SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands
Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein–protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants o...
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| Format: | Article |
| Language: | en_US |
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Elsevier
2017
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| Online Access: | http://hdl.handle.net/1721.1/109587 https://orcid.org/0000-0003-4074-8980 |
| _version_ | 1826193460193918976 |
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| author | Reich, Lothar Dutta, Sanjib Keating, Amy E. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Reich, Lothar Dutta, Sanjib Keating, Amy E. |
| author_sort | Reich, Lothar |
| collection | MIT |
| description | Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein–protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast-displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting of a library, deep sequencing of sorted pools and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY's utility by ranking 1026 BH3 (Bcl-2 homology 3) peptides with respect to their affinities for the anti-apoptotic protein Bcl-x[subscript L]. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60 nM. High-resolution ranking can be used to improve our understanding of sequence–function relationships and to support the development of computational models for predicting and designing novel interactions. |
| first_indexed | 2024-09-23T09:39:34Z |
| format | Article |
| id | mit-1721.1/109587 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:39:34Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1095872022-09-30T16:01:16Z SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands Reich, Lothar Dutta, Sanjib Keating, Amy E. Massachusetts Institute of Technology. Department of Biology Reich, Lothar Dutta, Sanjib Keating, Amy E. Uncovering the relationships between peptide and protein sequences and binding properties is critical for successfully predicting, re-designing and inhibiting protein–protein interactions. Systematically collected data that link protein sequence to binding are valuable for elucidating determinants of protein interaction but are rare in the literature because such data are experimentally difficult to generate. Here we describe SORTCERY, a high-throughput method that we have used to rank hundreds of yeast-displayed peptides according to their affinities for a target interaction partner. The procedure involves fluorescence-activated cell sorting of a library, deep sequencing of sorted pools and downstream computational analysis. We have developed theoretical models and statistical tools that assist in planning these stages. We demonstrate SORTCERY's utility by ranking 1026 BH3 (Bcl-2 homology 3) peptides with respect to their affinities for the anti-apoptotic protein Bcl-x[subscript L]. Our results are in striking agreement with measured affinities for 19 individual peptides with dissociation constants ranging from 0.1 to 60 nM. High-resolution ranking can be used to improve our understanding of sequence–function relationships and to support the development of computational models for predicting and designing novel interactions. National Institutes of Health (U.S.) (Award GM096466) German Academic Scholarship Foundation (Grant RE 3111/1-1) 2017-06-05T15:45:25Z 2017-06-05T15:45:25Z 2014-10 2014-08 Article http://purl.org/eprint/type/JournalArticle 0022-2836 1089-8638 http://hdl.handle.net/1721.1/109587 Reich, Lothar “Luther,” Sanjib Dutta, and Amy E. Keating. “SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands.” Journal of Molecular Biology 427.11 (2015): 2135–2150. https://orcid.org/0000-0003-4074-8980 en_US http://dx.doi.org/10.1016/j.jmb.2014.09.025 Journal of Molecular Biology Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier PMC |
| spellingShingle | Reich, Lothar Dutta, Sanjib Keating, Amy E. SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title | SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title_full | SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title_fullStr | SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title_full_unstemmed | SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title_short | SORTCERY—A High–Throughput Method to Affinity Rank Peptide Ligands |
| title_sort | sortcery a high throughput method to affinity rank peptide ligands |
| url | http://hdl.handle.net/1721.1/109587 https://orcid.org/0000-0003-4074-8980 |
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