Protein structure prediction in the deep learning era

Current structure predictors are not learning the physics of protein folding

(2022) Carlos Outeiral et al.   BIOINFORMATICS

SPOT-Contact-LM: improving single-sequence-based prediction of protein contact map using a transformer language model

(2022) Jaspreet Singh et al.   BIOINFORMATICS

The impact of AlphaFold2 one year on

(2022) David T. Jones et al.   NATURE METHODS

Can AlphaFold2 predict the impact of missense mutations on structure?

(2022) Gwen R. Buel et al.   NATURE STRUCTURAL & MOLECULAR BIOLOGY

The protein-folding problem: Not yet solved

(2022) Peter B. Moore et al.   SCIENCE

ColabFold: making protein folding accessible to all

(2022) Milot Mirdita et al.   NATURE METHODS

Improved prediction of protein-protein interactions using AlphaFold2

(2022) Patrick Bryant et al.   Nature Communications

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

(2022) Mu Gao et al.   Nature Communications

Structure of cytoplasmic ring of nuclear pore complex by integrative cryo-EM and AlphaFold

(2022) Pietro Fontana et al.   SCIENCE

CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction

(2021) Fusong Ju et al.   Nature Communications

Deep learning techniques have significantly impacted protein structure prediction and protein design

(2021) Robin Pearce et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

Toward the solution of the protein structure prediction problem

(2021) Robin Pearce et al.   JOURNAL OF BIOLOGICAL CHEMISTRY

Highly accurate protein structure prediction with AlphaFold

(2021) John Jumper et al.   NATURE

Highly accurate protein structure prediction for the human proteome

(2021) Kathryn Tunyasuvunakool et al.   NATURE

What does AlphaFold mean for drug discovery?

(2021) Asher Mullard   NATURE REVIEWS DRUG DISCOVERY

Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14

(2021) Wei Zheng et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Protein oligomer modeling guided by predicted interchain contacts in CASP14

(2021) Minkyung Baek et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14 —( tFold for CASP14 contact prediction)

(2021) Tao Shen et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Accurate prediction of protein structures and interactions using a three-track neural network

(2021) Minkyung Baek et al.   SCIENCE

De novo protein design by deep network hallucination

(2021) Ivan Anishchenko et al.   NATURE

The trRosetta server for fast and accurate protein structure prediction

(2021) Zongyang Du et al.   Nature Protocols

Computed structures of core eukaryotic protein complexes

(2021) Ian R. Humphreys et al.   SCIENCE

Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

(2021) Hong Su et al.   Advanced Science

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Improved protein structure prediction using predicted interresidue orientations

(2020) Jianyi Yang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The HDOCK server for integrated protein–protein docking

(2020) Yumeng Yan et al.   Nature Protocols

‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures

(2020) Ewen Callaway   NATURE

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

Protein contact prediction using metagenome sequence data and residual neural networks

(2019) Qi Wu et al.   BIOINFORMATICS

Distance-based protein folding powered by deep learning

(2019) Jinbo Xu   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

(2019) Joe G. Greener et al.   Nature Communications

MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets

(2017) Martin Steinegger et al.   NATURE BIOTECHNOLOGY

The ClusPro web server for protein–protein docking

(2017) Dima Kozakov et al.   Nature Protocols

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

(2017) Sheng Wang et al.   PLoS Computational Biology

The nature of protein folding pathways

(2014) S. Walter Englander et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

The Protein-Folding Problem, 50 Years On

(2012) K. A. Dill et al.   SCIENCE

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

(2011) Michael Remmert et al.   NATURE METHODS