuO1DAQhiMEYh6w4ALIQgKJRcCPxGkvm8zAjNSChh4EO8txKrSHJA62MzA7lqw5AFfiDpwEh266JVi5XPW5_t9VSXKP4CcEU_JUG5xjJvLqRnJI8kykguP3N__GueAHyZH3lxgzJji9nRwQnnOGKT5MfpzAFbR26KAPyDbopY1XtAoqGB-MVi1a2hBrRrUenYDXzlSm_4DKCNj-17fvP7-i8z6AU3pKeGR6tHTxiYmx6mtU2m5Qznjbo88mrNEKOgPdYNyf5hPxelR9GDtUrmPJB3eN5sPgrNJr8HeSW01Uhrvb8zh5-_z0ojxLF69enJfzRaoyQkOqoCZ5Q2lRU6YxpTTHDWhaiQr4LCMVFxoajtVMVVBrILXmTCk2y4q8piQX7Dh5vOm7Vq0cnOmUu5ZWGXk2X8gphzGJ48PiikT20YaNJj-N4IOMtjW0rerBjl7yohCEiwl88A94aUfXx39ISjjNqGBsr6yd9d5Bs5MnWE7blbvtRvb-tuFYdVDvye06I5BugDhH-LKrK_cxumJFLi-WK_mOviEie7aSPPIPN7zSfm_uf-HfjAK-iQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>216242933</pqid></control><display><type>article</type><title>Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches</title><source>MEDLINE</source><source>American Chemical Society Journals</source><creator>Gilis, Dimitri ; Biot, Christophe ; Buisine, Eric ; Dehouck, Yves ; Rooman, Marianne</creator><creatorcontrib>Gilis, Dimitri ; Biot, Christophe ; Buisine, Eric ; Dehouck, Yves ; Rooman, Marianne</creatorcontrib><description>Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation−π and amino−π interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on the propensity of residue types to be separated by a certain spatial distance or to have a given relative orientation. Several such potentials, describing different kinds of correlations between residue types, distances, and orientations, are derived and combined in a way that maximizes their information content and minimizes their redundancy. To test the ability of these potentials to describe cation−π and amino−π systems, we compare their energies with those computed with the CHARMM molecular mechanics force field and with quantum chemistry calculations at the Hartree−Fock level (HF) and at the second order of the Møller−Plesset perturbation theory (MP2). The latter calculations are performed in the gas phase and in acetone, in order to mimic the average dielectric constant of protein environments. The energies computed with the best of our statistical potentials and with gas-phase HF or MP2 show correlation coefficients up to 0.96 when considering one side-chain degree of freedom in the statistical potentials and up to 0.94 when using a totally simplified model excluding all side-chain degrees of freedom. These potentials perform as well as, or better than, the CHARMM molecular mechanics force field that uses a much more detailed protein representation. The good performance of our cation−π statistical potentials suggests their utility in protein structure and stability prediction and in protein design.</description><identifier>ISSN: 1549-9596</identifier><identifier>EISSN: 1549-960X</identifier><identifier>DOI: 10.1021/ci050395b</identifier><identifier>PMID: 16563020</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Algorithms ; Cations - chemistry ; Chemical Sciences ; Chemistry ; Databases as Topic ; Ions ; Medicinal Chemistry ; or physical chemistry ; Protein Binding ; Protein Structure, Tertiary ; Proteins ; Proteins - chemistry ; Quantum Theory ; Statistical analysis ; Theoretical and</subject><ispartof>Journal of chemical information and modeling, 2006-03, Vol.46 (2), p.884-893</ispartof><rights>Copyright © 2006 American Chemical Society</rights><rights>Copyright American Chemical Society Mar 2006</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a412t-aed15f227d23c022250fec2b9be6841b69cef60a8abedce1dc63aa38475d21593</citedby><cites>FETCH-LOGICAL-a412t-aed15f227d23c022250fec2b9be6841b69cef60a8abedce1dc63aa38475d21593</cites><orcidid>0000-0002-7396-1959</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://pubs.acs.org/doi/pdf/10.1021/ci050395b$$EPDF$$P50$$Gacs$$H</linktopdf><linktohtml>$$Uhttps://pubs.acs.org/doi/10.1021/ci050395b$$EHTML$$P50$$Gacs$$H</linktohtml><link.rule.ids>230,314,776,780,881,2751,27055,27903,27904,56716,56766</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16563020$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00100309$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Gilis, Dimitri</creatorcontrib><creatorcontrib>Biot, Christophe</creatorcontrib><creatorcontrib>Buisine, Eric</creatorcontrib><creatorcontrib>Dehouck, Yves</creatorcontrib><creatorcontrib>Rooman, Marianne</creatorcontrib><title>Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches</title><title>Journal