Generating functional protein variants with variational autoencoders

Fast and Flexible Protein Design Using Deep Graph Neural Networks

(2020) Alexey Strokach et al.   Cell Systems

Learning protein constitutive motifs from sequence data

(2019) Jérôme Tubiana et al.   eLife

Machine-learning-guided directed evolution for protein engineering

(2019) Kevin K. Yang et al.   NATURE METHODS

Unified rational protein engineering with sequence-based deep representation learning

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

How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?

(2018) Matteo Figliuzzi et al.   MOLECULAR BIOLOGY AND EVOLUTION

Inverse statistical physics of protein sequences: a key issues review

(2018) Simona Cocco et al.   REPORTS ON PROGRESS IN PHYSICS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Deep generative models of genetic variation capture the effects of mutations

(2018) Adam J. Riesselman et al.   NATURE METHODS

Design of metalloproteins and novel protein folds using variational autoencoders

(2018) Joe G. Greener et al.   Scientific Reports

The EVcouplings Python framework for coevolutionary sequence analysis

(2018) Thomas A Hopf et al.   BIOINFORMATICS

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

(2018) Susann Vorberg et al.   PLoS Computational Biology

Mutation effects predicted from sequence co-variation

(2017) Thomas A Hopf et al.   NATURE BIOTECHNOLOGY

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

(2017) Martin Steinegger et al.   NATURE BIOTECHNOLOGY

Clustal Omega for making accurate alignments of many protein sequences

(2017) Fabian Sievers et al.   PROTEIN SCIENCE

Global analysis of protein folding using massively parallel design, synthesis, and testing

(2017) Gabriel J. Rocklin et al.   SCIENCE

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

(2017) Sheng Wang et al.   PLoS Computational Biology

The coming of age of de novo protein design

(2016) Po-Ssu Huang et al.   NATURE

Consensus protein design

(2016) Benjamin T. Porebski et al.   PROTEIN ENGINEERING DESIGN & SELECTION

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

(2016) Sheng Wang et al.   Scientific Reports

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

(2015) Matt Spencer et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

(2015) Matteo Figliuzzi et al.   MOLECULAR BIOLOGY AND EVOLUTION

Methods for the directed evolution of proteins

(2015) Michael S. Packer et al.   NATURE REVIEWS GENETICS

CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

(2014) Stefan Seemayer et al.   BIOINFORMATICS

Pfam: the protein families database

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

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

(2011) David T. Jones et al.   BIOINFORMATICS

Protein 3D Structure Computed from Evolutionary Sequence Variation

(2011) Debora S. Marks et al.   PLoS One

Crystal Structure of the Bacterial Luciferase/Flavin Complex Provides Insight into the Function of the β Subunit

(2009) Zachary T. Campbell et al.   BIOCHEMISTRY

Enzymatic assembly of DNA molecules up to several hundred kilobases

(2009) Daniel G Gibson et al.   NATURE METHODS

InterPro: the integrative protein signature database

(2008) S. Hunter et al.   NUCLEIC ACIDS RESEARCH

A de novo redesign of the WW domain

(2003) Christina M. Kraemer-Pecore et al.   PROTEIN SCIENCE