State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
State-of-the-Art Estimation of Protein Model Accuracy Using AlphaFold
Authors
Keywords
-
Journal
PHYSICAL REVIEW LETTERS
Volume 129, Issue 23, Pages -
Publisher
American Physical Society (APS)
Online
2022-11-28
DOI
10.1103/physrevlett.129.238101
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Predicting and interpreting large-scale mutagenesis data using analyses of protein stability and conservation
- (2022) Magnus Haraldson Høie et al. Cell Reports
- Robust deep learning–based protein sequence design using ProteinMPNN
- (2022) J. Dauparas et al. SCIENCE
- Improved protein structure refinement guided by deep learning based accuracy estimation
- (2021) Naozumi Hiranuma et al. Nature Communications
- Highly accurate protein structure prediction with AlphaFold
- (2021) John Jumper et al. NATURE
- Assessment of protein model structure accuracy estimation in CASP14 : Old and new challenges
- (2021) Sohee Kwon et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design
- (2017) Rebecca F. Alford et al. Journal of Chemical Theory and Computation
- Simultaneous Optimization of Biomolecular Energy Functions on Features from Small Molecules and Macromolecules
- (2016) Hahnbeom Park et al. Journal of Chemical Theory and Computation
- Evolving New Protein-Protein Interaction Specificity through Promiscuous Intermediates
- (2015) Christopher D. Aakre et al. CELL
- Evolvability as a Function of Purifying Selection in TEM-1 β-Lactamase
- (2015) Michael A. Stiffler et al. CELL
- lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests
- (2013) Valerio Mariani et al. BIOINFORMATICS
- Computational Protein Design Quantifies Structural Constraints on Amino Acid Covariation
- (2013) Noah Ollikainen et al. PLoS Computational Biology
- The Protein-Folding Problem, 50 Years On
- (2012) K. A. Dill et al. SCIENCE
- PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments
- (2011) David T. Jones et al. BIOINFORMATICS
- Direct-coupling analysis of residue coevolution captures native contacts across many protein families
- (2011) F. Morcos et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Learning generative models for protein fold families
- (2010) Sivaraman Balakrishnan et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Protein structure homology modeling using SWISS-MODEL workspace
- (2009) Lorenza Bordoli et al. Nature Protocols
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started