Large language models generate functional protein sequences across diverse families

A backbone-centred energy function of neural networks for protein design

(2022) Bin Huang et al.   NATURE

Protein sequence design with a learned potential

(2022) Namrata Anand et al.   Nature Communications

ColabFold: making protein folding accessible to all

(2022) Milot Mirdita et al.   NATURE METHODS

ProtGPT2 is a deep unsupervised language model for protein design

(2022) Noelia Ferruz et al.   Nature Communications

Deep diversification of an AAV capsid protein by machine learning

(2021) Drew H. Bryant et al.   NATURE BIOTECHNOLOGY

Protein sequence design by conformational landscape optimization

(2021) Christoffer Norn et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations

(2021) Payel Das et al.   Nature Biomedical Engineering

Fast and sensitive taxonomic assignment to metagenomic contigs

(2021) M Mirdita et al.   BIOINFORMATICS

Low-N protein engineering with data-efficient deep learning

(2021) Surojit Biswas et al.   NATURE METHODS

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

(2021) Alexander Rives et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Protein design and variant prediction using autoregressive generative models

(2021) Jung-Eun Shin et al.   Nature Communications

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

De novo protein design by deep network hallucination

(2021) Ivan Anishchenko et al.   NATURE

Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM

(2020) Soon Wen Hoh et al.   Acta Crystallographica Section D-Structural Biology

Signal Peptides Generated by Attention-Based Neural Networks

(2020) Zachary Wu et al.   ACS Synthetic Biology

Unified rational protein engineering with sequence-based deep representation learning

(2019) Ethan C. Alley et al.   NATURE METHODS

On the catalytic mechanism of bacteriophage endolysins: Opportunities for engineering

(2019) Michael J. Love et al.   BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS

Overview of refinement procedures within REFMAC5: utilizing data from different sources

(2018) Oleg Kovalevskiy et al.   Acta Crystallographica Section D-Structural Biology

Catalytic diversity and cell wall binding repeats in the phage encoded endolysins

(2018) Sebastian S. Broendum et al.   MOLECULAR MICROBIOLOGY

The EVcouplings Python framework for coevolutionary sequence analysis

(2018) Thomas A Hopf et al.   BIOINFORMATICS

The coming of age of de novo protein design

(2016) Po-Ssu Huang et al.   NATURE

De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity

(2016) S. E. Boyken et al.   SCIENCE

Deep learning

(2015) Yann LeCun et al.   NATURE

De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy

(2015) Po-Ssu Huang et al.   Nature Chemical Biology

BetaCavityWeb: a webserver for molecular voids and channels

(2015) Jae-Kwan Kim et al.   NUCLEIC ACIDS RESEARCH

Control over overall shape and size in de novo designed proteins

(2015) Yu-Ru Lin et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models

(2015) Richard R. Stein et al.   PLoS Computational Biology

Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information

(2014) Sergey Ovchinnikov et al.   eLife

Pfam: the protein families database

(2013) Robert D. Finn et al.   NUCLEIC ACIDS RESEARCH

Towards automated crystallographic structure refinement withphenix.refine

(2012) Pavel V. Afonine et al.   ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY

Principles for designing ideal protein structures

(2012) Nobuyasu Koga et al.   NATURE

The NCBI Taxonomy database

(2011) S. Federhen   NUCLEIC ACIDS RESEARCH

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

(2011) F. Morcos et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

XDS

(2010) Wolfgang Kabsch   ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY

Features and development ofCoot

(2010) P. Emsley et al.   ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY

Lessons from the lysozyme of phage T4

(2010) Walter A. Baase et al.   PROTEIN SCIENCE

Learning generative models for protein fold families

(2010) Sivaraman Balakrishnan et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Evaluation at atomic resolution of the role of strain in destabilizing the temperature-sensitive T4 lysozyme mutant Arg 96 → His

(2009) Blaine H. M. Mooers et al.   PROTEIN SCIENCE

Graphical Models of Residue Coupling in Protein Families

(2008) J. Thomas et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

Identification of direct residue contacts in protein-protein interaction by message passing

(2008) M. Weigt et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA