6 papers
Multitask Bayesian Neural Networks for Multiparameter Protein Engineering
Fabio Herrera-Rocha, David Medina-Ortiz, Desiree Wyrzykala +2
Simultaneously engineering multiple protein properties remains a major challenge. Existing machine learning-based pipelines for protein engineering often model properties separatel…
Decoding Polyphenol-Protein Interactions with Deep Learning: From Molecular Mechanisms to Food Applications
Qiang Liu, Tiantian Wang, Binbin Nian +6
Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions -- hereafter referred to as PhPIs (polyphe…
Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
Yanzi Zhang, Felix Moorhoff, Sizhe Qiu +4
Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of…
Best Practices for Machine Learning-Assisted Protein Engineering
Fabio Herrera-Rocha, David Medina-Ortiz, Fabian Mauz +2
Data-driven modeling based on Machine Learning (ML) is becoming a central component of protein engineering workflows. This perspective presents the elements necessary to develop ef…
Geometric deep learning assists protein engineering. Opportunities and Challenges
Julián GarcÃa-Vinuesa, Jorge Rojas, Nicole Soto-GarcÃa +7
Protein engineering is experiencing a paradigmatic shift through the integration of geometric deep learning into computational design workflows. While traditional strategies, such…
From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
Wen-ran Li, Xavier F. Cadet, David Medina-Ortiz +6
Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great succes…