Loading…

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) me...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemical information and modeling 2022-02, Vol.62 (3), p.577-590
Main Authors: Crean, Rory M., Pudney, Christopher R., Cole, David K., van der Kamp, Marc W.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3
cites cdi_FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3
container_end_page 590
container_issue 3
container_start_page 577
container_title Journal of chemical information and modeling
container_volume 62
creator Crean, Rory M.
Pudney, Christopher R.
Cole, David K.
van der Kamp, Marc W.
description Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.
doi_str_mv 10.1021/acs.jcim.1c00765
format article
fullrecord <record><control><sourceid>proquest_swepu</sourceid><recordid>TN_cdi_swepub_primary_oai_DiVA_org_uu_470562</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2621660740</sourcerecordid><originalsourceid>FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3</originalsourceid><addsrcrecordid>eNp1kc9v0zAYhiPExH7AnROyxIUD7ezYseMLUlvGNmkTqCsIcbEc53PrkjrFTjbx389d28GQONmSn_e1_T1Z9prgIcE5OdUmDpfGrYbEYCx48Sw7IgWTA8nx9-f7fSH5YXYc4xJjSiXPX2SHtMBMUpIfZT-m0DhdNYAuPbpxjTMtmmr_0_k5ai0683PnAQLUaLaAoNfQd86g2WSKxs7XG2pkrfOucxDRnesW6Pr6y_j0fHwzepkdWN1EeLVbT7Kvn85mk4vB1efzy8noaqCZLLsB17zSBgpraih5xWXNBDOVzKmWJbElrfPaipoL4FZoRqiwCbcMgFZguaYn2fttb7yDdV-pdXArHX6rVjv10X0bqTbMVd8rJnDB84R_2OKJXUFtwHdBN09ST0-8W6h5e6skloIUNBW82xWE9lcPsVMrFw00jfbQ9lHlPCecY8FwQt_-gy7bPvg0jURRLBktSp4ovKVMaGMMYB8fQ7DaeFbJs9p4VjvPKfLm7088BvZi_wzlIbq_9L999yqatS0</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2630943586</pqid></control><display><type>article</type><title>Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA</title><source>American Chemical Society:Jisc Collections:American Chemical Society Read &amp; Publish Agreement 2022-2024 (Reading list)</source><creator>Crean, Rory M. ; Pudney, Christopher R. ; Cole, David K. ; van der Kamp, Marc W.</creator><creatorcontrib>Crean, Rory M. ; Pudney, Christopher R. ; Cole, David K. ; van der Kamp, Marc W.</creatorcontrib><description>Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.</description><identifier>ISSN: 1549-9596</identifier><identifier>ISSN: 1549-960X</identifier><identifier>EISSN: 1549-960X</identifier><identifier>DOI: 10.1021/acs.jcim.1c00765</identifier><identifier>PMID: 35049312</identifier><language>eng</language><publisher>United States: American Chemical Society</publisher><subject>Affinity ; Antigens ; Binding ; Computational Biochemistry ; Entropy ; Humans ; Leukocytes ; Ligands ; Molecular dynamics ; Molecular Dynamics Simulation ; Mutation ; Optimization ; Protein Binding ; Proteins ; Proteins - chemistry ; Ranking</subject><ispartof>Journal of chemical information and modeling, 2022-02, Vol.62 (3), p.577-590</ispartof><rights>2022 American Chemical Society</rights><rights>Copyright American Chemical Society Feb 14, 2022</rights><rights>2022 American Chemical Society 2022 American Chemical Society</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3</citedby><cites>FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3</cites><orcidid>0000-0002-8060-3359 ; 0000-0001-6211-0086</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35049312$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-470562$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Crean, Rory M.</creatorcontrib><creatorcontrib>Pudney, Christopher R.</creatorcontrib><creatorcontrib>Cole, David K.</creatorcontrib><creatorcontrib>van der Kamp, Marc W.</creatorcontrib><title>Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA</title><title>Journal of chemical information and modeling</title><addtitle>J. Chem. Inf. Model</addtitle><description>Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.</description><subject>Affinity</subject><subject>Antigens</subject><subject>Binding</subject><subject>Computational Biochemistry</subject><subject>Entropy</subject><subject>Humans</subject><subject>Leukocytes</subject><subject>Ligands</subject><subject>Molecular dynamics</subject><subject>Molecular Dynamics Simulation</subject><subject>Mutation</subject><subject>Optimization</subject><subject>Protein Binding</subject><subject>Proteins</subject><subject>Proteins - chemistry</subject><subject>Ranking</subject><issn>1549-9596</issn><issn>1549-960X</issn><issn>1549-960X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp1kc9v0zAYhiPExH7AnROyxIUD7ezYseMLUlvGNmkTqCsIcbEc53PrkjrFTjbx389d28GQONmSn_e1_T1Z9prgIcE5OdUmDpfGrYbEYCx48Sw7IgWTA8nx9-f7fSH5YXYc4xJjSiXPX2SHtMBMUpIfZT-m0DhdNYAuPbpxjTMtmmr_0_k5ai0683PnAQLUaLaAoNfQd86g2WSKxs7XG2pkrfOucxDRnesW6Pr6y_j0fHwzepkdWN1EeLVbT7Kvn85mk4vB1efzy8noaqCZLLsB17zSBgpraih5xWXNBDOVzKmWJbElrfPaipoL4FZoRqiwCbcMgFZguaYn2fttb7yDdV-pdXArHX6rVjv10X0bqTbMVd8rJnDB84R_2OKJXUFtwHdBN09ST0-8W6h5e6skloIUNBW82xWE9lcPsVMrFw00jfbQ9lHlPCecY8FwQt_-gy7bPvg0jURRLBktSp4ovKVMaGMMYB8fQ7DaeFbJs9p4VjvPKfLm7088BvZi_wzlIbq_9L999yqatS0</recordid><startdate>20220214</startdate><enddate>20220214</enddate><creator>Crean, Rory M.