Jussi Salmi

Quality classification of tandem mass spectrometry data

UNLABELLED Peptide identification by tandem mass spectrometry is an important tool in proteomic research. Powerful identification programs exist, such as SEQUEST, ProICAT and Mascot, which can relate experimental spectra to the theoretical ones derived from protein databases, thus removing much of the manual input needed in the identification process. However, the time-consuming validation of the peptide identifications is still the bottleneck of many proteomic studies. One way to further streamline this process is to remove those spectra that are unlikely to provide a confident or valid peptide identification, and in this way to reduce the labour from the validation phase. RESULTS We propose a prefiltering scheme for evaluating the quality of spectra before the database search. The spectra are classified into two classes: spectra which contain valuable information for peptide identification and spectra that are not derived from peptides or contain insufficient information for interpretation. The different spectral features developed for the classification are tested on a real-life material originating from human lymphoblast samples and on a standard mixture of 9 proteins, both labelled with the ICAT-reagent. The results show that the prefiltering scheme efficiently separates the two spectra classes.

Filtering strategies for improving protein identification in high-throughput MS/MS studies.

Despite the recent advances in streamlining high‐throughput proteomic pipelines using tandem mass spectrometry (MS/MS), reliable identification of peptides and proteins on a larger scale has remained a challenging task, still involving a considerable degree of user interaction. Recently, a number of papers have proposed computational strategies both for distinguishing poor MS/MS spectra prior to database search (pre‐filtering) as well as for verifying the peptide identifications made by the search programs (post‐filtering). Both of these filtering approaches can be very beneficial to the overall protein identification pipeline, since they can remove a substantial part of the time consuming manual validation work and convert large sets of MS/MS spectra into more reliable and interpretable proteome information. The choice of the filtering method depends both on the properties of the data and on the goals of the experiment. This review discusses the different pre‐ and post‐filtering strategies available to the researchers, together with their relative merits and potential pitfalls. We also highlight some additional research topics, such as spectral denoising and statistical assessment of the identification results, which aim at further improving the coverage and accuracy of high‐throughput protein identification studies.

Hierarchical grid transformation for image warping in the analysis of two-dimensional electrophoresis gels

Two‐dimensional electrophoresis is a widely used method for separating a large number of proteins from complex protein mixtures and for revealing differential patterns of protein expressions. In the computer‐assisted proteome research, the comparison of protein separation profiles involves several heuristic steps, ranging from protein spot detection to matching of unknown spots. An important prerequisite for efficient protein spot matching is the image warping step, where the geometric relationship between the gel profiles is modeled on the basis of a given set of known corresponding spots, so‐called landmarks, and the locations of unknown spots are predicted using the optimized model. Traditionally, polynomial functions together with least squares optimization has been used, even though this approach is known to be incapable of modeling all the complex distortions inherent in electrophoretic data. To satisfy the need of more flexible gel distortion correction, a hierarchical grid transformation method with stochastic optimization is presented. The method provides an adaptive multiresolution model between the gels, and good correction performance in the practical cross‐validation tests suggests that automatic warping of gel images could be based on this approach. We believe that the proposed model also has significance in the ultimate comparison of corresponding protein spots since the matching process should benefit from the closeness of the true spot pairs.

Pripper: prediction of caspase cleavage sites from whole proteomes

BackgroundCaspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.ResultsThree different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper.ConclusionsWe have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.

Serum Proteomes Distinguish Children Developing Type 1 Diabetes in a Cohort With HLA-Conferred Susceptibility

We determined longitudinal serum proteomics profiles from children with HLA-conferred diabetes susceptibility to identify changes that could be detected before seroconversion and positivity for disease-associated autoantibodies. Comparisons were made between children who seroconverted and progressed to type 1 diabetes (progressors) and those who remained autoantibody negative, matched by age, sex, sample periodicity, and risk group. The samples represented the prediabetic period and ranged from the age of 3 months to 12 years. After immunoaffinity depletion of the most abundant serum proteins, isobaric tags for relative and absolute quantification were used for sample labeling. Quantitative proteomic profiles were then measured for 13 case-control pairs by high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Additionally, a label-free LC-MS/MS approach was used to analyze depleted sera from six case-control pairs. Importantly, differences in abundance of a set of proteins were consistently detected before the appearance of autoantibodies in the progressors. Based on top-scoring pairs analysis, classification of such progressors was observed with a high success rate. Overall, the data provide a reference of temporal changes in the serum proteome in healthy children and children progressing to type 1 diabetes, including new protein candidates, the levels of which change before clinical diagnosis.

