Self-driving laboratories for chemistry and materials science
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …
Through the automation of experimental workflows, along with autonomous experimental …
Protein design: From the aspect of water solubility and stability
Water solubility and structural stability are key merits for proteins defined by the primary
sequence and 3D-conformation. Their manipulation represents important aspects of the …
sequence and 3D-conformation. Their manipulation represents important aspects of the …
Generalized biomolecular modeling and design with RoseTTAFold All-Atom
Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …
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 …
Accurate prediction of protein–nucleic acid complexes using RoseTTAFoldNA
Protein–RNA and protein–DNA complexes play critical roles in biology. Despite
considerable recent advances in protein structure prediction, the prediction of the structures …
considerable recent advances in protein structure prediction, the prediction of the structures …
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 …
Scaffolding protein functional sites using deep learning
The binding and catalytic functions of proteins are generally mediated by a small number of
functional residues held in place by the overall protein structure. Here, we describe deep …
functional residues held in place by the overall protein structure. Here, we describe deep …
Antigen-specific antibody design and optimization with diffusion-based generative models for protein structures
Antibodies are immune system proteins that protect the host by binding to specific antigens
such as viruses and bacteria. The binding between antibodies and antigens is mainly …
such as viruses and bacteria. The binding between antibodies and antigens is mainly …
Single-sequence protein structure prediction using a language model and deep learning
R Chowdhury, N Bouatta, S Biswas, C Floristean… - Nature …, 2022 - nature.com
AlphaFold2 and related computational systems predict protein structure using deep learning
and co-evolutionary relationships encoded in multiple sequence alignments (MSAs) …
and co-evolutionary relationships encoded in multiple sequence alignments (MSAs) …
Protein design with guided discrete diffusion
A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …
model for conditional sampling. The generative model samples plausible sequences while …