Confident protein identification using the average peptide score method coupled with search-specific, ab initio thresholds

Deep proteomics identifies shared molecular pathway alterations in synapses of patients with schizophrenia and bipolar disorder and mouse model

Synaptic dysfunction is implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BP). We use quantitative mass spectrometry to carry out deep, unbiased proteomic profiling of synapses purified from the dorsolateral prefrontal cortex of 35 cases of SCZ, 35 cases of BP, and 35 controls. Compared with controls, SCZ and BP synapses show substantial and similar proteomic alterations. Network analyses reveal upregulation of proteins associated with autophagy and certain vesicle transport pathways and downregulation of proteins related to synaptic, mitochondrial, and ribosomal function in the synapses of individuals with SCZ or BP. Some of the same pathways are similarly dysregulated in the synaptic proteome of mutant mice deficient in Akap11, a recently discovered shared risk gene for SCZ and BP. Our work provides biological insights into molecular dysfunction at the synapse in SCZ and BP and serves as a resource for understanding the pathophysiology of these disorders.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

PPM1D mutations are oncogenic drivers of de novo diffuse midline glioma formation

The role of PPM1D mutations in de novo gliomagenesis has not been systematically explored. Here we analyze whole genome sequences of 170 pediatric high-grade gliomas and find that truncating mutations in PPM1D that increase the stability of its phosphatase are clonal driver events in 11% of Diffuse Midline Gliomas (DMGs) and are enriched in primary pontine tumors. Through the development of DMG mouse models, we show that PPM1D mutations potentiate gliomagenesis and that PPM1D phosphatase activity is required for in vivo oncogenesis. Finally, we apply integrative phosphoproteomic and functional genomics assays and find that oncogenic effects of PPM1D truncation converge on regulators of cell cycle, DNA damage response, and p53 pathways, revealing therapeutic vulnerabilities including MDM2 inhibition.

The conserved translation factor LepA is required for optimal synthesis of a porin family in Mycobacterium smegmatis

The recalcitrance of mycobacteria to antibiotic therapy is in part due to its ability to build proteins into a multi-layer cell wall. Proper synthesis of both cell wall constituents and associated proteins is crucial to maintaining cell integrity, and intimately tied to antibiotic susceptibility. How mycobacteria properly synthesize the membrane-associated proteome, however, remains poorly understood. Recently, we found that loss of lepA in Mycobacterium smegmatis (Msm) altered tolerance to rifampin, a drug that targets a non-ribosomal cellular process. LepA is a ribosome-associated GTPase found in bacteria, mitochondria, and chloroplasts, yet its physiological contribution to cellular processes is not clear. To uncover the determinants of LepA-mediated drug tolerance, we characterized the whole-cell proteomes and transcriptomes of a lepA deletion mutant relative to strains with lepA We find that LepA is important for the steady-state abundance of a number of membrane-associated proteins, including an outer membrane porin, MspA, which is integral to nutrient uptake and drug susceptibility. Loss of LepA leads to a decreased amount of porin in the membrane which leads to the drug tolerance phenotype of the lepA mutant. In mycobacteria, the translation factor LepA modulates mycobacterial membrane homeostasis, which in turn affects antibiotic tolerance.ImportanceThe mycobacterial cell wall is a promising target for new antibiotics due to the abundance of important membrane-associated proteins. Defining mechanisms of synthesis of the membrane proteome will be critical to uncovering and validating drug targets. We found that LepA, a universally conserved translation factor, controls the synthesis of a number of major membrane proteins in M. smegmatis LepA primarily controls synthesis of the major porin MspA. Loss of LepA results in decreased permeability through the loss of this porin, including permeability to antibiotics like rifampin and vancomycin. In mycobacteria, regulation from the ribosome is critical for the maintenance of membrane homeostasis and, importantly, antibiotic susceptibility.

A novel method to differentiate bovine and porcine gelatins in food products: nanoUPLC-ESI-Q-TOF-MS(E) based data independent acquisition technique to detect marker peptides in gelatin

We presented a novel nanoUPLC-MS(E) workflow method that has potential to identify origin of gelatin in some dairy products; yoghurt, cheese and ice cream. In this study, the method was performed in two steps. In the first step, gelatin was extracted from these products before the MS-sample preparation. In the second step, tryptic gelatin peptides were separated and analyzed with ultra-performance liquid chromatography and electrospray ionization quadrupole time-of-flight mass spectrometry (nanoUPLC-ESI-q-TOF-MS(E)). The novelty of this setup was that it functioned in a data independent acquisition mode and that alternate low and elevated collision energy was applied to acquire precursor and product ion information. This enabled accurate mass acquisition on the peptide level to identify the gelatin peptides. The marker peptides specific for porcine and bovine could be successfully detected in the gelatin added to the dairy products analyzed, revealing that the detection of marker peptides in the digested gelatin samples using nanoUPLC-ESI-q-TOF-MS(E) could be an effective method to differentiate porcine and bovine gelatin in the dairy products.

