Improving RNA secondary structure prediction using direct coupling analysis

Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs.In the last decades,a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck.Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods.We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.

A direct coupling analysis method and its application to the Scientific Research and Demonstration Platform

Design of a very large floating structure(VLFS)deployed near islands and reefs,different from those in the open sea,inevitably faces new technical challenges including numerical analysis methods.In this paper,a direct coupling analysis method(DCAM)has been established based on the Boussinesq equations and the three-dimensional hydroelasisity theory with Rankine source method to analyze the responses of a VLFS in shallow sea with complicated geographical environment.Model tests have been carried out to validate the DCAM.To further verify the numerical methods and investigate the performance of such a VLFS,a“Scientific Research and Demonstration Platform(SRDP)”was built and deployed in 2019 at the site about 1000 m off an island with water depth around 40m in South China Sea.It is a simplified small model of a two-module semi-submersible-type VLFS.The numerical simulation of its responses on severe waves with focus on motions and connector forces is conduct by DCAM,and compared with the on-site measurements.Good agreement has been achieved.This approves the DCAM as a feasible tool for design and safety assessment of a VLFS deployed near islands and reefs.

Methods and applications of RNA contact prediction

The RNA tertiary structure is essential to understanding the function and biological processes. Unfortunately, it is still challenging to determine the large RNA structure from direct experimentation or computational modeling. One promising approach is first to predict the tertiary contacts and then use the contacts as constraints to model the structure. The RNA structure modeling depends on the contact prediction accuracy. Although many contact prediction methods have been developed in the protein field, there are only several contact prediction methods in the RNA field at present. Here, we first review the theoretical basis and test the performances of recent RNA contact prediction methods for tertiary structure and complex modeling problems. Then, we summarize the advantages and limitations of these RNA contact prediction methods. We suggest some future directions for this rapidly expanding field in the last.