Directed evolution: methodologies and applications
Directed evolution aims to expedite the natural evolution process of biological molecules
and systems in a test tube through iterative rounds of gene diversifications and library …
and systems in a test tube through iterative rounds of gene diversifications and library …
Learning the protein language: Evolution, structure, and function
Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …
distilling information from massive protein sequence databases. From readily available …
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …
enabled by unsupervised learning has led to major advances in representation learning and …
[HTML][HTML] D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions
We combine advances in neural language modeling and structurally motivated design to
develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts …
develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts …
Transforming the language of life: transformer neural networks for protein prediction tasks
The scientific community is rapidly generating protein sequence information, but only a
fraction of these proteins can be experimentally characterized. While promising deep …
fraction of these proteins can be experimentally characterized. While promising deep …
Neural networks to learn protein sequence–function relationships from deep mutational scanning data
S Gelman, SA Fahlberg… - Proceedings of the …, 2021 - National Acad Sciences
The mapping from protein sequence to function is highly complex, making it challenging to
predict how sequence changes will affect a protein's behavior and properties. We present a …
predict how sequence changes will affect a protein's behavior and properties. We present a …
Sequence-based prediction of protein-protein interactions: a structure-aware interpretable deep learning model
Protein-protein interaction (PPI) networks have proven to be a valuable tool in systems
biology to facilitate the discovery and understanding of protein function. Unfortunately …
biology to facilitate the discovery and understanding of protein function. Unfortunately …
Transformer neural networks for protein family and interaction prediction tasks
The scientific community is rapidly generating protein sequence information, but only a
fraction of these proteins can be experimentally characterized. While promising deep …
fraction of these proteins can be experimentally characterized. While promising deep …
A patent-based consideration of latest platforms in the art of directed evolution: A decade long untold story
Z Iqbal, S Sadaf - Biotechnology and Genetic Engineering Reviews, 2022 - Taylor & Francis
Directed (or in vitro) evolution of proteins and metabolic pathways requires tools for creating
genetic diversity and identifying protein variants with new or improved functional properties …
genetic diversity and identifying protein variants with new or improved functional properties …
XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers
Graph representations are traditionally used to represent protein structures in sequence
design protocols in which the protein backbone conformation is known. This infrequently …
design protocols in which the protein backbone conformation is known. This infrequently …