Version 1
: Received: 1 April 2024 / Approved: 2 April 2024 / Online: 2 April 2024 (15:14:54 CEST)
How to cite:
Kauffman, M. From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks. Preprints2024, 2024040220. https://doi.org/10.20944/preprints202404.0220.v1
Kauffman, M. From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks. Preprints 2024, 2024040220. https://doi.org/10.20944/preprints202404.0220.v1
Kauffman, M. From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks. Preprints2024, 2024040220. https://doi.org/10.20944/preprints202404.0220.v1
APA Style
Kauffman, M. (2024). From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks. Preprints. https://doi.org/10.20944/preprints202404.0220.v1
Chicago/Turabian Style
Kauffman, M. 2024 "From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks" Preprints. https://doi.org/10.20944/preprints202404.0220.v1
Abstract
Recent advancements in artificial intelligence (AI) and deep learning have revolutionized the field of protein engineering, particularly in the area of de novo protein design. This review article explores the impact of AI-driven approaches on protein design, with a specific focus on the role of structure prediction neural networks, such as AlphaFold and RoseTTAFold. The article discusses the paradigm shift brought about by these networks, which have enabled the design of proteins with unique structures and functions that are not found in nature. The review covers various aspects of AI-driven protein design, including the use of protein language models to harness evolutionary information, the development of de novo protein design workflows utilizing deep learning, and the application of generative models like RFdiffusion and RoseTTAFold All-Atom. The article also highlights the successes and applications of AI-driven protein design across diverse domains, such as enzyme engineering, antibody design, and vaccine development. Additionally, the review identifies current challenges and future directions in the field, emphasizing the need to address limitations in modeling conformational dynamics and designing proteins for in vivo functionality. The article concludes by underscoring the potential of AI-driven protein design to transform various aspects of science and technology, while also acknowledging the importance of interdisciplinary collaboration and the development of robust pipelines for the validation and optimization of designed proteins. Overall, this comprehensive review serves as a valuable resource for researchers and practitioners interested in understanding the current state and future prospects of AI-driven protein design.
Keywords
deep learning; machine learning; protein design; biotechnology
Subject
Biology and Life Sciences, Biology and Biotechnology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