of chemical information and modeling</title><addtitle>J. Chem. Inf. Model</addtitle><description>Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation−π and amino−π interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on the propensity of residue types to be separated by a certain spatial distance or to have a given relative orientation. Several such potentials, describing different kinds of correlations between residue types, distances, and orientations, are derived and combined in a way that maximizes their information content and minimizes their redundancy. To test the ability of these potentials to describe cation−π and amino−π systems, we compare their energies with those computed with the CHARMM molecular mechanics force field and with quantum chemistry calculations at the Hartree−Fock level (HF) and at the second order of the Møller−Plesset perturbation theory (MP2). The latter calculations are performed in the gas phase and in acetone, in order to mimic the average dielectric constant of protein environments. The energies computed with the best of our statistical potentials and with gas-phase HF or MP2 show correlation coefficients up to 0.96 when considering one side-chain degree of freedom in the statistical potentials and up to 0.94 when using a totally simplified model excluding all side-chain degrees of freedom. These potentials perform as well as, or better than, the CHARMM molecular mechanics force field that uses a much more detailed protein representation. The good performance of our cation−π statistical potentials suggests their utility in protein structure and stability prediction and in protein design.</description><subject>Algorithms</subject><subject>Cations - chemistry</subject><subject>Chemical Sciences</subject><subject>Chemistry</subject><subject>Databases as Topic</subject><subject>Ions</subject><subject>Medicinal Chemistry</subject><subject>or physical chemistry</subject><subject>Protein Binding</subject><subject>Protein Structure, Tertiary</subject><subject>Proteins</subject><subject>Proteins - chemistry</subject><subject>Quantum Theory</subject><subject>Statistical analysis</subject><subject>Theoretical and</subject><issn>1549-9596</issn><issn>1549-960X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNplkUuO1DAQhiMEYh6w4ALIQgKJRcCPxGkvm8zAjNSChh4EO8txKrSHJA62MzA7lqw5AFfiDpwEh266JVi5XPW5_t9VSXKP4CcEU_JUG5xjJvLqRnJI8kykguP3N__GueAHyZH3lxgzJji9nRwQnnOGKT5MfpzAFbR26KAPyDbopY1XtAoqGB-MVi1a2hBrRrUenYDXzlSm_4DKCNj-17fvP7-i8z6AU3pKeGR6tHTxiYmx6mtU2m5Qznjbo88mrNEKOgPdYNyf5hPxelR9GDtUrmPJB3eN5sPgrNJr8HeSW01Uhrvb8zh5-_z0ojxLF69enJfzRaoyQkOqoCZ5Q2lRU6YxpTTHDWhaiQr4LCMVFxoajtVMVVBrILXmTCk2y4q8piQX7Dh5vOm7Vq0cnOmUu5ZWGXk2X8gphzGJ48PiikT20YaNJj-N4IOMtjW0rerBjl7yohCEiwl88A94aUfXx39ISjjNqGBsr6yd9d5Bs5MnWE7blbvtRvb-tuFYdVDvye06I5BugDhH-LKrK_cxumJFLi-WK_mOviEie7aSPPIPN7zSfm_uf-HfjAK-iQ</recordid><startdate>20060301</startdate><enddate>20060301</enddate><creator>Gilis, Dimitri</creator><creator>Biot, Christophe</creator><creator>Buisine, Eric</creator><creator>Dehouck, Yves</creator><creator>Rooman, Marianne</creator><general>American Chemical Society</general><scope>BSCLL</scope><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>7SC</scope><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>1XC</scope><orcidid>https://orcid.org/0000-0002-7396-1959</orcidid></search><sort><creationdate>20060301</creationdate><title>Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches</title><author>Gilis, Dimitri ; Biot, Christophe ; Buisine, Eric ; Dehouck, Yves ; Rooman, Marianne</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a412t-aed15f227d23c022250fec2b9be6841b69cef60a8abedce1dc63aa38475d21593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Cations - chemistry</topic><topic>Chemical Sciences</topic><topic>Chemistry</topic><topic>Databases as Topic</topic><topic>Ions</topic><topic>Medicinal Chemistry</topic><topic>or physical chemistry</topic><topic>Protein Binding</topic><topic>Protein Structure, Tertiary</topic><topic>Proteins</topic><topic>Proteins - chemistry</topic><topic>Quantum Theory</topic><topic>Statistical