SUMMARY: Identification of the amino-acid motifs in proteins that are targeted for post-translational modifications (PTMs) is of great importance in understanding regulatory networks. Information about targeted motifs can be derived from mass spectrometry data that identify peptides containing specific PTMs such as phosphorylation, ubiquitylation and acetylation. Comparison of input data against a standardized 'background' set allows identification of over- and under-represented amino acids surrounding the modified site. Conventionally, calculation of targeted motifs assumes a random background distribution of amino acids surrounding the modified position. However, we show that probabilities of amino acids depend on (i) the type of the modification and (ii) their positions relative to the modified site. Thus, software that identifies such over- and under-represented amino acids should make appropriate adjustments for these effects. Here we present a new program, PTM-Logo, that generates representations of these amino acid preferences ('logos') based on position-specific amino-acid probability backgrounds calculated either from user-input data or curated databases. AVAILABILITY AND IMPLEMENTATION: PTM-Logo is freely available online at http://sysbio.chula.ac.th/PTMLogo/ or https://hpcwebapps.cit.nih.gov/PTMLogo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: We present AbDesigner3D, a new tool for identification of optimal immunizing peptides for antibody production using a peptide-based strategy. AbDesigner3D integrates 3D structural data from the Protein Data Bank (PDB) with UniProt data, which includes basic sequence data, post-translational modification sites, SNP occurrences and more. Other features, such as uniqueness and conservation scores, are calculated based on sequences from UniProt. The 3D visualization capabilities allow an intuitive interface, while an abundance of quantitative output simplifies the process of comparing immunogen peptides. Important quantitative features added in this tool include calculation and display of accessible surface area (ASA) and protein-protein interacting residues (PPIR). The specialized data visualization features of AbDesigner3D will greatly assist users to optimize their choice of immunizing peptides. Availability and implementation: AbDesigner3D is freely available at http://sysbio.chula.ac.th/AbDesigner3D or https://hpcwebapps.cit.nih.gov/AbDesigner3D/. Supplementary information: Supplementary data are available at Bioinformatics online.

High-throughput mass spectrometers can produce massive amounts of redundant data at an astonishing rate with many of them having poor signal-to-noise (S/N) ratio. These low S/N ratio spectra may not get interpreted using conventional spectra-to-database matching techniques. In this paper, we present an efficient algorithm, CAMS-RS (Clustering Algorithm for Mass Spectra using Restricted Space and Sampling) for clustering of raw mass spectrometry data. CAMS-RS utilizes a novel metric (called F-set) that exploits the temporal and spatial patterns to accurately assess similarity between two given spectra. The F-set similarity metric is independent of the retention time and allows clustering of mass spectrometry data from independent LC-MS/MS runs. A novel restricted search space strategy is devised to limit the comparisons of the number of spectra. An intelligent sampling method is executed on individual bins that allow merging of the results to make the final clusters. Our experiments, using experimentally generated data sets, show that the proposed algorithm is able to cluster spectra with high accuracy and is helpful in interpreting low S/N ratio spectra. The CAMS-RS algorithm is highly scalable with increasing number of spectra and our implementation allows clustering of up to a million spectra within minutes.

High-throughput mass spectrometers can produce thousands of peptide spectra from a single complex protein sample in a short amount of time. These data sets contain a substantial amount of redundancy (i.e. the same peptide is selected and identified multiple times in a single experiment) from peptides that may get selected multiple times in the liquid chromatography mass spectrometry (LC-MS/MS) experiment. The data from these mass spectrometers contain a substantial number of spectra that have low signal to noise (S/N) ratio and may not get interpreted due to poor quality. Recently, we presented a graph theoretic algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data. CAMS utilized a novel metric, called a F-set, that allows accurate identification of the spectra that are similar with much higher accuracy and sensitivity than if single peak comparisons were performed. In this paper we present a multithreaded algorithm, called P-CAMS, for clustering of mass spectral data on multicore machines. The algorithm relies on intelligent matrix completion for graph construction and a load-balancing scheme for substantial speedups. We study the scalability performance of the proposed parallel algorithm on a multicore machine using synthetically generated spectra with parameters carefully chosen to mimic real-world mass spectrometry datasets. Real experimental datasets were also generated for quality assessment of the clustering results from the proposed algorithm. The results show that the proposed algorithms have scalable runtime performances and gives clustering results similar to a serial algorithm. The study also provides insight into the design of high performance algorithms for irregular problems in proteomics on many-core architectures.

High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra.