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Opportunities and challenges in design and optimization of protein function
The field of protein design has made remarkable progress over the past decade. Historically,
the low reliability of purely structure-based design methods limited their application, but …
the low reliability of purely structure-based design methods limited their application, but …
[HTML][HTML] De novo protein design—From new structures to programmable functions
Methods from artificial intelligence (AI) trained on large datasets of sequences and
structures can now" write" proteins with new shapes and molecular functions de novo …
structures can now" write" proteins with new shapes and molecular functions de novo …
Mega-scale experimental analysis of protein folding stability in biology and design
Advances in DNA sequencing and machine learning are providing insights into protein
sequences and structures on an enormous scale. However, the energetics driving folding …
sequences and structures on an enormous scale. However, the energetics driving folding …
Learning inverse folding from millions of predicted structures
We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …
coordinates. Machine learning approaches to this problem to date have been limited by the …
Design of protein-binding proteins from the target structure alone
The design of proteins that bind to a specific site on the surface of a target protein using no
information other than the three-dimensional structure of the target remains a challenge …
information other than the three-dimensional structure of the target remains a challenge …
De novo design of high-affinity binders of bioactive helical peptides
Many peptide hormones form an α-helix on binding their receptors,,–, and sensitive methods
for their detection could contribute to better clinical management of disease. De novo protein …
for their detection could contribute to better clinical management of disease. De novo protein …
De novo design of protein interactions with learned surface fingerprints
Physical interactions between proteins are essential for most biological processes
governing life. However, the molecular determinants of such interactions have been …
governing life. However, the molecular determinants of such interactions have been …
Improving de novo protein binder design with deep learning
Recently it has become possible to de novo design high affinity protein binding proteins from
target structural information alone. There is, however, considerable room for improvement as …
target structural information alone. There is, however, considerable room for improvement as …
ProteinBERT: a universal deep-learning model of protein sequence and function
Self-supervised deep language modeling has shown unprecedented success across natural
language tasks, and has recently been repurposed to biological sequences. However …
language tasks, and has recently been repurposed to biological sequences. However …
Learning protein fitness models from evolutionary and assay-labeled data
Abstract Machine learning-based models of protein fitness typically learn from either
unlabeled, evolutionarily related sequences or variant sequences with experimentally …
unlabeled, evolutionarily related sequences or variant sequences with experimentally …