analysis</topic><topic>Theoretical and</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gilis, Dimitri</creatorcontrib><creatorcontrib>Biot, Christophe</creatorcontrib><creatorcontrib>Buisine, Eric</creatorcontrib><creatorcontrib>Dehouck, Yves</creatorcontrib><creatorcontrib>Rooman, Marianne</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Journal of chemical information and modeling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gilis, Dimitri</au><au>Biot, Christophe</au><au>Buisine, Eric</au><au>Dehouck, Yves</au><au>Rooman, Marianne</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches</atitle><jtitle>Journal of chemical information and modeling</jtitle><addtitle>J. Chem. Inf. Model</addtitle><date>2006-03-01</date><risdate>2006</risdate><volume>46</volume><issue>2</issue><spage>884</spage><epage>893</epage><pages>884-893</pages><issn>1549-9596</issn><eissn>1549-960X</eissn><abstract>Novel statistical potentials derived from known protein structures are presented. They are designed to describe cation−π and amino−π interactions between a positively charged amino acid or an amino acid carrying a partially charged amino group and an aromatic moiety. These potentials are based on the propensity of residue types to be separated by a certain spatial distance or to have a given relative orientation. Several such potentials, describing different kinds of correlations between residue types, distances, and orientations, are derived and combined in a way that maximizes their information content and minimizes their redundancy. To test the ability of these potentials to describe cation−π and amino−π systems, we compare their energies with those computed with the CHARMM molecular mechanics force field and with quantum chemistry calculations at the Hartree−Fock level (HF) and at the second order of the Møller−Plesset perturbation theory (MP2). The latter calculations are performed in the gas phase and in acetone, in order to mimic the average dielectric constant of protein environments. The energies computed with the best of our statistical potentials and with gas-phase HF or MP2 show correlation coefficients up to 0.96 when considering one side-chain degree of freedom in the statistical potentials and up to 0.94 when using a totally simplified model excluding all side-chain degrees of freedom. These potentials perform as well as, or better than, the CHARMM molecular mechanics force field that uses a much more detailed protein representation. The good performance of our cation−π statistical potentials suggests their utility in protein structure and stability prediction and in protein design.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>16563020</pmid><doi>10.1021/ci050395b</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-7396-1959</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1549-9596 |
ispartof | Journal of chemical information and modeling, 2006-03, Vol.46 (2), p.884-893 |
issn | 1549-9596 1549-960X |
language | eng |
recordid | cdi_hal_primary_oai_HAL_hal_00100309v1 |
source | MEDLINE; American Chemical Society Journals |
subjects | Algorithms Cations - chemistry Chemical Sciences Chemistry Databases as Topic Ions Medicinal Chemistry or physical chemistry Protein Binding Protein Structure, Tertiary Proteins Proteins - chemistry Quantum Theory Statistical analysis Theoretical and |
title | Development of Novel Statistical Potentials Describing Cation−π Interactions in Proteins and Comparison with Semiempirical and Quantum Chemistry Approaches |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T10%3A56%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20of%20Novel%20Statistical%20Potentials%20Describing%20Cation%E2%88%92%CF%80%20Interactions%20in%20Proteins%20and%20Comparison%20with%20Semiempirical%20and%20Quantum%20Chemistry%20Approaches&rft.jtitle=Journal%20of%20chemical%20information%20and%20modeling&rft.au=Gilis,%20Dimitri&rft.date=2006-03-01&rft.volume=46&rft.issue=2&rft.spage=884&rft.epage=893&rft.pages=884-893&rft.issn=1549-9596&rft.eissn=1549-960X&rft_id=info:doi/10.1021/ci050395b&rft_dat=%3Cproquest_hal_p%3E67791691%3C/proquest_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=216242933&rft_id=info:pmid/16563020&rfr_iscdi=true |