Compid: a new software tool to integrate and compare MS/MS based protein identification results from Mascot and Paragon.

Tandem mass spectrometry-based proteomics experiments produce large amounts of raw data, and different database search engines are needed to reliably identify all the proteins from this data. Here, we present Compid, an easy-to-use software tool that can be used to integrate and compare protein identification results from two search engines, Mascot and Paragon. Additionally, Compid enables extraction of information from large Mascot result files that cannot be opened via the Web interface and calculation of general statistical information about peptide and protein identifications in a data set. To demonstrate the usefulness of this tool, we used Compid to compare Mascot and Paragon database search results for mitochondrial proteome sample of human keratinocytes. The reports generated by Compid can be exported and opened as Excel documents or as text files using configurable delimiters, allowing the analysis and further processing of Compid output with a multitude of programs. Compid is freely available and can be downloaded from http://users.utu.fi/lanatr/compid. It is released under an open source license (GPL), enabling modification of the source code. Its modular architecture allows for creation of supplementary software components e.g. to enable support for additional input formats and report categories.

PolyAlign: A Versatile LC-MS Data Alignment Tool for Landmark-Selected and -Automated Use

We present a versatile user-friendly software tool, PolyAlign, for the alignment of multiple LC-MS signal maps with the option of manual landmark setting or automated alignment. One of the spectral images is selected as a reference map, and after manually setting the landmarks, the program warps the images using either polynomial or Hermite transformation. The software provides an option for automated landmark finding. The software includes a very fast zoom-in function synchronized between the images, which facilitate detecting correspondences between the adjacent images. Such an interactive visual process enables the analyst to decide when the alignment is satisfactory and to correct known irregularities. We demonstrate that the software provides significant improvements in the alignment of LC-MALDI data, with 10–15 landmark pairs, and it is also applicable to correcting electrospray LC-MS data. The results with practical data show substantial improvement in peak alignment compared to MZmine, which was among the best analysis packages in a recent assessment. The PolyAlign software is freely available and easily accessible as an integrated component of the popular MZmine software, and also as a simpler stand-alone Perl implementation to preview data and apply landmark directed polynomial transformation.

DNA methylation and Transcriptome Changes Associated with Cisplatin Resistance in Ovarian Cancer

High-grade serous ovarian cancer is the most common ovarian cancer type. Although the combination of surgery and platinum-taxane chemotherapy provide an effective treatment, drug resistance frequently occurs leading to poor outcome. In order to clarify the molecular mechanisms of drug resistance, the DNA methylation and transcriptomic changes, associated with the development of drug resistance in high-grade serous ovarian cancer, were examined from patient derived malignant ascites cells. In parallel with large-scale transcriptome changes, cisplatin resistance was associated with loss of hypermethylation at several CpG sites primarily localized in the intergenic regions of the genome. The transcriptome and CpG methylome changes in response to cisplatin treatment of both sensitive and resistant cells were minimal, indicating the importance of post-translational mechanisms in regulating death or survival of the cells. The response of resistant cells to high concentrations of cisplatin revealed transcriptomic changes in potential key drivers of drug resistance, such as KLF4. Among the strongest changes was also induction of IL6 in resistant cells and the expression was further increased in response to cisplatin. Also, several other components of IL6 signaling were affected, further supporting previous observations on its importance in malignant transformation and development of drug resistance in ovarian cancer.

Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

Next generation sequencing of all variable loops of synthetic single framework scFv-Application in anti-HDL antibody selections.

Next generation sequencing (NGS) can be applied to monitoring antibody phage display library selection processes to follow the enrichment of each individual antibody clone. Utilising the recent development of the Illumina sequencing platform enabling sequencing up to 2×300bp, we have developed a method to deep sequence all complementarity determining regions (CDRs) in the clones obtained from a synthetic single framework antibody library. This was complemented by an in-house bioinformatics pipeline for efficient analysis of the sequencing results. The method was utilised to study antibody selections against high density lipoprotein (HDL) particles. Sequencing of the output from each selection round enabled extraction of useful information on both the total copy numbers as well as the relative enrichment rates of the clones. Ten antibody clones showing different ranking in terms of frequency were reproduced from synthetic DNA constructs and their capacity to bind HDL was verified by an immunoassay. The method thus facilitates the isolation of clones of interest, and in particular can assist retrieval of less efficiently enriched, yet interesting clones, which are unlikely to be identified by conventional, random colony picking based, screening.