Finding quasi-modules of human and viral miRNAs: a case study of human cytomegalovirus (HCMV)

Background

MicroRNAs (miRNAs) are important regulators of gene expression encoded by a variety of organisms, including viruses. Although the function of most of the viral miRNAs is currently unknown, there is evidence that both viral and host miRNAs contribute to the interactions between viruses and their hosts. miRNAs constitute a complex combinatorial network, where one miRNA may target many genes and one gene may be targeted by multiple miRNAs. In particular, viral and host miRNAs may also have mutual target genes. Based on published evidence linking viral and host miRNAs there are three modes of mutual regulation: competing, cooperating, and compensating modes.

Results

In this paper we explore the compensating mode of mutual regulation upon Human Cytomegalovirus (HCMV) infection, when host miRNAs are down regulated and viral miRNAs compensate by mimicking their function. To achieve this, we develop a new algorithm which finds groups, called quasi-modules, of viral and host miRNAs and their mutual target genes, and use a new host miRNA expression data for HCMV-infected and uninfected cells. For two of the reported quasi-modules, supporting evidence from biological and medical literature is provided.

Conclusions

The modules found by our method may advance the understanding of the role of miRNAs in host-viral interactions, and the genes in these modules may serve as candidates for further experimental validation.

The microRNA Transcriptome of Human Cytomegalovirus (HCMV)

The purpose of the present study was to characterize the microRNA transcriptome (miRNAome) of the human cytomegalovirus (HCMV or HHV5). We used deep sequencing and real time PCR (qPCR) together with bioinformatics to analyze the pattern of small RNA expression in cells infected with low-passage isolates of HCMV as well as in plasma and amniotic fluid. We report here on the discovery of four new precursors and ten new miRNAs as well as eleven microRNA-offset-RNAs (moRs) that are all encoded by HCMV. About eighty percent of the total HCMV reads were perfectly mapped to HCMV miRNAs, strongly suggestive of their important biological role that in large remains still to be defined and characterized. Taken altogether, the results of this study demonstrate the power and usefulness of the combined bioinformatics/biological approach in discovering additional important members of HCMV- encoded small RNAs and can be applied to the study of other viruses as well.

A library of protein surface patches discriminates between native structures and decoys generated by structure prediction servers

Background

Protein surfaces serve as an interface with the molecular environment and are thus tightly bound to protein function. On the surface, geometric and chemical complementarity to other molecules provides interaction specificity for ligand binding, docking of bio-macromolecules, and enzymatic catalysis.

As of today, there is no accepted general scheme to represent protein surfaces. Furthermore, most of the research on protein surface focuses on regions of specific interest such as interaction, ligand binding, and docking sites. We present a first step toward a general purpose representation of protein surfaces: a novel surface patch library that represents most surface patches (~98%) in a data set regardless of their functional roles.

Results

Surface patches, in this work, are small fractions of the protein surface. Using a measure of inter-patch distance, we clustered patches extracted from a data set of high quality, non-redundant, proteins. The surface patch library is the collection of all the cluster centroids; thus, each of the data set patches is close to one of the elements in the library.

We demonstrate the biological significance of our method through the ability of the library to capture surface characteristics of native protein structures as opposed to those of decoy sets generated by state-of-the-art protein structure prediction methods. The patches of the decoys are significantly less compatible with the library than their corresponding native structures, allowing us to reliably distinguish native models from models generated by servers. This trend, however, does not extend to the decoys themselves, as their similarity to the native structures does not correlate with compatibility with the library.

Conclusions

We expect that this high-quality, generic surface patch library will add a new perspective to the description of protein structures and improve our ability to predict them. In particular, we expect that it will help improve the prediction of surface features that are apparently neglected by current techniques.

The surface patch libraries are publicly available at http://www.cs.bgu.ac.il/~keasar/patchLibrary.

Gene bi-targeting by viral and human miRNAs

BACKGROUND

MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs (20-24 nts) that can affect gene expression by post-transcriptional regulation of mRNAs. They play important roles in several biological processes (e.g., development and cell cycle regulation). Numerous bioinformatics methods have been developed to identify the function of miRNAs by predicting their target mRNAs. Some viral organisms also encode miRNAs, a fact that contributes to the complex interactions between viruses and their hosts. A need arises to understand the functional relationship between viral and host miRNAs and their effect on viral and host genes. Our approach to meet this challenge is to identify modules where viral and host miRNAs cooperatively regulate host gene expression.

