Biomedical subjects

Danny Barash

Publications and source records attributed to Danny Barash.

11 recordsLinked to original sources

PubMed2007

In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions.

BACKGROUND: A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA. RESULTS: In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression. CONCLUSION: We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.

Computational Biology↗

PubMed2006

RNAmute: RNA secondary structure mutation analysis tool.

BACKGROUND: RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation. RESULTS: Selecting a category in each one of the processed tables lists all single point mutations belonging to that category. Selecting a mutation displays a graphical drawing of the single point mutation and the wild type, and includes basic information such as associated energies, representations and distances. RNAMute can be used successfully with very little previous experience and without choosing any parameter value alongside the initial RNA sequence. The package runs under LINUX operating system. CONCLUSION: RNAMute is a user friendly tool that can be used to predict single point mutations leading to conformational rearrangements in the secondary structure of RNAs. In several cases of substantial interest, notably in virology, a point mutation may lead to a loss of important functionality such as the RNA virus replication and translation initiation because of a conformational rearrangement in the secondary structure.

Animals↗

PubMed2006

Primordia vita. Deconvolution from modern sequences.

Evolution of the triplet code is reconstructed on the basis of consensus temporal order of appearance of amino acids. Several important predictions are confirmed by computational sequence analyses. The earliest amino acids, alanine and glycine, have been encoded by GCC and GGC codons, as today. They were succeeded, respectively, by A- and G-series of amino acids, encoded by pyrimidine-central and purine-central codons. The length of the earliest proteins is estimated to be 6-7 residues. The earliest mRNAs were short G+C-rich molecules. These short sequences could have formed hairpins. This is confirmed by analysis of modern prokaryotic mRNA sequences. Predominant size of detected ancient hairpins also corresponds to 6-7 amino acids, as above. Vestiges of last common ancestor can be found in extant proteins in form of entirely conserved short sequences of size six to nine residues present in all or almost all sequenced prokaryotic proteomes (omnipresent motifs). The functions of the topmost conserved octamers are not involved in the basic elementary syntheses. This suggests an initial abiotic supply of amino acids, bases and sugars.

Base Sequence↗

PubMed2006

Shape similarity measures for the design of small RNA switches.

Conformational switching in the secondary structure of RNAs has recently attracted considerable attention, fostered by the discovery of 'riboswitches' in living organisms. These are genetic control elements that were found in bacteria and offer a unique regulation mechanism based on switching between two highly stable states, separated by an energy barrier between them. In riboswitches, the energy barrier is crossed by direct metabolite binding, which facilitates regulation by allosteric means. However, other event triggers can cause switching to occur, such as single-point mutations and slight variations in temperature. Examples of switches with these event triggers have already been reported experimentally in the past. Here, the goal is to computationally design small RNA switches that rely on these triggers. Towards this end, our computer simulations utilize a variety of different similarity measures to assess the distances between an initial state and triggered states, based on the topology of the secondary structure itself. We describe these combined similarity measures that rely on both coarse-grained and fine-grained graph representations of the RNA secondary structure. As a result of our simulations, we provide some candidate sequences of approximately 30-50 nt, along with the exact triggers that drive the switching. The event triggers under consideration can be modelled by Zuker's mfold or the Vienna package. The proposed methodology that rely on shape measures can further be used to computationally generate more candidates by simulating various event triggers and calculating their effect on the shape.

Nucleic Acid Conformation↗

PubMed2006

Tracing ancient mRNA hairpins.

From recent developments of the early evolution theory it follows that the earliest mRNAs were short ( approximately 20 nt) (G+C)-rich polynucleotides. These short sequences could form hairpins, which would be of high evolutionary advantage because of stability and uniqueness of their conformations. Due to mutations accumulated during billions of years of evolution, the speculated earliest hairpins would largely lose the initial complementarities. Some of the original complementary base-to-base contacts, however, may have survived. Computational analysis of modern prokaryotic mRNA sequences reveals excess population of the expected short range complementarities. The derived earliest mRNA hairpin size fully corresponds to the predicted size of ancient coding duplexes. The repertoire of the surviving hairpins traced in modern mRNA confirms duplex structure of the earliest mRNA, suggested by the early molecular evolution theory.

