scholarly journals Protein modeling and structure prediction with a reduced representation.

2004 ◽  
Vol 51 (2) ◽  
pp. 349-371 ◽  
Author(s):  
Andrzej Kolinski
Keyword(s):  
High Resolution ◽  
Force Field ◽  
Structure Prediction ◽  
Comparative Modeling ◽  
Protein Modeling ◽  
Conformational Space ◽  
Structural Proteomics ◽  
New Approach ◽  
Statistical Potentials ◽  
Alpha Carbon

Protein modeling could be done on various levels of structural details, from simplified lattice or continuous representations, through high resolution reduced models, employing the united atom representation, to all-atom models of the molecular mechanics. Here I describe a new high resolution reduced model, its force field and applications in the structural proteomics. The model uses a lattice representation with 800 possible orientations of the virtual alpha carbon-alpha carbon bonds. The sampling scheme of the conformational space employs the Replica Exchange Monte Carlo method. Knowledge-based potentials of the force field include: generic protein-like conformational biases, statistical potentials for the short-range conformational propensities, a model of the main chain hydrogen bonds and context-dependent statistical potentials describing the side group interactions. The model is more accurate than the previously designed lattice models and in many applications it is complementary and competitive in respect to the all-atom techniques. The test applications include: the ab initio structure prediction, multitemplate comparative modeling and structure prediction based on sparse experimental data. Especially, the new approach to comparative modeling could be a valuable tool of the structural proteomics. It is shown that the new approach goes beyond the range of applicability of the traditional methods of the protein comparative modeling.

scholarly journals TRACER. A new approach to comparative modeling that combines threading with free-space conformational sampling.

2010 ◽  
Vol 57 (1) ◽  
Author(s):  
Sebastian Trojanowski ◽  
Aleksandra Rutkowska ◽  
Andrzej Kolinski
Keyword(s):  
Force Field ◽  
Sequence Similarity ◽  
Three Dimensional ◽  
Comparative Modeling ◽  
Query Protein ◽  
New Method ◽  
Conformational Space ◽  
Coarse Grained ◽  
Conformational Search ◽  
New Approach

A new approach to comparative modeling of proteins, TRACER, is described and benchmarked against classical modeling procedures. The new method unifies true three-dimensional threading with coarse-grained sampling of query protein conformational space. The initial sequence alignment of a query protein with a template is not required, although a template needs to be somehow identified. The template is used as a multi-featured fuzzy three-dimensional scaffold. The conformational search for the query protein is guided by intrinsic force field of the coarse-grained modeling engine CABS and by compatibility with the template scaffold. During Replica Exchange Monte Carlo simulations the model chain representing the query protein finds the best possible structural alignment with the template chain, that also optimizes the intra-protein interactions as approximated by the knowledge based force field of CABS. The benchmark done for a representative set of query/template pairs of various degrees of sequence similarity showed that the new method allows meaningful comparative modeling also for the region of marginal, or non-existing, sequence similarity. Thus, the new approach significantly extends the applicability of comparative modeling.

KORP: knowledge-based 6D potential for fast protein and loop modeling

2019 ◽  
Vol 35 (17) ◽  
pp. 3013-3019 ◽  
Author(s):  
José Ramón López-Blanco ◽  
Pablo Chacón
Keyword(s):  
Structure Prediction ◽  
Protein Structures ◽  
Joint Probability ◽  
Protein Modeling ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Loop Modeling ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Backbone Atoms

Abstract Motivation Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue–residue interactions from known protein structures using a simple backbone-based representation. Results We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function. Availability and implementation http://chaconlab.org/modeling/korp. Supplementary information Supplementary data are available at Bioinformatics online.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

scholarly journals High-resolution neutron imaging: a new approach to characterize water in anodic aluminum oxides

2020 ◽  
Vol 8 ◽  
pp. 100121
Author(s):  
Noémie Ott ◽  
Claudia Cancellieri ◽  
Pavel Trtik ◽  
Patrik Schmutz
Keyword(s):  
High Resolution ◽  
Neutron Imaging ◽  
Aluminum Oxides ◽  
New Approach ◽  
Anodic Aluminum

scholarly journals High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling

2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
High Resolution ◽  
Adaptive Sampling ◽  
Force Fields ◽  
Sampling Strategy ◽  
Md Simulations ◽  
Conformational Space ◽  
Many Body ◽  
Polarizable Force Fields ◽  
Main Protease

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).

Protein Loop Structure Prediction Using Conformational Space Annealing

2017 ◽  
Vol 57 (5) ◽  
pp. 1068-1078 ◽  
Author(s):  
Seungryong Heo ◽  
Juyong Lee ◽  
Keehyoung Joo ◽  
Hang-Cheol Shin ◽  
Jooyoung Lee
Keyword(s):  
Structure Prediction ◽  
Conformational Space ◽  
Loop Structure
Sign in / Sign up

Export Citation Format

Share Document