RESULTS

We present a method to identify groups of viral and host miRNAs that cooperate in post-transcriptional gene regulation, and their target genes that are involved in similar biological processes. We call these groups (genes and miRNAs of human and viral origin) - modules. The modules are found in a new two-stage procedure, which we call bi-targeting, and is presented in this paper. The stages are (i) a new and efficient target prediction, and (ii) a new method for clustering objects of three different data types. In this work we integrate multiple information sources, including miRNA-target binding information, miRNA expression profiles, and GO annotations. Our hypotheses and the methods have been tested on human and Epstein Barr virus (EBV) miRNAs and human genes, for which we found 34 modules. We provide supporting evidence from biological and medical literature for two of our modules. Our code and data are available at http://www.cs.bgu.ac.il/~vaksler/BiTargeting.htm

CONCLUSIONS

The presented algorithm, which makes use of diverse biological data, is demonstrated to be an efficient approach for finding bi-targeting modules of viral and human miRNAs. These modules can contribute to a better understanding of viral-host interactions and the role that miRNAs play in them.

FASH: A web application for nucleotides sequence search

: FASH (Fourier Alignment Sequence Heuristics) is a web application, based on the Fast Fourier Transform, for finding remote homologs within a long nucleic acid sequence. Given a query sequence and a long text-sequence (e.g, the human genome), FASH detects subsequences within the text that are remotely-similar to the query. FASH offers an alternative approach to Blast/Fasta for querying long RNA/DNA sequences. FASH differs from these other approaches in that it does not depend on the existence of contiguous seed-sequences in its initial detection phase. The FASH web server is user friendly and very easy to operate.

AVAILABILITY

FASH can be accessed athttps://fash.bgu.ac.il:8443/fash/default.jsp (secured website).

A structure-based flexible search method for motifs in RNA

The discovery of non-coding RNA (ncRNA) motifs and their role in regulating gene expression has recently attracted considerable attention. The goal is to discover these motifs in a sequence database. Current RNA motif search methods start from the primary sequence and only then take into account secondary structure considerations. One can think of developing a flexible structure-based motif search method that will filter datasets based on secondary structure first, while allowing extensive primary sequence factors and additional factors such as potential pseudoknots as constraints. Since different motifs vary in structure rigidity and in local sequence constraints, there is a need for algorithms and tools that can be fine-tuned according to the searched RNA motif, but differ in their approach from the RNAMotif descriptor language. We present an RNA motif search tool called STRMS (Structural RNA Motif Search), which takes as input the secondary structure of the query, including local sequence and structure constraints, and a target sequence database. It reports all occurrences of the query in the target, ranked by their similarity to the query, and produces an html file that displays graphical images of the predicted structures for both the query and the candidate hits. Our tool is flexible and takes into account a large number of sequence options and existence of potential pseudoknots as dictated by specific queries. Our approach combines pre-folding and an O(m n) RNA pattern matching algorithm based on subtree homeomorphism for ordered, rooted trees. An O(n(2) log n) extension is described that allows the search engine to take into account the pseudoknots typical to riboswitches. We employed STRMS in search for both new and known RNA motifs (riboswitches and tRNAs) in large target databases. Our results point to a number of additional purine bacterial riboswitch candidates in newly sequenced bacteria, and demonstrate high sensitivity on known riboswitches and tRNAs. Code and data are available at www.cs.bgu.ac.il/vaksler/STRMS.

Computational identification of three-way junctions in folded RNAs: a case study in Arabidopsis

Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.

The URMS-RMS hybrid algorithm for fast and sensitive local protein structure alignment

We present an efficient and sensitive hybrid algorithm for local structure alignment of a pair of 3D protein structures. The hybrid algorithm employs both the URMS (unit-vector root mean squared) metric and the RMS metric. Our algorithm searches efficiently the transformation space using a fast screening protocol; initial transformations (rotations) are identified using the URMS algorithm. These rotations are then clustered and an RMS-based dynamic programming algorithm is invoked to find the maximal local similarities for representative rotations of the clusters. Statistical significance of the alignments is estimated using a model that accounts for both the score of the match and the RMS. We tested our algorithm over the SCOP classification of protein domains. Our algorithm performs very well; its main advantages are that (1) it combines the advantages of the RMS and the URMS metrics, (2) it searches extensively the transformation space, (3) it detects complex similarities and structural repeats, and (4) its results are symmetric. The software is available for download at biozon.org/ftp/software/urms/.

