TY - JOUR
T1 - Peptriever
T2 - a Bi-Encoder approach for large-scale protein–peptide binding search
AU - Gurvich, Roni
AU - Markel, Gal
AU - Tanoli, Ziaurrehman
AU - Meirson, Tomer
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/5/2
Y1 - 2024/5/2
N2 - 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/.
AB - 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/.
UR - http://www.scopus.com/inward/record.url?scp=85194073484&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btae303
DO - 10.1093/bioinformatics/btae303
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C2 - 38710496
AN - SCOPUS:85194073484
SN - 1367-4803
VL - 40
JO - Bioinformatics
JF - Bioinformatics
IS - 5
M1 - btae303
ER -