Peptriever: a Bi-Encoder approach for large-scale protein–peptide binding search

Roni Gurvich, Gal Markel, Ziaurrehman Tanoli, Tomer Meirson

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: Peptide therapeutics hinge on the precise interaction between a tailored peptide and its designated receptor while mitigating interactions with alternate receptors is equally indispensable. Existing methods primarily estimate the binding score between protein and peptide pairs. However, for a specific peptide without a corresponding protein, it is challenging to identify the proteins it could bind due to the sheer number of potential candidates. Results: We propose a transformers-based protein embedding scheme in this study that can quickly identify and rank millions of interacting proteins. Furthermore, the proposed approach outperforms existing sequence- and structure-based methods, with a mean AUC-ROC and AUC-PR of 0.73. Availability and implementation: Training data, scripts, and fine-tuned parameters are available at https://github.com/RoniGurvich/Peptriever. The proposed method is linked with a web application available for customized prediction at https://peptriever.app/.

Original languageEnglish
Article numberbtae303
JournalBioinformatics
Volume40
Issue number5
DOIs
StatePublished - 2 May 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

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