STR2: a structure to string approach for locating G-box riboswitch shapes in pre-selected genes

Traditional sequence-based search methods such as BLAST and FASTA can be used to identify sequence similarities. Recently, there is a growing interest in performing RNA shape similarity searches inside selected genes to locate RNA structure motifs that are known to possess functionally important roles. For example, in the newly discovered RNA genetic control elements called "riboswitches", the box domain is known to be highly conserved among various bacterial species in both its nucleotide composition and shape. However, in non-bacterial species, shape conservation is likely to become more important than sequence conservation when searching for riboswitch patterns. For this purpose, we present an approach tailored for detecting RNA shape similarities. We extend the Structure to String (ST R2) method that was initially proposed to locate shape similarities in proteins to identify predicted secondary structures of RNAs. The ST R2 for RNAs is a translation of a secondary structure to a string of characters, after which known sequence-based search algorithms with an efficient implementation are being used. We validate that the ST R2 succeeds to locate G-box riboswitches in prokaryotes, as expected. Subsequently we show running examples when attempting to detect G-box riboswitch candidates in eukaryotes.

Comparative analysis of dendritic architecture of identified neurons using the Hausdorff distance metric

Dendritic trees often are complex, three-dimensional structures. Comparative morphologic studies have not yet provided a reliable measure to analyze and compare the geometry of different dendritic trees. Therefore, it is important to develop quantitative methods for analyzing the three-dimensional geometry of these complex trees. The authors developed a comparison measure based on the Hausdorff distance for comparing quantitatively the three-dimensional structure of different neurons. This algorithm was implemented and incorporated into a new software package that the authors developed called NeuroComp. The authors tested this algorithm to study the variability in the three-dimensional structure of identified central neurons as well as measuring the structural differences between homologue neurons. They took advantage of the uniform dendritic morphology of identified interneurons of an insect, the giant interneurons of the cockroach. More specifically, after establishing a morphometric data base of these neurons, the authors found that the algorithm is a reliable tool for distinguishing between dendritic trees of different neurons, whereas conventional metric analysis often is inadequate. The authors propose to use this method as a quantitative tool for the investigation of the effects of various experimental paradigms on three-dimensional dendritic architecture.

Fast detection of common geometric substructure in proteins

We consider the problem of identifying common three-dimensional substructures between proteins. Our method is based on comparing the shape of the alpha-carbon backbone structures of the proteins in order to find three-dimensional (3D) rigid motions that bring portions of the geometric structures into correspondence. We propose a geometric representation of protein backbone chains that is compact yet allows for similarity measures that are robust against noise and outliers. This representation encodes the structure of the backbone as a sequence of unit vectors, defined by each adjacent pair of alpha-carbons. We then define a measure of the similarity of two protein structures based on the root mean squared (RMS) distance between corresponding orientation vectors of the two proteins. Our measure has several advantages over measures that are commonly used for comparing protein shapes, such as the minimum RMS distance between the 3D positions of corresponding atoms in two proteins. A key advantage is that this new measure behaves well for identifying common substructures, in contrast with position-based measures where the nonmatching portions of the structure dominate the measure. At the same time, it avoids the quadratic space and computational difficulties associated with methods based on distance matrices and contact maps. We show applications of our approach to detecting common contiguous substructures in pairs of proteins, as well as the more difficult problem of identifying common protein domains (i.e., larger substructures that are not necessarily contiguous along the protein chain).

Unit-vector RMS (URMS) as a tool to analyze molecular dynamics trajectories

The Unit-vector RMS (URMS) is a new technique to compare protein chains and to detect similarities of chain segments. It is limited to comparison of C(alpha) chains. However, it has a number of unique features that include exceptionally weak dependence on the length of the chain and efficient detection of substructure similarities. Two molecular dynamics simulations of proteins in the neighborhood of their native states are used to test the performance of the URMS. The first simulation is of a solvated myoglobin and the second is of the protein MHC. In accord with previous studies the secondary structure elements (helices or sheets) are found to be moving relatively rigidly among flexible loops. In addition to these tests, folding trajectories of C peptides are analyzed, revealing a folding nucleus of seven amino acids.