Protein matchmaking through representation learning

Michael Heinzinger; Christian Dallago; Burkhard Rost

doi:10.1016/j.cels.2021.09.007

Protein matchmaking through representation learning

Michael Heinzinger, Christian Dallago, Burkhard Rost

Informatics 12 - Chair of Bioinformatics

Technical University of Munich

Research output: Contribution to journal › Article › peer-review

Abstract

Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.

Original language	English
Pages (from-to)	948-950
Number of pages	3
Journal	Cell Systems
Volume	12
Issue number	10
DOIs	https://doi.org/10.1016/j.cels.2021.09.007
State	Published - 20 Oct 2021

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title = "Protein matchmaking through representation learning",

abstract = "Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.",

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AU - Dallago, Christian

AU - Rost, Burkhard

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AB - Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while evaluating on others. The work exemplifies how transfer of AI-learned representations can advance knowledge in molecular biology.

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SN - 2405-4712

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JO - Cell Systems

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Protein matchmaking through representation learning

Abstract

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