PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra.

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. AVAILABILITY: The source code of PTMTreeSearch and a demo server application can be found at http://net.icgeb.org/ptmtreesearch

Precursor mass dependent filtering of mass spectra for proteomics analysis.

Identification and elimination of noise peaks in mass spectra from large proteomics data streams simultaneously improves the accuracy of peptide identification and significantly decreases the size of the data. There are a number of peak filtering strategies that can achieve this goal. Here we present a simple algorithm wherein the number of highest intensity peaks retained for further analysis is proportional to the mass of the precursor ion. We show that this technique provides an improvement over other intensity based strategies, especially for low mass precursors.

Chemical rule-based filtering of MS/MS spectra.

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%. AVAILABILITY: The MATLAB source code and a web server are available at http://hydrax.icgeb.trieste.it/CRFilter/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Locality versus globality in bacterial signalling: can local communication stabilize bacterial communities?

BACKGROUND: Microbial consortia are a major form of life; however their stability conditions are poorly understood and are often explained in terms of species-specific defence mechanisms (secretion of extracellular matrix, antimicrobial compounds, siderophores, etc.). Here we propose a hypothesis that the primarily local nature of intercellular signalling can be a general mechanism underlying the stability of many forms of microbial communities. PRESENTATION OF THE HYPOTHESIS: We propose that a large microbial community can be pictured as a theatre of spontaneously emerging, partially overlapping, locally recruited microcommunities whose members interact primarily among themselves, via secreted (signalling) molecules or cell-cell contacts. We hypothesize that stability in an open environment relies on a predominantly local steady state of intercellular communication which ensures that i) deleterious mutants or strains can be excluded by a localized collapse, while ii) microcommunities harbouring useful traits can persist and/or spread even in the absence of specific protection mechanisms. TESTING THE HYPOTHESIS: Some elements of this model can be tested experimentally by analyzing the behaviour of synthetic consortia composed of strains having well-defined communication systems and devoid of specific defence mechanisms. Supporting evidence can be obtained by in silico simulations. IMPLICATIONS OF THE HYPOTHESIS: The hypothesis provides a framework for a systematic comparison of bacterial community behavior in open and closed environments. The model predicts that local signalling may enable multispecies communities to colonize open, structured environments. On the other hand, a confined niche or a host may be more likely to be colonized by a bacterial mono-species community, and local communication here provides a control against spontaneously arising cheaters, provided that survival depends on cooperation.