Peptide Mapping

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.

BackgroundA goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semi-relative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.ResultsTo address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chen et al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies.ConclusionPatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available at http://pcarvalho.com/patternlab.

Peptide mapping is a component of the analytical toolbox used within the biopharmaceutical industry to aid in the identity confirmation of a protein therapeutic and to monitor degradative events such as oxidation or deamidation. These methods offer the advantage of providing site-specific information regarding post-translational and chemical modifications that may arise during production, processing or storage. A number of such variations may also be induced by the sample preparation methods themselves which may confound the ability to accurately evaluate the true modification levels. One important focus when developing a peptide mapping method should therefore be the use of sample preparation conditions that will minimize the degree of artificial modifications induced. Unfortunately, the conditions that are amenable to effective reduction, alkylation and digestion are often the same conditions that promote unwanted modifications. Here we describe the optimization of a tryptic digestion protocol used for peptide mapping of the NISTmAb IgG1κ which addresses the challenge of balancing maximum digestion efficiency with minimum artificial modifications. The parameters on which we focused include buffer concentration, digestion time and temperature, as well as the source and type of trypsin (recombinant vs. pancreatic; bovine vs porcine) used. Using the optimized protocol we generated a peptide map of the NISTmAb which allowed us to confirm its identity at the level of primary structure. Graphical abstract Peptide map of the NISTmAb RM 8671 monoclonal antibody. Tryptic digestion was performed using an optimized protocol and followed by LC-UV-MS analysis. The trace represents the total ion chromatogram. Each peak was mapped to peptides identified using mass spectrometry data.

Collagen is the most ubiquitous protein in the animal kingdom and one of the most abundant proteins on Earth. Despite having a relatively repetitive amino acid sequence motif that enables its triple helical structure, in type 1 collagen, that dominates skin and bone, there is enough variation for its increasing use for the biomolecular species identification of animal tissues processed or degraded beyond the amenability of DNA-based analyses. In recent years, this has been most commonly achieved through the technique of collagen peptide mass fingerprinting (PMF) known as ZooMS (Zooarchaeology by Mass Spectrometry), applied to the analysis of tens of thousands of samples across over one hundred studies in the past decade alone. However, a robust means to quantify variation between these fingerprints remains elusive, despite being increasingly required due to the shift towards a wider range of wild fauna and those that are more distantly related from currently known sequences. This is particularly problematic in fish due to their greater sequence variation. Here we evaluate the quantification of the relative closeness of collagen fingerprints between families using ANOSIM and a modified SIMPER analysis, incorporating relative peak intensity. Our results show a clear correlation between sequence differentiation and statistical distance of PMFs, indicating that the additional complexity of type 1 collagen in fish could directly affect the efficacy of biomolecular techniques such as ZooMS. Furthermore, this multivariate statistical analysis demonstrates that PMFs in fish are substantively more distinct than those of mammalian or amphibian taxa.