Identification of substrates for human deubiquitinating enzymes (DUBs): An up-to-date review and a case study for neurodevelopmental disorders.

Similar to the reversal of kinase-mediated protein phosphorylation by phosphatases, deubiquitinating enzymes (DUBs) oppose the action of E3 ubiquitin ligases and reverse the ubiquitination of proteins. A total of 99 human DUBs, classified in 7 families, allow in this way for a precise control of cellular function and homeostasis. Ubiquitination regulates a myriad of cellular processes, and is altered in many pathological conditions.

A Proteomic Approach for Systematic Mapping of Substrates of Human Deubiquitinating Enzymes.

The human genome contains nearly 100 deubiquitinating enzymes (DUBs) responsible for removing ubiquitin moieties from a large variety of substrates. Which DUBs are responsible for targeting which substrates remain mostly unknown. Here we implement the Ub approach to identify DUB substrates in a systematic manner, combining gene silencing and proteomics analyses.

Using a Simple Cellular Assay to Map NES Motifs in Cancer-Related Proteins, Gain Insight into CRM1-Mediated NES Export, and Search for NES-Harboring Micropeptides.

The nuclear export receptor CRM1 (XPO1) recognizes and binds specific sequence motifs termed nuclear export signals (NESs) in cargo proteins. About 200 NES motifs have been identified, but over a thousand human proteins are potential CRM1 cargos, and most of their NESs remain to be identified. On the other hand, the interaction of NES peptides with the "NES-binding groove" of CRM1 was studied in detail using structural and biochemical analyses, but a better understanding of CRM1 function requires further investigation of how the results from these in vitro studies translate into actual NES export in a cellular context.

Protein Probability Model for High-Throughput Protein Identification by Mass Spectrometry-Based Proteomics.

Shotgun proteomics is the method of choice for high-throughput protein identification; however, robust statistical methods are essential to automatize this task while minimizing the number of false identifications. The standard method for estimating the false discovery rate (FDR) of individual identifications and keeping it below a threshold (typically 1%) is the target-decoy approach. However, numerous works have shown that FDR at the protein level may become much larger than FDR at the peptide level.

Calculation of False Discovery Rate for Peptide and Protein Identification.

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications.

Comparing Peptide Spectra Matches Across Search Engines.

Mass spectrometry is extremely efficient for sequencing small peptides generated by, for example, a trypsin digestion of a complex mixture. Current instruments have the capacity to generate 50-100 K MSMS spectra from a single run. Of these ~30-50% is typically assigned to peptide matches on a 1% FDR threshold.

Altered Nuclear Export Signal Recognition as a Driver of Oncogenesis.

Altered expression of XPO1, the main nuclear export receptor in eukaryotic cells, has been observed in cancer, and XPO1 has been a focus of anticancer drug development. However, mechanistic evidence for cancer-specific alterations in XPO1 function is lacking. Here, genomic analysis of 42,793 cancers identified recurrent and previously unrecognized mutational hotspots in XPO1 mutations exhibited striking lineage specificity, with enrichment in a variety of B-cell malignancies, and introduction of single amino acid substitutions in XPO1 initiated clonal, B-cell malignancy .

PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets.

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly.

Enhanced Missing Proteins Detection in NCI60 Cell Lines Using an Integrative Search Engine Approach.

The Human Proteome Project (HPP) aims deciphering the complete map of the human proteome. In the past few years, significant efforts of the HPP teams have been dedicated to the experimental detection of the missing proteins, which lack reliable mass spectrometry evidence of their existence. In this endeavor, an in depth analysis of shotgun experiments might represent a valuable resource to select a biological matrix in design validation experiments.

Proteome-wide search for functional motifs altered in tumors: Prediction of nuclear export signals inactivated by cancer-related mutations.

Large-scale sequencing projects are uncovering a growing number of missense mutations in human tumors. Understanding the phenotypic consequences of these alterations represents a formidable challenge. In silico prediction of functionally relevant amino acid motifs disrupted by cancer mutations could provide insight into the potential impact of a mutation, and guide functional tests.

Proteogenomics Dashboard for the Human Proteome Project.

dasHPPboard is a novel proteomics-based dashboard that collects and reports the experiments produced by the Spanish Human Proteome Project consortium (SpHPP) and aims to help HPP to map the entire human proteome. We have followed the strategy of analog genomics projects like the Encyclopedia of DNA Elements (ENCODE), which provides a vast amount of data on human cell lines experiments. The dashboard includes results of shotgun and selected reaction monitoring proteomics experiments, post-translational modifications information, as well as proteogenomics studies.

A standardized framing for reporting protein identifications in mzIdentML 1.2.

Inferring which protein species have been detected in bottom-up proteomics experiments has been a challenging problem for which solutions have been maturing over the past decade. While many inference approaches now function well in isolation, comparing and reconciling the results generated across different tools remains difficult. It presently stands as one of the greatest barriers in collaborative efforts such as the Human Proteome Project and public repositories such as the PRoteomics IDEntifications (PRIDE) database.

Prediction of nuclear export signals using weighted regular expressions (Wregex).

Motivation: Leucine-rich nuclear export signals (NESs) are short amino acid motifs that mediate binding of cargo proteins to the nuclear export receptor CRM1, and thus contribute to regulate the localization and function of many cellular proteins. Computational prediction of NES motifs is of great interest, but remains a significant challenge.

Results: We have developed a novel approach for amino acid motif searching that can be used for NES prediction.

Surfing transcriptomic landscapes. A step beyond the annotation of chromosome 16 proteome.

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.

PAnalyzer: a software tool for protein inference in shotgun proteomics.

Background: Protein inference from peptide identifications in shotgun proteomics must deal with ambiguities that arise due to the presence of peptides shared between different proteins, which is common in higher eukaryotes. Recently data independent acquisition (DIA) approaches have emerged as an alternative to the traditional data dependent acquisition (DDA) in shotgun proteomics experiments. MSE is the term used to name one of the DIA approaches used in QTOF instruments.

SIR: Deterministic protein inference from peptides assigned to MS data.

Currently the bottom up approach is the most popular for characterizing protein samples by mass spectrometry. This is mainly attributed to the fact that the bottom up approach has been successfully optimized for high throughput studies. However, the bottom up approach is associated with a number of challenges such as loss of linkage information between peptides.