Bacteria↗

PubMed2004

Second eigenvalue of the Laplacian matrix for predicting RNA conformational switch by mutation.

MOTIVATION: Conformational switching in RNAs is thought to be of fundamental importance in several biological processes, including translational regulation, regulation of self-cleavage in viruses, protein biosynthesis and mRNA splicing. Current methods for detecting bi-stable RNAs that can lead to structural switching when triggered by an outside event rely on kinetics, energetics and properties of the combinatorial structure space of RNAs. Based on these properties, tools have been developed to predict whether a given sequence folds to a structure characterized by a bi-stable conformation, or to design multi-stable RNAs by an iterative algorithm. A useful addition is in developing a local procedure to prescribe, given an initial sequence, the least amount of mutations needed to drive the system into an optimal bi-stable conformation. RESULTS: We introduce a local procedure for predicting mutations, by generating and analyzing eigenvalue tables, that are capable of transforming the wild-type sequence into a bi-stable conformation. The method is independent of the folding algorithms but relies on their success. It can be used in conjunction with existing tools, as well as being incorporated into more general RNA prediction packages. We apply this procedure on three well-studied structures. First, the method is validated on the mutation leading to a conformational switch in the spliced leader RNA from Leptomonas collosoma, a mutation that has already been confirmed by an experiment. Second, the method is used to predict a mutation that can lead to a novel conformational switch in the P5abc subdomain of the group I intron ribozyme in Tetrahymena thermophila. Third, the method is applied on Hepatitis delta virus to predict mutations that transform the wild-type into a bi-stable conformation, a configuration assessed by calculating the free energies using folding prediction algorithms. The predictions in the final examples need to be verified experimentally, whereas the mutation predicted in the first example complies with the experiment. This supports the use of our proposed method on other known structures, as well as genetically engineered ones. AVAILABILITY: An eigenvalue application will be available in the near future attached to one of the existing tools.

Algorithms↗

PubMed2004

Spectral decomposition for the search and analysis of RNA secondary structure.

Scales in RNA, based on geometrical considerations, can be exploited for the analysis and prediction of RNA structures. By using spectral decomposition, geometric information that relates to a given RNA fold can be reduced to a single positive scalar number, the second eigenvalue of the Laplacian matrix corresponding to the tree-graph representation of the RNA secondary structure. Along with the free energy of the structure, being the most important scalar number in the prediction of RNA folding by energy minimization methods, the second eigenvalue of the Laplacian matrix can be used as an effective signature for locating a target folded structure given a set of RNA folds. Furthermore, the second eigenvector of the Laplacian matrix can be used to partition large RNA structures into smaller fragments. An illustrative example is given for the use of the second eigenvalue to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs.

Archaea↗

PubMed2004

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.

Algorithms↗

PubMed2003

Deleterious mutation prediction in the secondary structure of RNAs.

Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the field of RNAs where very few homologs are available at present, this analogy can serve as a precursor to investigate a deleterious mutation prediction approach that is based on RNA secondary structure. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algorithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation prediction method, based on subdivision of the initial structure into smaller substructures and construction of eigenvalue tables, is independent of the folding algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control.

Algorithms↗

PubMed2003

Inherent speedup limitations in multiple time step/particle mesh Ewald algorithms.

