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Pongor L.S.,Pazmany Peter Catholic University | Pongor L.S.,Semmelweis University | Vera R.,Pazmany Peter Catholic University | Vera R.,Protein Structure and Bioinformatics Group | Ligeti B.,Pazmany Peter Catholic University
PLoS ONE | Year: 2014

Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result, species or strain level identification is often inaccurate and low abundance pathogens can sometimes be missed. We have developed Taxoner, an open source, taxon assignment pipeline that includes a fast aligner (e.g. Bowtie2) and a comprehensive DNA sequence database. We tested the program on simulated datasets as well as experimental data from Illumina, IonTorrent, and Roche 454 sequencing platforms. We found that Taxoner performs as well as, and often better than BLAST, but requires two orders of magnitude less running time meaning that it can be run on desktop or laptop computers. Taxoner is slower than the approaches that use small marker databases but is more sensitive due the comprehensive reference database. In addition, it can be easily tuned to specific applications using small tailored databases. When applied to metagenomic datasets, Taxoner can provide a functional summary of the genes mapped and can provide strain level identification. Taxoner is written in C for Linux operating systems. The code and documentation are available for research applications at http://code.google.com/p/taxoner. © 2014 Pongor et al. Source

Petric I.,University of Nova Gorica | Petric I.,Protein Structure and Bioinformatics Group | Ligeti B.,Pazmany Peter Catholic University | Gyorffy B.,Hungarian Academy of Sciences | And 2 more authors.
Protein and Peptide Letters | Year: 2014

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database. © 2014 Bentham Science Publishers. Source

Kertesz-Farkas A.,Protein Structure and Bioinformatics Group | Reiz B.,Institute of Biophysics | Vera R.,Protein Structure and Bioinformatics Group | Myers M.P.,Protein Networks Group | And 2 more authors.
Bioinformatics | Year: 2014

Motivation: Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times.Results: To address this issue, we present PTMTreeSearch, a new algorithm that uses a large database of known PTMs to identify PTMs from MS/MS data. For a given peptide sequence, PTMTreeSearch builds a computational tree wherein each path from the root to the leaves is labeled with the amino acids of a peptide sequence. Branches then represent PTMs. Various empirical tree pruning rules have been designed to decrease the search-execution time by eliminating biologically unlikely solutions. PTMTreeSearch first identifies a relatively small set of high confidence PTM types, and in a second stage, performs a more exhaustive search on this restricted set using relaxed search parameter settings. An analysis of experimental data shows that using the same criteria for false discovery, PTMTreeSearch annotates more peptides than the current state-of-the-art methods and PTM identification algorithms, and achieves this at roughly the same execution time. PTMTreeSearch is implemented as a plugable scoring function in the X!Tandem search engine. © 2013 The Author. Source

Reiz B.,Protein Structure and Bioinformatics Group | Reiz B.,Hungarian Academy of Sciences | Reiz B.,University of Szeged | Kertesz-Farkas A.,Protein Structure and Bioinformatics Group | And 4 more authors.
Bioinformatics | Year: 2013

Motivation: Identification of proteins by mass spectrometry-based proteomics requires automated interpretation of peptide tandem mass spectrometry spectra. The effectiveness of peptide identification can be greatly improved by filtering out extraneous noise peaks before the subsequent database searching steps.Results: Here we present a novel chemical rule-based filtering algorithm, termed CRF, which makes use of the predictable patterns (rules) of collision-induced peptide fragmentation. The algorithm selects peak pairs that obey the common fragmentation rules within plausible limits of mass tolerance as well as peak intensity and produces spectra that can be subsequently submitted to any search engine. CRF increases the positive predictive value and decreases the number of random matches and thus improves performance by 15-20% in terms of peptide annotation using search engines, such as X!Tandem. Importantly, the algorithm also achieves data compression rates of ∼75%. © 2013 The Author. Published by Oxford University Press. All rights reserved. Source

Popovic M.,Protein Structure and Bioinformatics Group | Zlatev V.,Protein Structure and Bioinformatics Group | Hodnik V.,University of Ljubljana | Anderluh G.,University of Ljubljana | And 4 more authors.
Biochimica et Biophysica Acta - Biomembranes | Year: 2012

Human Jagged-1, one of the ligands of Notch receptors, is a transmembrane protein composed of a large extracellular region and a 125-residue cytoplasmic tail which bears a C-terminal PDZ recognition motif. To investigate the interaction between Jagged-1 cytoplasmic tail and the inner leaflet of the plasma membrane we determined, by solution NMR, the secondary structure and dynamics of the recombinant protein corresponding to the intracellular region of Jagged-1, J1-tmic, bound to negatively charged lysophospholipid micelles. NMR showed that the PDZ binding motif is preceded by four α-helical segments and that, despite the extensive interaction between J1-tmic and the micelle, the PDZ binding motif remains highly flexible. Binding of J1-tmic to negatively charged, but not to zwitterionic vesicles, was confirmed by surface plasmon resonance. To study the PDZ binding region in more detail, we prepared a peptide corresponding to the last 24 residues of Jagged-1, J1C24, and different phosphorylated variants of it. J1C24 displays a marked helical propensity and undergoes a coil-helix transition in the presence of negatively charged, but not zwitterionic, lysophospholipid micelles. Phosphorylation at different positions drastically decreases the helical propensity of the peptides and abolishes the coil-helix transition triggered by lysophospholipid micelles. We propose that phosphorylation of residues upstream of the PDZ binding motif may shift the equilibrium from an ordered, membrane-bound, interfacial form of Jagged-1 C-terminal region to a more disordered form with an increased accessibility of the PDZ recognition motif, thus playing an indirect role in the interaction between Jagged-1 and the PDZ-containing target protein. © 2012 Elsevier B.V. All rights reserved. Source

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