title: RNA Structure Prediction using Deep Neural Network Architectures and Improved Evolutionary Profiles
Creators: name: Singh, Jaswinder
Publisher: name: Griffith University
Year: 2022
Subjects: subject: RNAs
subject: biological processes.
subject: 3D
subject: deep learning
subject: algorithms
subject: predicting
subject: restraints
subject: SPOT-RNA
subject: SPOT-RNA2
subject: backbone

description: RNAs are important biological macro-molecules that play critical roles in many biological processes. The functionality of RNA depends on its three-dimensional (3D) structure, which further depends on its primary structure, i.e. the order of sequence of nucleotides in the RNA chain. Direct prediction of the 3D structure of an RNA from its sequence is a challenging task. Therefore, the 3D structure is further divided into two-dimensional (2D) properties such as secondary structure, contact maps and one-dimensional (1D) properties such as torsion angles and solvent accessibility. An accurate prediction of these 1D and 2D structural properties will increase the accuracy in predicting the 3D structure of the RNA. This thesis explores various deep learning algorithms and input features relevant to predicting the 1D and 2D structural properties of an RNA. Using these predicted 1D and 2D structural properties further as restraints, we have demonstrated an improvement in the prediction of the RNA 3D structure. There are four primary studies performed in this thesis for RNA structural properties prediction. The first study introduces two methods (SPOT-RNA and SPOT-RNA2) for RNA secondary structure prediction using an ensemble of Residual Con-volution and Bi-directional LSTM recurrent neural networks. This study shows that deep learning based methods can outperform existing dynamic programming based algorithms and achieve state-of-the-art performance using single-sequence and evolutionary information as input. The second study investigates the application of deep neural networks for predicting RNA backbone torsion and pseudotorsion angles. We have pioneered in predicting the backbone torsion and pseudotorsion angles using deep learning (SPOT-RNA-1D). The angles predicted using SPOT-RNA-1D could be used as 3D model quality indicators. The third study introduces a method (SPOT-RNA-2D) to predict RNA distance-based contact maps using an ensemble of deep neural networks and improved evolutionary profles from RNAcmap. This study shows that the use of predicted distance-based contact maps as restraints can signifcantly improve the performance of 3D structure prediction. The fourth study developed a fully automated pipeline (RNAcmap2) to generate aligned homologs. Here, we showed that using a combination of BLAST-N and iterative INFERNAL searches along with an expanded sequence database leads to multiple sequence alignments (MSA) comparable to those provided by Rfam MSAs according to secondary structure extracted from mutational coupling analysis and alignment accuracy when compared to structural alignment. This fully automatic tool (RNAcmap2) allows to search homolog, multiple sequence alignment, and mutational coupling analysis for any non-Rfam RNA sequences with Rfam-like performance. The improved RNA 1D and 2D structural properties predictions using deep learning along with improved homolog search collectively is expected to be useful in predicting RNA three-dimensional structure and better un-derstand its biological function.