Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Abstract excerpt

Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardization, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid...

Topics

• Proteins
• Supervised Machine Learning
• Software
• Machine Learning
• Computational Biology
• Mutation
• Models, Molecular