All-Atom Knowledge-Based Potential for RNA Structure Discrimination Based on the Distance-Scaled Finite Ideal-Gas Reference State

2020 ◽  
Vol 27 (6) ◽  
pp. 856-867 ◽  
Author(s):  
Tongchuan Zhang ◽  
Guodong Hu ◽  
Yuedong Yang ◽  
Jihua Wang ◽  
Yaoqi Zhou
Keyword(s):  
Rna Structure ◽  
Ideal Gas ◽  
Reference State ◽  
Knowledge Based

scholarly journals EVALUATING MIXTURE MODELS FOR BUILDING RNA KNOWLEDGE-BASED POTENTIALS

2012 ◽  
Vol 10 (02) ◽  
pp. 1241010 ◽  
Author(s):  
ADELENE Y. L. SIM ◽  
OLIVIER SCHWANDER ◽  
MICHAEL LEVITT ◽  
JULIE BERNAUER
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Rna Structure ◽  
Structural Information ◽  
A Priori ◽  
Comparable Efficacy ◽  
Rna Structures ◽  
Rna Molecules ◽  
Knowledge Based ◽  
Spline Fitting

Ribonucleic acid (RNA) molecules play important roles in a variety of biological processes. To properly function, RNA molecules usually have to fold to specific structures, and therefore understanding RNA structure is vital in comprehending how RNA functions. One approach to understanding and predicting biomolecular structure is to use knowledge-based potentials built from experimentally determined structures. These types of potentials have been shown to be effective for predicting both protein and RNA structures, but their utility is limited by their significantly rugged nature. This ruggedness (and hence the potential's usefulness) depends heavily on the choice of bin width to sort structural information (e.g. distances) but the appropriate bin width is not known a priori. To circumvent the binning problem, we compared knowledge-based potentials built from inter-atomic distances in RNA structures using different mixture models (Kernel Density Estimation, Expectation Minimization and Dirichlet Process). We show that the smooth knowledge-based potential built from Dirichlet process is successful in selecting native-like RNA models from different sets of structural decoys with comparable efficacy to a potential developed by spline-fitting — a commonly taken approach — to binned distance histograms. The less rugged nature of our potential suggests its applicability in diverse types of structural modeling.

scholarly journals All-atom knowledge-based potential for RNA structure prediction and assessment

2011 ◽  
Vol 27 (8) ◽  
pp. 1086-1093 ◽  
Author(s):  
Emidio Capriotti ◽  
Tomas Norambuena ◽  
Marc A. Marti-Renom ◽  
Francisco Melo
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Structure Prediction ◽  
Knowledge Based

scholarly journals Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

2021 ◽  
Author(s):  
Stefano Zamuner ◽  
Flavio Seno ◽  
Antonio Trovato
Keyword(s):  
Effective Interaction ◽  
Polymer Melt ◽  
Coarse Graining ◽  
Data Bank ◽  
Polymer Chains ◽  
Reference State ◽  
Protein Chain ◽  
Statistical Potentials ◽  
Knowledge Based ◽  
Wide Range

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, we use it to define a `bona fide' reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.

scholarly journals Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

RNA ◽  
2011 ◽  
Vol 17 (6) ◽  
pp. 1066-1075 ◽  
Author(s):  
J. Bernauer ◽  
X. Huang ◽  
A. Y. L. Sim ◽  
M. Levitt
Keyword(s):  
Rna Structure ◽  
Coarse Grained ◽  
Knowledge Based ◽  
Structure Evaluation

scholarly journals Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peng Xiong ◽  
Ruibo Wu ◽  
Jian Zhan ◽  
Yaoqi Zhou
Keyword(s):  
Rna Structure ◽  
Global Minimum ◽  
Structure Refinement ◽  
Orientation Dependence ◽  
Native Structure ◽  
Sampling Algorithm ◽  
Knowledge Based ◽  
Oxygen Oxygen ◽  
Rna Backbone ◽  
Modelling Techniques

AbstractRefining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques.

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