Robust deep learning–based protein sequence design using ProteinMPNN
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Robust deep learning–based protein sequence design using ProteinMPNN
Authors
Keywords
-
Journal
SCIENCE
Volume 378, Issue 6615, Pages 49-56
Publisher
American Association for the Advancement of Science (AAAS)
Online
2022-09-16
DOI
10.1126/science.add2187
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Protein sequence design with a learned potential
- (2022) Namrata Anand et al. Nature Communications
- Design of protein-binding proteins from the target structure alone
- (2022) Longxing Cao et al. NATURE
- Hallucinating symmetric protein assemblies
- (2022) B. I. M. Wicky et al. SCIENCE
- Quadrivalent influenza nanoparticle vaccines induce broad protection
- (2021) Seyhan Boyoglu-Barnum et al. NATURE
- Highly accurate protein structure prediction with AlphaFold
- (2021) John Jumper et al. NATURE
- Accurate prediction of protein structures and interactions using a three-track neural network
- (2021) Minkyung Baek et al. SCIENCE
- DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet
- (2020) Yifei Qi et al. Journal of Chemical Information and Modeling
- Macromolecular modeling and design in Rosetta: recent methods and frameworks
- (2020) Julia Koehler Leman et al. NATURE METHODS
- Fast and Flexible Protein Design Using Deep Graph Neural Networks
- (2020) Alexey Strokach et al. Cell Systems
- SNAC-tag for sequence-specific chemical protein cleavage
- (2019) Bobo Dang et al. NATURE METHODS
- Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus
- (2019) Jessica Marcandalli et al. CELL
- ProDCoNN: Protein design using a convolutional neural network
- (2019) Yuan Zhang et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
- (2017) Martin Steinegger et al. NATURE BIOTECHNOLOGY
- MolProbity: More and better reference data for improved all-atom structure validation
- (2017) Christopher J. Williams et al. PROTEIN SCIENCE
- Accurate design of co-assembling multi-component protein nanomaterials
- (2014) Neil P. King et al. NATURE
- lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
- (2013) Valerio Mariani et al. BIOINFORMATICS
- Overview of theCCP4 suite and current developments
- (2011) Martyn D. Winn et al. ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY
- XDS
- (2010) Wolfgang Kabsch ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY
- PHENIX: a comprehensive Python-based system for macromolecular structure solution
- (2010) Paul D. Adams et al. ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started