Multiple time step (MTS) algorithms present an effective integration approach to reduce the computational cost of dynamics simulations. By using force splitting to allow larger time steps for the more slowly varying force components, computational savings can be realized. The Particle-Mesh-Ewald (PME) method has been independently devised to provide an effective and efficient treatment of the long-range electrostatics interactions. Here we examine the performance of a combined MTS/PME algorithm previously developed for AMBER on a large polymerase beta/DNA complex containing 40,673 atoms. Our goal is to carefully combine the robust features of the Langevin/MTS (LN) methodology implemented in CHARMM-which uses position rather than velocity Verlet with stochasticity to make possible outer time steps of 150 fs-with the PME formulation. The developed MTS/PME integrator removes fast terms from the reciprocal-space Ewald component by using switch functions. We analyze the advantages and limitations of the resulting scheme by comparing performance to the single time step leapfrog Verlet integrator currently used in AMBER by evaluating different time-step protocols using three assessors for accuracy, speedup, and stability, all applied to long (i.e., nanosecond) simulations to ensure proper energy conservation. We also examine the performance of the algorithm on a parallel, distributed shared-memory computer (SGI Origin 2000 with 8 300-MHz R12000 processors). Good energy conservation and stability behavior can be demonstrated, for Newtonian protocols with outer time steps of up to 8 fs and Langevin protocols with outer time steps of up to 16 fs. Still, we emphasize the inherent limitations imposed by the incorporation of MTS methods into the PME formulation that may not be widely appreciated. Namely, the limiting factor on the largest outer time-step size, and hence speedup, is an intramolecular cancellation error inherent to PME. This error stems from the excluded-nonbonded correction term contained in the reciprocal-space component. This cancellation error varies in time and introduces artificial frequencies to the governing dynamics motion. Unfortunately, we find that this numerical PME error cannot be easily eliminated by refining the PME parameters (grid resolution and/or order of interpolating polynomial). We suggest that methods other than PME for fast electrostatics may allow users to reap the full advantages from MTS algorithms.

Algorithms↗

PubMed2003

Sequence-dependent solution structure and motions of 13 TATA/TBP (TATA-box binding protein) complexes.

The TATA element is a well-known example of a DNA promoter sequence recognized by the TATA box binding protein (TBP) through its intrinsic motion and deformability. Although TBP recognizes the TATA element octamer unusually (through the minor groove, which lacks the distinctive features of the major groove), single base-pair replacements alter transcriptional activity. Recent crystallographic experiments have suggested that TATA/TBP complexes differing by a single base pair retain substantial structural similarity despite their functional differences in activating transcription. To investigate the subtle role of sequence-dependent motion within the TATA element and certain aspects of its effect on assembly of the transcriptional complex, we examine 5-ns dynamics trajectories of 13 variant TATA/TBP complexes differing from each other by a single base pair. They include the wild-type (WT) adenovirus 2 major late promoter (AdMLP) TATA element, TATAAAAG (the octamer specifies positions -31 to -24 with respect to the transcription initiation site), and the variants A31 (i.e., AATAAAAG), T30, A29, C29, G28, T28, T27, G26, T26, C25, T25, and T24. Our simulated TATA/TBP complexes develop sequence-dependent structure and motion trends that may lead to favorable orientations for high-activity variants (with respect to binding TFIIA, TFIIB, and other transcription factors), while conversely, accelerate dissociation of low-activity TATA/TBP complexes. The motions that promote favorable geometries for preinitiation complexes include small rotations between TBP's N- and C-terminal domains, sense strand DNA backbone "slithering," and rotations in TBP's H2 and H2' helices. Low-activity variants tend to translate the H1 and H1' helices and withdraw the intercalating phenylalanines. These cumulative DNA and protein motions lead to a spatial spread of complex orientations up to 4 A; this is associated with an overall bend of the variant TATA/TBP complexes that spans 93 degrees to 110 degrees (107 degrees for the crystal reference). Taken together, our analyses imply larger differences when these local structural and bending changes are extended to longer DNA (upstream and downstream) and suggest that specific local TATA/TBP motions (e.g., shifts in TBP helices and TATA bases and backbone) play a role in modulating the formation and maintenance of the transcription initiation complex.

Base Sequence↗