Related references
Note: Only part of the references are listed.ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Ahmed Elnaggar et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)
Improved protein structure refinement guided by deep learning based accuracy estimation
Naozumi Hiranuma et al.
NATURE COMMUNICATIONS (2021)
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)
Improved protein structure prediction using potentials from deep learning
Andrew W. Senior et al.
NATURE (2020)
Improved protein structure prediction using predicted interresidue orientations
Jianyi Yang et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
Deep learning enables the atomic structure determination of the Fanconi Anemia core complex from cryoEM
Daniel P. Farrell et al.
IUCRJ (2020)
Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold
Martin Steinegger et al.
NATURE METHODS (2019)
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13
Shaun M. Kandathil et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)
HH-suite3 for fast remote homology detection and deep protein annotation
Martin Steinegger et al.
BMC BIOINFORMATICS (2019)
Distance-based protein folding powered by deep learning
Jinbo Xu
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
High-accuracy refinement using Rosetta in CASP13
Hahnbeom Park et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
Joe G. Greener et al.
NATURE COMMUNICATIONS (2019)
MolProbity: More and better reference data for improved all-atom structure validation
Christopher J. Williams et al.
PROTEIN SCIENCE (2018)
Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12
Jurgen Haas et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2018)
Protein homology model refinement by large-scale energy optimization
Hahnbeom Park et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Clustering huge protein sequence sets in linear time
Martin Steinegger et al.
NATURE COMMUNICATIONS (2018)
Uniclust databases of clustered and deeply annotated protein sequences and alignments
Milot Mirdita et al.
NUCLEIC ACIDS RESEARCH (2017)
IMG/M: integrated genome and metagenome comparative data analysis system
I-Min A. Chen et al.
NUCLEIC ACIDS RESEARCH (2017)
MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger et al.
NATURE BIOTECHNOLOGY (2017)
Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules
Hahnbeom Park et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2016)
Relaxation of backbone bond geometry improves protein energy landscape modeling
Patrick Conway et al.
PROTEIN SCIENCE (2014)
Accelerated Profile HMM Searches
Sean R. Eddy
PLOS COMPUTATIONAL BIOLOGY (2011)
Clustal W and clustal X version 2.0
M. A. Larkin et al.
BIOINFORMATICS (2007)
Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction
HY Zhou et al.
PROTEIN SCIENCE (2002)