The plant cell wall-decomposing machinery underlies the functional diversity of forest fungi

Brown rot decay removes cellulose and hemicellulose from wood--residual lignin contributing up to 30% of forest soil carbon--and is derived from an ancestral white rot saprotrophy in which both lignin and cellulose are decomposed. Comparative and functional genomics of the "dry rot" fungus Serpula lacrymans, derived from forest ancestors, demonstrated that the evolution of both ectomycorrhizal biotrophy and brown rot saprotrophy were accompanied by reductions and losses in specific protein families, suggesting adaptation to an intercellular interaction with plant tissue. Transcriptome and proteome analysis also identified differences in wood decomposition in S. lacrymans relative to the brown rot Postia placenta. Furthermore, fungal nutritional mode diversification suggests that the boreal forest biome originated via genetic coevolution of above- and below-ground biota.

Computational approaches to peptide identification via tandem MS

The peptide identification problem lies at the heart of modern proteomic methodology, from which the presence of a particular protein or proteins in a sample may be inferred. The challenge is to find the most likely amino acid sequence, which corresponds to each tandem mass spectrum that has been collected, and produce some kind of score and associated statistical measure that the putative identification is correct. This approach assumes that the peptide (and parent protein) sequence in question is known and is present in the database which is to be searched, as opposed to de novo methods, which seek to identify the peptide ab initio. This chapter will provide an overview of the methods that common, popular software tools employ to search protein sequence databases to provide the non-expert reader with sufficient background to appreciate the choices they can make. This will cover the approaches used to compare experimental and theoretical spectra and some of the methods used to validate and provide higher confidence in the assignments.

Mining proteomic MS/MS data for MRM transitions

Multiple reaction monitoring (MRM) of peptides is a popular proteomics technique that employs tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential protein identification studies. Using this technique, the specificity of precursor to product transitions is exploited to determine the absolute quantity of multiple proteins in a single sample. Selection of suitable transitions is critical for the success of MRM experiments, but accurate theoretical prediction of fragmentation patterns and peptide signal intensity is currently not possible. A recently proposed solution to this problem is to combine knowledge of the preferred properties of transitions for MRM, taken from expert practitioners, with MS/MS evidence extracted from a proteomics data repository. In addition, by predicting retention time for each peptide candidate, it allows selection of several compatible transition candidates that can be monitored simultaneously, permitting MRM. In this chapter, we explain how to go about designing transitions using the web-based transition design tool, MRMaid, which leverages high quality MS/MS evidence from the Genome Annotating Proteomic Pipeline (GAPP).

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

BACKGROUND

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR).

RESULTS

405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models.

CONCLUSION

This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.

MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions

Multiple reaction monitoring (MRM) of peptides uses tandem mass spectrometry to quantify selected proteins of interest, such as those previously identified in differential studies. Using this technique, the specificity of precursor to product transitions is harnessed for quantitative analysis of multiple proteins in a single sample. The design of transitions is critical for the success of MRM experiments, but predicting signal intensity of peptides and fragmentation patterns ab initio is challenging given existing methods. The tool presented here, MRMaid (pronounced "mermaid") offers a novel alternative for rapid design of MRM transitions for the proteomics researcher. The program uses a combination of knowledge of the properties of optimal MRM transitions taken from expert practitioners and literature with MS/MS evidence derived from interrogation of a database of peptide identifications and their associated mass spectra. The tool also predicts retention time using a published model, allowing ordering of transition candidates. By exploiting available knowledge and resources to generate the most reliable transitions, this approach negates the need for theoretical prediction of fragmentation and the need to undertake prior "discovery" MS studies. MRMaid is a modular tool built around the Genome Annotating Proteomic Pipeline framework, providing a web-based solution with both descriptive and graphical visualizations of transitions. Predicted transition candidates are ranked based on a novel transition scoring system, and users may filter the results by selecting optional stringency criteria, such as omitting frequently modified residues, constraining the length of peptides, or omitting missed cleavages. Comparison with published transitions showed that MRMaid successfully predicted the peptide and product ion pairs in the majority of cases with appropriate retention time estimates. As the data content of the Genome Annotating Proteomic Pipeline repository increases, the coverage and reliability of MRMaid are set to increase further. MRMaid is freely available over the internet as an executable web-based service at www.mrmaid.info.

