Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours
Authors
Keywords
Biochemical simulations, Protein structure, Hydrogen bonding, Free energy, Protein structure prediction, Crystal structure, Protein structure comparison, Simulation and modeling
Journal
PLoS Computational Biology
Volume 14, Issue 12, Pages e1006578
Publisher
Public Library of Science (PLoS)
Online
2018-12-28
DOI
10.1371/journal.pcbi.1006578
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Performance of protein-structure predictions with the physics-based UNRES force field in CASP11
- (2016) Paweł Krupa et al. BIOINFORMATICS
- Critical assessment of methods of protein structure prediction: Progress and new directions in round XI
- (2016) John Moult et al. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
- Blind protein structure prediction using accelerated free-energy simulations
- (2016) A. Perez et al. Science Advances
- A Maximum-Likelihood Approach to Force-Field Calibration
- (2015) Bartłomiej Zaborowski et al. Journal of Chemical Information and Modeling
- Folding Simulations for Proteins with Diverse Topologies Are Accessible in Days with a Physics-Based Force Field and Implicit Solvent
- (2014) Hai Nguyen et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Testing the validity of ensemble descriptions of intrinsically disordered proteins
- (2014) M. R. Jensen et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Benchmarking all-atom simulations using hydrogen exchange
- (2014) John J. Skinner et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach
- (2013) Csilla Várnai et al. Journal of Chemical Theory and Computation
- Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences
- (2013) Aashish N. Adhikari et al. PHYSICAL REVIEW LETTERS
- De novo prediction of protein folding pathways and structure using the principle of sequential stabilization
- (2012) A. N. Adhikari et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- How Fast-Folding Proteins Fold
- (2011) K. Lindorff-Larsen et al. SCIENCE
- Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model
- (2010) Daniel Ting et al. PLoS Computational Biology
- Progress and challenges in the automated construction of Markov state models for full protein systems
- (2009) Gregory R. Bowman et al. JOURNAL OF CHEMICAL PHYSICS
- Optimized Molecular Dynamics Force Fields Applied to the Helix−Coil Transition of Polypeptides
- (2009) Robert B. Best et al. JOURNAL OF PHYSICAL CHEMISTRY B
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