Dataset Information

Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.

ABSTRACT:

Motivation

RNA folding kinetics plays an important role in the biological functions of RNA molecules. An important goal in the investigation of the kinetic behavior of RNAs is to find the folding pathway with the lowest energy barrier. For this purpose, most of the existing methods use heuristics because the number of possible pathways is huge even if only the shortest (direct) folding pathways are considered.

Results

In this study, we propose a new method using a best-first search strategy to efficiently compute the exact solution of the minimum barrier energy of direct pathways. Using our method, we can find the exact direct pathways within a Hamming distance of 20, whereas the previous methods even miss the exact short pathways. Moreover, our method can be used to improve the pathways found by existing methods for exploring indirect pathways.

Availability and implementation

The source code and datasets created and used in this research are available at https://github.com/eukaryo/czno.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Takizawa H

PROVIDER: S-EPMC7355307 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.

Takizawa Hiroki H Iwakiri Junichi J Terai Goro G Asai Kiyoshi K

Bioinformatics (Oxford, England) 20200701 Suppl_1

<h4>Motivation</h4>RNA folding kinetics plays an important role in the biological functions of RNA molecules. An important goal in the investigation of the kinetic behavior of RNAs is to find the folding pathway with the lowest energy barrier. For this purpose, most of the existing methods use heuristics because the number of possible pathways is huge even if only the shortest (direct) folding pathways are considered.<h4>Results</h4>In this study, we propose a new method using a best-first searc ...[more]

PMID: 32657400

Similar Datasets

Project description:In prokaryotic genomes the number of transcriptional regulators is known to be proportional to the square of the total number of protein-coding genes. A toolbox model of evolution was recently proposed to explain this empirical scaling for metabolic enzymes and their regulators. According to its rules, the metabolic network of an organism evolves by horizontal transfer of pathways from other species. These pathways are part of a larger "universal" network formed by the union of all species-specific networks. It remained to be understood, however, how the topological properties of this universal network influence the scaling law of functional content of genomes in the toolbox model. Here we answer this question by first analyzing the scaling properties of the toolbox model on arbitrary tree-like universal networks. We prove that critical branching topology, in which the average number of upstream neighbors of a node is equal to one, is both necessary and sufficient for quadratic scaling. We further generalize the rules of the model to incorporate reactions with multiple substrates/products as well as branched and cyclic metabolic pathways. To achieve its metabolic tasks, the new model employs evolutionary optimized pathways with minimal number of reactions. Numerical simulations of this realistic model on the universal network of all reactions in the KEGG database produced approximately quadratic scaling between the number of regulated pathways and the size of the metabolic network. To quantify the geometrical structure of individual pathways, we investigated the relationship between their number of reactions, byproducts, intermediate, and feedback metabolites. Our results validate and explain the ubiquitous appearance of the quadratic scaling for a broad spectrum of topologies of underlying universal metabolic networks. They also demonstrate why, in spite of "small-world" topology, real-life metabolic networks are characterized by a broad distribution of pathway lengths and sizes of metabolic regulons in regulatory networks.

Dataset Information

Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets