The promises of large language models for protein design and modeling.

IF 2.8 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in bioinformatics Pub Date : 2023-11-23 eCollection Date: 2023-01-01 DOI:10.3389/fbinf.2023.1304099
Giorgio Valentini, Dario Malchiodi, Jessica Gliozzo, Marco Mesiti, Mauricio Soto-Gomez, Alberto Cabri, Justin Reese, Elena Casiraghi, Peter N Robinson
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引用次数: 0

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.

大语言模型在蛋白质设计和建模方面的前景。
大语言模型(LLMs)最近在自然语言处理方面取得的突破为蛋白质研究的重大进展开辟了道路。事实上,人类自然语言与 "蛋白质语言 "之间的关系促使人们将大型语言模型应用于蛋白质建模和设计。考虑到 GPT-4 和其他最近开发的 LLM 在处理、生成和翻译人类语言方面取得的令人印象深刻的成果,我们预计蛋白质语言也会取得类似的成果。事实上,蛋白质语言模型已经经过训练,可以准确预测蛋白质特性,生成具有功能特征的新型蛋白质,取得了最先进的成果。在本文中,我们将讨论这一令人兴奋的新研究领域所带来的前景和挑战,并就 LLM 将如何影响蛋白质建模和设计提出我们的看法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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