Determining the protein repertoire of Cryptosporidium parvum sporozoites

The genome of the intracellular parasite Cryptosporidium parvum has recently been sequenced, but protein expression data for the invasive stages of this important zoonotic gastrointestinal pathogen are limited. In this paper a comprehensive analysis of the expressed protein repertoire of an excysted oocyst/sporozoite preparation of C. parvum is presented. Three independent proteome platforms were employed which yielded more than 4800 individual protein identifications representing 1237 nonredundant proteins, corresponding to approximately 30% of the predicted proteome. Peptide data were mapped to the corresponding locations on the C. parvum genome and a publicly accessible interface for proteome data was developed for data-mining and visualisation at CryptoDB (http://cryptodb.org). These data provide a timely and valuable resource for improved annotation of the genome, verification of predicted hypothetical proteins and identification of proteins not predicted by current gene models. The data indicated the expression of proteins likely to be important to the invasion and intracellular establishment of the parasite, including surface proteins, constituents of the remnant mitochondrion and apical organelles. Comparison of the expressed proteome with existing transcriptional data indicated only a weak correlation. For approximately half the proteome there was limited functional and structural information, highlighting the limitations in the current understanding of Cryptosporidium biology.

Systematic determination of ion score cutoffs based on calculated false positive rates: application for identifying ubiquitinated proteins by tandem mass spectrometry

We report a simple approach for determining ion score cutoffs that permit the confident identification of ubiquitinated proteins by tandem mass spectrometry (MS/MS). Initial experiments involving the analysis of gel bands containing multi-Ubiquitin chains with quadrupole time-of-flight and quadrupole ion trap mass spectrometers revealed that standard ion score cutoffs used for database searching were not sufficiently stringent. We also found that false positive and false negative rates (FPR and FNR) varied significantly depending on the cutoff scores used and that appropriate cutoffs could only be determined following a systematic evaluation of false positive rates. When standard cutoff scores were used for the analysis of complex mixtures of ubiquitinated proteins, unacceptably high FPR were observed. Finally, we found that FPR for ubiquitinated proteins are affected by the size of the protein database that is searched. These observations may be applicable for the study of other post-translational modifications.

Public proteomic MS repositories and pipelines: available tools and biological applications

As proteomic MS has increased in throughput, so has the demand to catalogue the increasing number of peptides and proteins observed by the community using this technique. As in other 'omics' fields, this brings obvious scientific benefits such as sharing of results and prevention of unnecessary repetition, but also provides technical insights, such as the ability to compare proteome coverage between different laboratories, or between different proteomic platforms. Journals are also moving towards mandating that proteomics data be submitted to public repositories upon publication. In response to these demands, several web-based repositories have been established to store protein and peptide identifications derived from MS data, and a similar number of peptide identification software pipelines have emerged to deliver identifications to these repositories. This paper reviews the latest developments in public domain peptide and protein identification databases and describes the analysis pipelines that feed them. Recent applications of the tools to pertinent biological problems are examined, and through comparing and contrasting the capabilities of each system, the issues facing research users of web-based repositories are explored. Future developments and mechanisms to enhance system functionality and user-interfacing opportunities are also suggested.

Mass spectrometric genomic data mining: Novel insights into bioenergetic pathways in Chlamydomonas reinhardtii

A new high-throughput computational strategy was established that improves genomic data mining from MS experiments. The MS/MS data were analyzed by the SEQUEST search algorithm and a combination of de novo amino acid sequencing in conjunction with an error-tolerant database search tool, operating on a 256 processor computer cluster. The error-tolerant search tool, previously established as GenomicPeptideFinder (GPF), enables detection of intron-split and/or alternatively spliced peptides from MS/MS data when deduced from genomic DNA. Isolated thylakoid membranes from the eukaryotic green alga Chlamydomonas reinhardtii were separated by 1-D SDS gel electrophoresis, protein bands were excised from the gel, digested in-gel with trypsin and analyzed by coupling nano-flow LC with MS/MS. The concerted action of SEQUEST and GPF allowed identification of 2622 distinct peptides. In total 448 peptides were identified by GPF analysis alone, including 98 intron-split peptides, resulting in the identification of novel proteins, improved annotation of gene models, and evidence of alternative splicing.

Genome annotating proteomics pipelines: available tools

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

GAPP: A Fully Automated Software for the Confident Identification of Human Peptides from Tandem Mass Spectra

This paper introduces the genome annotating proteomic pipeline (GAPP), a totally automated publicly available software pipeline for the identification of peptides and proteins from human proteomic tandem mass spectrometry data. The pipeline takes as its input a series of MS/MS peak lists from a given experimental sample and produces a series of database entries corresponding to the peptides observed within the sample, along with related confidence scores. The pipeline is capable of finding any peptides expected, including those that cross intron-exon boundaries, and those due to single nucleotide polymorphisms (SNPs), alternate splicing, and post-translational modifications (PTMs). GAPP can therefore be used to re-annotate genomes, and this is supported through the inclusion of a Distributed Annotation System (DAS) server, which allows the peptides identified by the pipeline to be displayed in their genomic context within the Ensembl genome browser. GAPP is freely available via the web, at www. gapp.info.