Document Type
Article
Publication Date
1-1-2023
Original Citation
Valentini G,
Malchiodi D,
Gliozzo J,
Mesiti M,
Soto-Gomez M,
Cabri A,
Reese J,
Casiraghi E,
Robinson P.
The promises of large language models for protein design and modeling. Front Bioinform. 2023;3:1304099.
Keywords
JGM
JAX Source
Front Bioinform. 2023;3:1304099.
ISSN
2673-7647
PMID
38076030
DOI
https://doi.org/10.3389/fbinf.2023.1304099
Grant
The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the “National Center for Gene Therapy and Drugs based on RNA Technology,” PNRR- NextGenerationEU program [G43C22001320007], Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy Contract No. DE-AC02-05CH11231, and was realised with the collaboration of the European Commission Joint Research Centre under the Collaborative Doctoral Partnership Agreement No. 35454.
Abstract
The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the "language of proteins" invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design.
Comments
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