Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Machine learning for functional protein design
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …
structure data have radically transformed computational protein design. New methods …
Evolutionary-scale prediction of atomic-level protein structure with a language model
Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …
sequence alignments to predict protein structure. We demonstrate direct inference of full …
ProtGPT2 is a deep unsupervised language model for protein design
Protein design aims to build novel proteins customized for specific purposes, thereby
holding the potential to tackle many environmental and biomedical problems. Recent …
holding the potential to tackle many environmental and biomedical problems. Recent …
Genome-wide prediction of disease variant effects with a deep protein language model
Predicting the effects of coding variants is a major challenge. While recent deep-learning
models have improved variant effect prediction accuracy, they cannot analyze all coding …
models have improved variant effect prediction accuracy, they cannot analyze all coding …
Efficient evolution of human antibodies from general protein language models
Natural evolution must explore a vast landscape of possible sequences for desirable yet
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
rare mutations, suggesting that learning from natural evolutionary strategies could guide …
Proteingym: Large-scale benchmarks for protein fitness prediction and design
Predicting the effects of mutations in proteins is critical to many applications, from
understanding genetic disease to designing novel proteins to address our most pressing …
understanding genetic disease to designing novel proteins to address our most pressing …
Protst: Multi-modality learning of protein sequences and biomedical texts
Current protein language models (PLMs) learn protein representations mainly based on
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
Graph denoising diffusion for inverse protein folding
Inverse protein folding is challenging due to its inherent one-to-many mapping
characteristic, where numerous possible amino acid sequences can fold into a single …
characteristic, where numerous possible amino acid sequences can fold into a single …
xTrimoPGLM: unified 100B-scale pre-trained transformer for deciphering the language of protein
Protein language models have shown remarkable success in learning biological information
from protein sequences. However, most existing models are limited by either autoencoding …
from protein sequences. However, most existing models are limited by either autoencoding …