Machine learning for functional protein design

Notin, Pascal; Rollins, Nathan; Gal, Yarin; Sander, Chris; Marks, Debora

doi:10.1038/s41587-024-02127-0

Cited by 26 publications

(5 citation statements)

References 174 publications

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“…Antibody design is another area where AI has made significant contributions. Machine learning models have been used to enhance the features of existing antibodies, such as improving binding affinity, reducing polyspecificity, and optimizing developability properties (Notin et al, 2024). For instance, a deep learning model trained on antibody sequences and their corresponding binding affinities was able to predict mutations that increased the binding strength of a therapeutic antibody by up to 160-fold (Hie et al, 2024).…”

Section: Applications and Successes Of Ai-driven Protein Designmentioning

confidence: 99%

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From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Kauffman

2024

Preprint

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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.

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Section: Applications and Successes Of Ai-driven Protein Designmentioning

confidence: 99%

“…Sequence-label models leverage functional data to guide design towards specific properties. Structure-based models, including inverse folding and diffusion-based approaches, enable the design of novel backbone structures and the scaffolding of functional motifs (Notin et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Kauffman

2024

Preprint

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“…Recently, advancements in artificial intelligence have been revolutionizing in the field of chemistry and biology, such as the organic synthesis, − rational protein design − and protein structure prediction. − The potential of AI to facilitate the development of chemical protein synthesis remains to be explored. To harness the power of AI in chemical protein synthesis, comprehensive knowledge on peptide and protein synthesis can be incorporated into machine learning models.…”

Section: Outlook: Opportunity and Challengesmentioning

confidence: 99%

Chemical Synthesis of Human Proteoforms and Application in Biomedicine

Ai,

Pan,

Liu

2024

ACS Cent. Sci.

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Limited understanding of human proteoforms with complex posttranslational modifications and the underlying mechanisms poses a major obstacle to research on human health and disease. This Outlook discusses opportunities and challenges of de novo chemical protein synthesis in human proteoform studies. Our analysis suggests that to develop a comprehensive, robust, and cost-effective methodology for chemical synthesis of various human proteoforms, new chemistries of the following types need to be developed: (1) easy-to-use peptide ligation chemistries allowing more efficient de novo synthesis of protein structural domains, (2) robust temporary structural support strategies for ligation and folding of challenging targets, and (3) efficient transpeptidative protein domain−domain ligation methods for multidomain proteins. Our analysis also indicates that accurate chemical synthesis of human proteoforms can be applied to the following aspects of biomedical research: (1) dissection and reconstitution of the proteoform interaction networks, (2) structural mechanism elucidation and functional analysis of human proteoform complexes, and (3) development and evaluation of drugs targeting human proteoforms. Overall, we suggest that through integrating chemical protein synthesis with in vivo functional analysis, mechanistic biochemistry, and drug development, synthetic chemistry would play a pivotal role in human proteoform research and facilitate the development of precision diagnostics and therapeutics.

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“…Traditionally, protein design has been approached from two different angles: optimization of an existing protein 4 or complete de novo design 5 . Interestingly, there are an increasing number of approaches being developed that blur the line between these two ends of the spectrum, with all machine learning methods incorporating knowledge about existing proteins.…”

mentioning

confidence: 99%

Spotlight on protein structure design

2024

Nat Biotechnol

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Machine learning for functional protein design

Cited by 26 publications

References 174 publications

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

From Sequence to Structure to Function: De Novo Protein Design, the Role of AI and Structure Prediction Neural Networks

Chemical Synthesis of Human Proteoforms and Application in Biomedicine

Spotlight on protein structure design

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