Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1
Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interacti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier BV
2021
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| Online Access: | https://hdl.handle.net/1721.1/124632.2 |
| _version_ | 1811076650301915136 |
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| author | Frappier, Vincent Jenson, Justin Michael Zhou, Jianfu Grigoryan, Gevorg Keating, Amy E. |
| author2 | Massachusetts Institute of Technology. Department of Biology |
| author_facet | Massachusetts Institute of Technology. Department of Biology Frappier, Vincent Jenson, Justin Michael Zhou, Jianfu Grigoryan, Gevorg Keating, Amy E. |
| author_sort | Frappier, Vincent |
| collection | MIT |
| description | Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%–38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering. |
| first_indexed | 2024-09-23T10:25:28Z |
| format | Article |
| id | mit-1721.1/124632.2 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:25:28Z |
| publishDate | 2021 |
| publisher | Elsevier BV |
| record_format | dspace |
| spelling | mit-1721.1/124632.22021-09-08T14:19:03Z Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 Frappier, Vincent Jenson, Justin Michael Zhou, Jianfu Grigoryan, Gevorg Keating, Amy E. Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Department of Biological Engineering Koch Institute for Integrative Cancer Research at MIT Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%–38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering. NIGMS (Award R01-GM110048) 2021-09-08T14:19:02Z 2020-04-14T19:29:40Z 2021-09-08T14:19:02Z 2019-02 2018-12 2020-04-06T17:26:56Z Article http://purl.org/eprint/type/JournalArticle 0969-2126 https://hdl.handle.net/1721.1/124632.2 "Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1." Structure 27, 4 (April 2019): 606-617.e5. © 2019 Elsevier Ltd en http://dx.doi.org/10.1016/j.str.2019.01.008 Structure Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/octet-stream Elsevier BV PMC |
| spellingShingle | Frappier, Vincent Jenson, Justin Michael Zhou, Jianfu Grigoryan, Gevorg Keating, Amy E. Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title | Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title_full | Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title_fullStr | Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title_full_unstemmed | Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title_short | Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1 |
| title_sort | tertiary structural motif sequence statistics enable facile prediction and design of peptides that bind anti apoptotic bfl 1 and mcl 1 |
| url | https://hdl.handle.net/1721.1/124632.2 |
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