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Large‐scale survey and database of high affinity ligands for peptide recognition modules
Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct struct...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724964/ https://www.ncbi.nlm.nih.gov/pubmed/33438817 http://dx.doi.org/10.15252/msb.20199310 |
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author | Teyra, Joan Kelil, Abdellali Jain, Shobhit Helmy, Mohamed Jajodia, Raghav Hooda, Yogesh Gu, Jun D’Cruz, Akshay A Nicholson, Sandra E Min, Jinrong Sudol, Marius Kim, Philip M Bader, Gary D Sidhu, Sachdev S |
author_facet | Teyra, Joan Kelil, Abdellali Jain, Shobhit Helmy, Mohamed Jajodia, Raghav Hooda, Yogesh Gu, Jun D’Cruz, Akshay A Nicholson, Sandra E Min, Jinrong Sudol, Marius Kim, Philip M Bader, Gary D Sidhu, Sachdev S |
author_sort | Teyra, Joan |
collection | PubMed |
description | Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs. |
format | Online Article Text |
id | pubmed-7724964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77249642020-12-11 Large‐scale survey and database of high affinity ligands for peptide recognition modules Teyra, Joan Kelil, Abdellali Jain, Shobhit Helmy, Mohamed Jajodia, Raghav Hooda, Yogesh Gu, Jun D’Cruz, Akshay A Nicholson, Sandra E Min, Jinrong Sudol, Marius Kim, Philip M Bader, Gary D Sidhu, Sachdev S Mol Syst Biol Articles Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs. John Wiley and Sons Inc. 2020-12-08 /pmc/articles/PMC7724964/ /pubmed/33438817 http://dx.doi.org/10.15252/msb.20199310 Text en © 2020 The Authors. Published under the terms of the CC BY 4.0 license This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Teyra, Joan Kelil, Abdellali Jain, Shobhit Helmy, Mohamed Jajodia, Raghav Hooda, Yogesh Gu, Jun D’Cruz, Akshay A Nicholson, Sandra E Min, Jinrong Sudol, Marius Kim, Philip M Bader, Gary D Sidhu, Sachdev S Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title | Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title_full | Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title_fullStr | Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title_full_unstemmed | Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title_short | Large‐scale survey and database of high affinity ligands for peptide recognition modules |
title_sort | large‐scale survey and database of high affinity ligands for peptide recognition modules |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7724964/ https://www.ncbi.nlm.nih.gov/pubmed/33438817 http://dx.doi.org/10.15252/msb.20199310 |
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