Exploring RNA and protein 3D structures by geometric algorithms

Many problems in RNA and protein structures are related with their specific geometric properties. Geometric algorithms can be used to explore the possible solutions of these problems. This dissertation investigates the geometric properties of RNA and protein structures and explores three different ways that geometric algorithms can help to the study of the structures. Determine accurate structures. Accurate details in RNA structures are important for understanding RNA function, but the backbone conformation is difficult to determine and most existing RNA structures show serious steric clashes (greater than or equal to 0.4 A overlap). I developed a program called RNABC (RNA Backbone Correction) that searches for alternative clash-free conformations with acceptable geometry. It rebuilds a suite (unit from sugar to sugar) by anchoring phosphorus and base positions, which are clearest in crystallographic electron density, and reconstructing other atoms using forward kinematics and conjugate gradient methods. Two tests show that RNABC improves backbone conformations for most problem suites in S-motifs and for many of the worst problem suites identified by members of the Richardson lab. Display structure commonalities. Structure alignment commonly uses root mean squared distance (RMSD) to measure the structural similarity. I first extend RMSD to weighted RMSD (wRMSD) for multiple structures and show that using wRMSD with multiplicative weights implies the average is a consensus structure. Although I show that finding the optimal translations and rotations for minimizing wRMSD cannot be decoupled for multiple structures, I develop a near-linear iterative algorithm to converge to a local minimum of wRMSD. Finally I propose a heuristic algorithm to iteratively reassign weights to reduce the effect of outliers and find well-aligned positions that determine structurally conserved regions. Distinguish local structural features. Identifying common motifs (fragments of structures common to a group of molecules) is one way to further our understanding of the structure and function of molecules. I apply a graph database mining technique to identify RNA tertiary motifs. I abstract RNA molecules as labeled graphs, use a frequent subgraph mining algorithm to derive tertiary motifs, and present an iterative structure alignment algorithm to classify tertiary motifs and generate consensus motifs. Tests on ribosomal and transfer RNA families show that this method can identify most known RNA tertiary motifs in these families and suggest candidates for novel tertiary motifs

Ab initio RNA folding

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, experimental determination of RNA structures through X-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 figure

A data science approach to pattern discovery in complex structures with applications in bioinformatics

Pattern discovery aims to find interesting, non-trivial, implicit, previously unknown and potentially useful patterns in data. This dissertation presents a data science approach for discovering patterns or motifs from complex structures, particularly complex RNA structures. RNA secondary and tertiary structure motifs are very important in biological molecules, which play multiple vital roles in cells. A lot of work has been done on RNA motif annotation. However, pattern discovery in RNA structure is less studied. In the first part of this dissertation, an ab initio algorithm, named DiscoverR, is introduced for pattern discovery in RNA secondary structures. This algorithm works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using a quadratic time dynamic programming algorithm. The algorithm is able to identify and extract the largest common substructures from two RNA molecules of different sizes, without prior knowledge of locations and topologies of these substructures. One application of DiscoverR is to locate the RNA structural elements in genomes. Experimental results show that this tool complements the currently used approaches for mining conserved structural RNAs in the human genome. DiscoverR can also be extended to find repeated regions in an RNA secondary structure. Specifically, this extended method is used to detect structural repeats in the 3\u27-untranslated region of a protein kinase gene

VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network Motifs

RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax these constraints by posing the motif finding problem as a graph representation learning and clustering task. This framing takes advantage of the continuous nature of graph representations to model the flexibility and variability of RNA motifs in an efficient manner. We propose a set of node similarity functions, clustering methods, and motif construction algorithms to recover flexible RNA motifs. Our tool, VeRNAl can be easily customized by users to desired levels of motif flexibility, abundance and size. We show that VeRNAl is able to retrieve and expand known classes of motifs, as well as to propose novel motifs

Aminormotiffinder - a graph grammar based tool to effectively search a minor motifs in 3D RNA molecules

RNA Motifs are three dimensional folds that play important role in RNA folding and its interaction with other molecules. They basically have modular structure and are composed of conserved building blocks dependent upon the sequence. Their automated in silico identification remains a challenging task. Existing motif identification tools does not correctly identify motifs with large structure variations. Here a “graph rewriting” based method is proposed to identify motifs in real three dimensional structures. The unique encoding of A Minor Searcher takes into consideration the non canonical base pairs and also multipairing of RNA structural motifs. The accuracy is demonstrated by correctly predicting A minor motifs across many PDB files with zero false positives. There is a huge demand of a good well developed RNA Motif identification algorithm that would successfully identify both canonical / non canonical and isomorphic motifs. In this thesis, a novel encoding algorithm is demonstrated that successfully identifies RNA A Minor Motifs from 3D RNAs. The algorithm encodes the three dimensional RNA Data into one dimension without losing any tertiary information during the transition. A Minor motif is then searched in this one dimensional string using exhaustive search technique with linear time complexity. The efficiency is demonstrated by the comparison of AMinorSearcher with benchmark tool FR3D. FR3D lacked in both precision and recall while AMinorSearcher did not

Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis

RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction. In this dissertation, a cyberinfrastructure, named RNAcyber, capable of performing RNA motif search and prediction, is proposed, designed and implemented. The first component of RNAcyber is a web-based search engine, named RmotifDB. This web-based tool integrates an RNA secondary structure comparison algorithm with the secondary structure motifs stored in the Rfam database. With a user-friendly interface, RmotifDB provides the ability to search for ncRNA structure motifs in both structural and sequential ways. The second component of RNAcyber is an enhanced version of RmotifDB. This enhanced version combines data from multiple sources, incorporates a variety of well-established structure-based search methods, and is integrated with the Gene Ontology. To display RmotifDB’s search results, a software tool, called RSview, is developed. RSview is able to display the search results in a graphical manner. Finally, RNAcyber contains a web-based tool called Junction-Explorer, which employs a data mining method for predicting tertiary motifs in RNA junctions. Specifically, the tool is trained on solved RNA tertiary structures obtained from the Protein Data Bank, and is able to predict the configuration of coaxial helical stacks and families (topologies) in RNA junctions at the secondary structure level. Junction-Explorer employs several algorithms for motif prediction, including a random forest classification algorithm, a pseudoknot removal algorithm, and a feature ranking algorithm based on the gini impurity measure. A series of experiments including 10-fold cross- validation has been conducted to evaluate the performance of the Junction-Explorer tool. Experimental results demonstrate the effectiveness of the proposed algorithms and the superiority of the tool over existing methods. The RNAcyber infrastructure is fully operational, with all of its components accessible on the Internet

Towards protein function annotations for matching remote homologs

Identifying functional similarities for proteins with low sequence identity and low structure similarity often suffers from high false positives and false negatives results. To improve the functional prediction ability based on the local protein structures, we proposed two different refinement and filtering approaches. We built a statistical model (known as Markov Random Field) to describe protein functional site structure. We also developed filters that consider the local environment around the active sites to remove the false positives. Our experimental results, as evaluated in five sets of enzyme families with less than 40% sequence identity, demonstrated that our methods can obtain more remote homologs that could not be detected by traditional sequence-based methods. At the same time, our method could reduce large amount of random matches. Our methods could improve up to 70% of the functional annotation ability (measured by their Area under the ROC curve) in extended motif method

Automatic Exploration of the Natural Variability of RNA Non-Canonical Geometric Patterns with a Parameterized Sampling Technique

Motivation. Recurrent substructures in RNA, known as 3D motifs, consist of networks of base pair interactions and are critical to understanding the relationship between structure and function. Their structure is naturally expressed as a graph which has led to many graph-based algorithms to automatically catalog identical motifs found in 3D structures. Yet, due to the complexity of the problem, state-of-the-art methods are often optimized to find exact matches, limiting the search to a subset of potential solutions, or do not allow explicit control over the desired variability. Results. We developed FuzzTree, a method able to efficiently sample approximate instances of an RNA motif, abstracted as a subgraph within a target RNA structure. It is the first method that allows explicit control over (1) the admissible geometric variability in the interactions; (2) the number of missing edges; and (3) the introduction of discontinuities in the backbone given close distances in the 3D structure. Our tool relies on a multidimensional Boltzmann sampling, having complexity parameterized by the treewidth of the requested motif. We applied our method to the well-known internal loop Kink-Turn motif, which can be divided into 12 subgroups. Given only the graph representing the main Kink-Turn subgroup, FuzzTree retrieved over 3/4 of all kink-turns. We also highlighted two occurrences of new sampled patterns. Our tool is available as free software and can be customized for different parameters and types of graphs

Big data analytics in computational biology and bioinformatics

Big data analytics in computational biology and bioinformatics refers to an array of operations including biological pattern discovery, classification, prediction, inference, clustering as well as data mining in the cloud, among others. This dissertation addresses big data analytics by investigating two important operations, namely pattern discovery and network inference. The dissertation starts by focusing on biological pattern discovery at a genomic scale. Research reveals that the secondary structure in non-coding RNA (ncRNA) is more conserved during evolution than its primary nucleotide sequence. Using a covariance model approach, the stems and loops of an ncRNA secondary structure are represented as a statistical image against which an entire genome can be efficiently scanned for matching patterns. The covariance model approach is then further extended, in combination with a structural clustering algorithm and a random forests classifier, to perform genome-wide search for similarities in ncRNA tertiary structures. The dissertation then presents methods for gene network inference. Vast bodies of genomic data containing gene and protein expression patterns are now available for analysis. One challenge is to apply efficient methodologies to uncover more knowledge about the cellular functions. Very little is known concerning how genes regulate cellular activities. A gene regulatory network (GRN) can be represented by a directed graph in which each node is a gene and each edge or link is a regulatory effect that one gene has on another gene. By evaluating gene expression patterns, researchers perform in silico data analyses in systems biology, in particular GRN inference, where the “reverse engineering” is involved in predicting how a system works by looking at the system output alone. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioin-formatics tools capable of performing perfect GRN inference. Here, extensive experiments are conducted to evaluate and compare recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these tools based on both simulated and real data sets are generally low, suggesting that further efforts are needed to develop more reliable GRN inference tools. It is also observed that using multiple tools together can help identify true regulatory interactions between genes, a finding consistent with those reported in the literature. Finally, the dissertation discusses and presents a framework for parallelizing GRN inference methods using Apache Hadoop in a cloud environment

RAG-3D: a search tool for RNA 3D substructures

To address many challenges in RNA structure/function prediction, the characterization of RNA\u27s modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding