AI Medical Compendium Journal:
Protein engineering, design & selection : PEDS

Showing 1 to 5 of 5 articles

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.

Protein engineering, design & selection : PEDS
Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for t...

Protein sequence design on given backbones with deep learning.

Protein engineering, design & selection : PEDS
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need ...

Growing ecosystem of deep learning methods for modeling protein-protein interactions.

Protein engineering, design & selection : PEDS
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a...

Data-driven enzyme engineering to identify function-enhancing enzymes.

Protein engineering, design & selection : PEDS
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of p...

Machine learning for enzyme engineering, selection and design.

Protein engineering, design & selection : PEDS
Machine learning is a useful computational tool for large and complex tasks such as those in the field of enzyme engineering, selection and design. In this review, we examine enzyme-related applications of machine learning. We start by comparing tool...