Exploring the protein sequence space with global generative models

• Verfasst von: Romero Romero, Sergio [VerfasserIn]
• Verfasst von: Lindner, Sebastian [VerfasserIn]
• Verfasst von: Ferruz, Noelia [VerfasserIn]
• Titel: Exploring the protein sequence space with global generative models
• Verf.angabe: Sergio Romero-Romero, Sebastian Lindner, and Noelia Ferruz
• E-Jahr: 2023
• Jahr: October 17, 2023
• Umfang: 17 S.
• Fussnoten: Gesehen am 08.05.2024
• Titel Quelle: Enthalten in: Cold Spring Harbor perspectives in biology
• Ort Quelle: Cold Spring Harbor, NY : Cold Spring Harbor Laboratory Press, 2009
• Jahr Quelle: 2023
• Band/Heft Quelle: 15(2023), 11, Artikel-ID a041471, Seite 1-17
• ISSN Quelle: 1943-0264
• DOI: doi:10.1101/cshperspect.a041471
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: DESIGN
• Sach-SW: DIRECTED EVOLUTION
• Sach-SW: FITNESS LANDSCAPES
• Sach-SW: LANGUAGE
• K10plus-PPN: 1888145382

Abstract

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.