Time for a break: happy new year!

A performance enhanced PSI-BLAST based on hybrid alignment

Abstract

Motivation: Sequence alignment is one of the most popular tools of modern biology. NCBI's PSI-BLAST utilizes iterative model building in order to better detect distant homologs with greater sensitivity than non-iterative BLAST. However, PSI-BLAST's performance is limited by the fact that it relies on deterministic alignments. Using a semi-probabilistic alignment scheme such as Hybrid alignment should allow for better informed model building and improved identification of homologous sequences, particularly remote homologs.

Results: We have built a new version of the tool in which the Smith-Waterman alignment algorithm core is replaced by the hybrid alignment algorithm. The favorable statistical properties of the hybrid algorithm allow the introduction of position-specific gap penalties in Hybrid PSI-BLAST. This improves the position-specific modeling of protein families and results in an overall improvement of performance.

Availability: Source code is freely available for download at http://bioserv.mps.ohio-state.edu/HybridPSI, implemented in C and supported on linux.

Contact: bundschuh@mps.ohio-state.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

BMC Genomics. 2010 Dec 1;11 Suppl 3:S8.

ICPD-a new peak detection algorithm for LC/MS.

Zhang J, Haskins W.

Department of Electrical Engineering, University of Texas at San Antonio, Texas, USA. michelle.zhang@utsa.edu

Abstract

BACKGROUND: The identification and quantification of proteins using label-free Liquid Chromatography/Mass Spectrometry (LC/MS) play crucial roles in biological and biomedical research. Increasing evidence has shown that biomarkers are often low abundance proteins. However, LC/MS systems are subject to considerable noise and sample variability, whose statistical characteristics are still elusive, making computational identification of low abundance proteins extremely challenging. As a result, the inability of identifying low abundance proteins in a proteomic study is the main bottleneck in protein biomarker discovery.

RESULTS: In this paper, we propose a new peak detection method called Information Combining Peak Detection (ICPD ) for high resolution LC/MS. In LC/MS, peptides elute during a certain time period and as a result, peptide isotope patterns are registered in multiple MS scans. The key feature of the new algorithm is that the observed isotope patterns registered in multiple scans are combined together for estimating the likelihood of the peptide existence. An isotope pattern matching score based on the likelihood probability is provided and utilized for peak detection.

CONCLUSIONS: The performance of the new algorithm is evaluated based on protein standards with 48 known proteins. The evaluation shows better peak detection accuracy for low abundance proteins than other LC/MS peak detection methods.

Anal Chem. 2010 Dec 22. [Epub ahead of print]

On the Accuracy and Limits of Peptide Fragmentation Spectrum Prediction.

Li S, Arnold RJ, Tang H, Radivojac P.

School of Informatics and Computing, Indiana University , Bloomington, Indiana 47408, United States.

Abstract

We estimated the reproducibility of tandem mass spectra for the widely used collision-induced dissociation (CID) of peptide ions. Using the Pearson correlation coefficient as a measure of spectral similarity, we found that the within-experiment reproducibility of fragment ion intensities is very high (about 0.85). However, across different experiments and instrument types/setups, the correlation decreases by more than 15% (to about 0.70). We further investigated the accuracy of current predictors of peptide fragmentation spectra and found that they are more accurate than the ad-hoc models generally used by search engines (e.g., SEQUEST) and, surprisingly, approaching the empirical upper limit set by the average across-experiment spectral reproducibility (especially for charge +1 and charge +2 precursor ions). These results provide evidence that, in terms of accuracy of modeling, predicted peptide fragmentation spectra provide a viable alternative to spectral libraries for peptide identification, with a higher coverage of peptides and lower storage requirements. Furthermore, using five data sets of proteome digests by two different proteases, we find that PeptideART (a data-driven machine learning approach) is generally more accurate than MassAnalyzer (an approach based on a kinetic model for peptide fragmentation) in predicting fragmentation spectra but that both models are significantly more accurate than the ad-hoc models.

PMID: 21175207 [PubMed - as supplied by publisher]


My comments: the ad-hoc model used by SEQUEST internally is well-known a simple one. Most prediction models can 
outperform it with flying color.

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a dynamic data structure for flexible molecular maintenance and informatics

Abstract

Motivation: We present the ‘Dynamic Packing Grid’ (DPG), a neighborhood data structure for maintaining and manipulating flexible molecules and assemblies, for efficient computation of binding affinities in drug design or in molecular dynamics calculations.

Results: DPG can efficiently maintain the molecular surface using only linear space and supports quasi-constant time insertion, deletion and movement (i.e. updates) of atoms or groups of atoms. DPG also supports constant time neighborhood queries from arbitrary points. Our results for maintenance of molecular surface and polarization energy computations using DPG exhibit marked improvement in time and space requirements.

Availability: http://www.cs.utexas.edu/~bajaj/cvc/software/DPG.shtml

Contact: bajaj@cs.utexas.edu

Full article

A label-free differential quantitative mass spectrometry method for the characterization and identification of protein changes during citrus fruit development

Background

Citrus is one of the most important and widely grown commodity fruit crops. In this study a label-free LC-MS/MS based shot-gun proteomics approach was taken to explore three main stages of citrus fruit development. These approaches were used to identify and evaluate changes occurring in juice sac cells in various metabolic pathways affecting citrus fruit development and quality.

Results

Protein changes in citrus juice sac cells were identified and quantified using label-free shotgun methodologies. Two alternative methods, differential mass-spectrometry (dMS) and spectral counting (SC) were used to analyze protein changes occurring during earlier and late stages of fruit development. Both methods were compared in order to develop a proteomics workflow that could be used in a non-model plant lacking a sequenced genome. In order to resolve the bioinformatics limitations of EST databases from species that lack a full sequenced genome, we established iCitrus. iCitrus is a comprehensive sequence database created by merging three major sources of sequences (HarvEST:citrus, NCBI/citrus/unigenes, NCBI/citrus/proteins) and improving the annotation of existing unigenes. iCitrus provided a useful bioinformatics tool for the high-throughput identification of citrus proteins. We have identified approximately 1500 citrus proteins expressed in fruit juice sac cells and quantified the changes of their expression during fruit development. Our results showed that both dMS and SC provided significant information on protein changes, with dMS providing a higher accuracy.

Conclusion

Our data supports the notion of the complementary use of dMS and SC for label-free comparative proteomics, broadening the identification spectrum and strengthening the identification of trends in protein expression changes during the particular processes being compared.


Full article