A challenging task in the study of the secretory pathway is the identification and localization of new proteins to increase our understanding of the functions of different organelles. Previous proteomic studies of the endomembrane system have been hindered by contaminating proteins, making it impossible to assign proteins to organelles. Here we have used the localization of organelle proteins by the isotope tagging technique in conjunction with isotope tags for relative and absolute quantitation and 2D liquid chromatography for the simultaneous assignment of proteins to multiple subcellular compartments. With this approach, the density gradient distributions of 689 proteins from Arabidopsis thaliana were determined, enabling confident and simultaneous localization of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar membrane, plasma membrane, or mitochondria and plastids. This parallel analysis of endomembrane components has enabled protein steady-state distributions to be determined. Consequently, genuine organelle residents have been distinguished from contaminating proteins and proteins in transit through the secretory pathway.endomembrane ͉ localization of organelle proteins by isotope tagging ͉ isotope tags for relative and absolute quantitation ͉ organelle proteomics P roteins are spatially organized according to their functions within the eukaryotic cell. Therefore, protein localization is an important step toward assigning functions to the thousands of uncharacterized proteins predicted by the genome-sequencing projects. Proteomics provides powerful tools for characterizing the protein contents of organelles. Confident protein localization, however, requires that either organelle preparations are free of contaminants or that techniques are used to discriminate between genuine organelle residents and contaminating proteins (1). Although reasonably pure preparations of some organelles, such as mitochondria, can be achieved, the components of the endomembrane system so far have proved recalcitrant to purification (2, 3). The constituent organelles of the endomembrane system have similar sizes and densities, making them difficult to separate. In addition, the proteins that reside within this system are in a constant state of flux. Endomembrane proteins traffic through the system en route to their final destination; for example, plasma membrane (PM) proteins travel although the endoplasmic reticulum (ER) and the Golgi apparatus before reaching the cell surface. Proteins within the endomembrane system also cycle between compartments; for example, ER residents continuously escape to the Golgi apparatus and are retrieved in COPI vesicles (4). Consequently, it is not sufficient merely to identify the proteins within a single organelle-enriched fraction. Instead, the steady-state distributions of proteins within the whole endomembrane system must be determined if a realistic insight into the subcellular localization of endomembrane proteins is to be achieved.Localization of organelle proteins by...

Background: iTRAQ™ technology for protein quantitation using mass spectrometry is a recent, powerful means of determining relative protein levels in up to four samples simultaneously. Although protein identification of samples generated using iTRAQ may be carried out using any current identification software, the quantitation calculations have been restricted to the ProQuant software supplied by Applied Biosciences. i-Tracker software has been developed to extract reporter ion peak ratios from non-centroided tandem MS peak lists in a format easily linked to the results of protein identification tools such as Mascot and Sequest. Such functionality is currently not provided by ProQuant, which is restricted to matching quantitative information to the peptide identifications from Applied Biosciences' Interrogator™ software.

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.

Current proteomics experiments can generate vast quantities of data very quickly, but this has not been matched by data analysis capabilities. Although there have been a number of recent reviews covering various aspects of peptide and protein identification methods using MS, comparisons of which methods are either the most appropriate for, or the most effective at, their proposed tasks are not readily available. As the need for high-throughput, automated peptide and protein identification systems increases, the creators of such pipelines need to be able to choose algorithms that are going to perform well both in terms of accuracy and computational efficiency. This article therefore provides a review of the currently available core algorithms for PMF, database searching using MS/MS, sequence tag searches and de novo sequencing. We also assess the relative performances of a number of these algorithms. As there is limited reporting of such information in the literature, we conclude that there is a need for the adoption of a system of standardised reporting on the performance of new peptide and protein identification algorithms, based upon freely available datasets. We go on to present our initial suggestions for the format and content of these datasets.

Perhaps the greatest difficulty in interpreting large sets of protein identifications derived from mass spectrometric methods is whether or not to trust the results. For such experiments, the level of confidence in each protein identification made needs to be far greater than the often used 95% significance threshold to avoid the identification of many false-positives. To provide higher confidence results, we have developed an innovative scoring strategy coupling the recently published Average Peptide Score (APS) method with pre-filtering of peptide identifications, using a simple peptide quality filter. Iterative generation of these filters in conjunction with reversed database searching is used to determine the correct levels at which the APS and peptide quality thresholds should be set to return virtually zero false-positive reports. This proceeds without the need to reference a known dataset.

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.

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

BackgroundArtificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography.MethodsThis prospective, multicenter, non-significant risk study was designed to: 1) develop machine-learned algorithms to assess the presence of CAD (defined as one or more ≥ 70% stenosis, or fractional flow reserve ≤ 0.80) and 2) test the accuracy of these algorithms prospectively in a naïve verification cohort. This report is an analysis of phase signals acquired from 606 subjects at rest just prior to angiography. From the collective phase signal data, features were extracted and paired with the known angiographic results. A development set, consisting of signals from 512 subjects, was used for machine learning to determine an algorithm that correlated with significant CAD. Verification testing of the algorithm was performed utilizing previously untested phase signals from 94 subjects.ResultsThe machine-learned algorithm had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing in the verification cohort. The negative predictive value (NPV) was 96% (95% CI: 85%-100%).ConclusionsThese initial multicenter results suggest that resting cPSTA may have comparable diagnostic utility to functional tests currently used to assess CAD without requiring cardiac stress (exercise or pharmacological) or exposure of the patient to radioactivity.