</creator><creator>Pudney, Christopher R.</creator><creator>Cole, David K.</creator><creator>van der Kamp, Marc W.</creator><general>American Chemical Society</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>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>5PM</scope><scope>ACNBI</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DF2</scope><scope>ZZAVC</scope><orcidid>https://orcid.org/0000-0002-8060-3359</orcidid><orcidid>https://orcid.org/0000-0001-6211-0086</orcidid></search><sort><creationdate>20220214</creationdate><title>Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA</title><author>Crean, Rory M. ; Pudney, Christopher R. ; Cole, David K. ; van der Kamp, Marc W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Affinity</topic><topic>Antigens</topic><topic>Binding</topic><topic>Computational Biochemistry</topic><topic>Entropy</topic><topic>Humans</topic><topic>Leukocytes</topic><topic>Ligands</topic><topic>Molecular dynamics</topic><topic>Molecular Dynamics Simulation</topic><topic>Mutation</topic><topic>Optimization</topic><topic>Protein Binding</topic><topic>Proteins</topic><topic>Proteins - chemistry</topic><topic>Ranking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Crean, Rory M.</creatorcontrib><creatorcontrib>Pudney, Christopher R.</creatorcontrib><creatorcontrib>Cole, David K.</creatorcontrib><creatorcontrib>van der Kamp, Marc W.</creatorcontrib><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>PubMed Central (Full Participant titles)</collection><collection>SWEPUB Uppsala universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Uppsala universitet</collection><collection>SwePub Articles full text</collection><jtitle>Journal of chemical information and modeling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Crean, Rory M.</au><au>Pudney, Christopher R.</au><au>Cole, David K.</au><au>van der Kamp, Marc W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA</atitle><jtitle>Journal of chemical information and modeling</jtitle><addtitle>J. Chem. Inf. Model</addtitle><date>2022-02-14</date><risdate>2022</risdate><volume>62</volume><issue>3</issue><spage>577</spage><epage>590</epage><pages>577-590</pages><issn>1549-9596</issn><issn>1549-960X</issn><eissn>1549-960X</eissn><abstract>Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.</abstract><cop>United States</cop><pub>American Chemical Society</pub><pmid>35049312</pmid><doi>10.1021/acs.jcim.1c00765</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-8060-3359</orcidid><orcidid>https://orcid.org/0000-0001-6211-0086</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1549-9596
ispartof Journal of chemical information and modeling, 2022-02, Vol.62 (3), p.577-590
issn 1549-9596
1549-960X
1549-960X
language eng
recordid cdi_swepub_primary_oai_DiVA_org_uu_470562
source American Chemical Society:Jisc Collections:American Chemical Society Read & Publish Agreement 2022-2024 (Reading list)
subjects Affinity
Antigens
Binding
Computational Biochemistry
Entropy
Humans
Leukocytes
Ligands
Molecular dynamics
Molecular Dynamics Simulation
Mutation
Optimization
Protein Binding
Proteins
Proteins - chemistry
Ranking
title Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T06%3A20%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Reliable%20In%20Silico%20Ranking%20of%20Engineered%20Therapeutic%20TCR%20Binding%20Affinities%20with%20MMPB/GBSA&rft.jtitle=Journal%20of%20chemical%20information%20and%20modeling&rft.au=Crean,%20Rory%20M.&rft.date=2022-02-14&rft.volume=62&rft.issue=3&rft.spage=577&rft.epage=590&rft.pages=577-590&rft.issn=1549-9596&rft.eissn=1549-960X&rft_id=info:doi/10.1021/acs.jcim.1c00765&rft_dat=%3Cproquest_swepu%3E2621660740%3C/proquest_swepu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a498t-6a6bace5fcde86b69d474cb923a981f83d2df7d67e6f7a4137facef4ee3bef6a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2630943586&rft_id=info:pmid/35049312&rfr_iscdi=true