Related references
Note: Only part of the references are listed.EvoEF2: accurate and fast energy function for computational protein design
Xiaoqiang Huang et al.
BIOINFORMATICS (2020)
Improved protein structure prediction using potentials from deep learning
Andrew W. Senior et al.
NATURE (2020)
A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures
Jianfu Zhou et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
ProDCoNN: Protein design using a convolutional neural network
Yuan Zhang et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2020)
Modular repeat protein sculpting using rigid helical junctions
T. J. Brunette et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
Macromolecular modeling and design in Rosetta: recent methods and frameworks
Julia Koehler Leman et al.
NATURE METHODS (2020)
De novo design of protein peptides to block association of the SARS-CoV-2 spike protein with human ACE2
Xiaoqiang Huang et al.
Aging-US (2020)
Practically useful protein-design methods combining phylogenetic and atomistic calculations
Jonathan Weinstein et al.
CURRENT OPINION IN STRUCTURAL BIOLOGY (2020)
Signal Peptides Generated by Attention-Based Neural Networks
Zachary Wu et al.
ACS SYNTHETIC BIOLOGY (2020)
Engineered ACE2 receptor traps potently neutralize SARS-CoV-2
Anum Glasgow et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
A defined structural unit enables de novo design of small-molecule-binding proteins
Nicholas F. Polizzi et al.
SCIENCE (2020)
De novo design of picomolar SARS-CoV-2 miniprotein inhibitors
Longxing Cao et al.
SCIENCE (2020)
Fast and Flexible Protein Design Using Deep Graph Neural Networks
Alexey Strokach et al.
CELL SYSTEMS (2020)
An evolution-based model for designing chorismate mutase enzymes
William P. Russ et al.
SCIENCE (2020)
EvoDesign: Designing Protein-Protein Binding Interactions Using Evolutionary Interface Profiles in Conjunction with an Optimized Physical Energy Function
Robin Pearce et al.
JOURNAL OF MOLECULAR BIOLOGY (2019)
Large-Scale, Quantitative Protein Assays on a High-Throughput DNA Sequencing Chip
Curtis J. Layton et al.
MOLECULAR CELL (2019)
Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1
Vincent Frappier et al.
STRUCTURE (2019)
Consensus sequence design as a general strategy to create hyperstable, biologically active proteins
Matt Sternke et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Machine learning-assisted directed protein evolution with combinatorial libraries
Zachary Wu et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis
John M. Nicoludis et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics
Claire N. Bedbrook et al.
NATURE METHODS (2019)
Unified rational protein engineering with sequence-based deep representation learning
Ethan C. Alley et al.
NATURE METHODS (2019)
Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)
Andrew W. Senior et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)
Computational design of a modular protein sense-response system
Anum A. Glasgow et al.
SCIENCE (2019)
CATH: expanding the horizons of structure-based functional annotations for genome sequences
Ian Sillitoe et al.
NUCLEIC ACIDS RESEARCH (2019)
The Pfam protein families database in 2019
Sara El-Gebali et al.
NUCLEIC ACIDS RESEARCH (2019)
Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations
Manasi A. Pethe et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Co-Evolutionary Fitness Landscapes for Sequence Design
Pengfei Tian et al.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)
ProtaBank: A repository for protein design and engineering data
Connie Y. Wang et al.
PROTEIN SCIENCE (2018)
SPIN2: Predicting sequence profiles from protein structures using deep neural networks
James O'Connell et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2018)
Computational Protein Design with Deep Learning Neural Networks
Jingxue Wang et al.
SCIENTIFIC REPORTS (2018)
Automated Design of Efficient and Functionally Diverse Enzyme Repertoires
Olga Khersonsky et al.
MOLECULAR CELL (2018)
De novo design of a fluorescence-activating β-barrel
Jiayi Dou et al.
NATURE (2018)
Deep generative models of genetic variation capture the effects of mutations
Adam J. Riesselman et al.
NATURE METHODS (2018)
Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
Yutaka Saito et al.
ACS SYNTHETIC BIOLOGY (2018)
Peptide design by optimization on a data-parameterized protein interaction landscape
Justin M. Jenson et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging
Katherine W. Tripp et al.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2017)
Mutation effects predicted from sequence co-variation
Thomas A. Hopf et al.
NATURE BIOTECHNOLOGY (2017)
Principles for computational design of binding antibodies
Dror Baran et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)
Global analysis of protein folding using massively parallel design, synthesis, and testing
Gabriel J. Rocklin et al.
SCIENCE (2017)
Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization
Claire N. Bedbrook et al.
PLOS COMPUTATIONAL BIOLOGY (2017)
Automated Structure- and Sequence-Based Design of Proteins for High Bacterial Expression and Stability
Adi Goldenzweig et al.
MOLECULAR CELL (2016)
Tertiary alphabet for the observable protein structural universe
Craig O. Mackenzie et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2016)
Consensus protein design
Benjamin T. Porebski et al.
PROTEIN ENGINEERING DESIGN & SELECTION (2016)
Design of structurally distinct proteins using strategies inspired by evolution
T. M. Jacobs et al.
SCIENCE (2016)
Tertiary Structural Propensities Reveal Fundamental Sequence/Structure Relationships
Fan Zheng et al.
STRUCTURE (2015)
Data-Driven Prediction and Design of bZIP Coiled-Coil Interactions
Vladimir Potapov et al.
PLOS COMPUTATIONAL BIOLOGY (2015)
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
Zhixiu Li et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2014)
Protein 3D Structure Computed from Evolutionary Sequence Variation
Debora S. Marks et al.
PLOS ONE (2011)
Protein design automation
Bassil I. Dahiyat et al.
PROTEIN SCIENCE (2010)
Design of protein-interaction specificity gives selective bZIP-binding peptides
Gevorg Grigoryan et al.
NATURE (2009)
Structure-based prediction of bZIP partnering specificity
G Grigoryan et al.
JOURNAL OF MOLECULAR BIOLOGY (2006)
Share Paper
