A HUPO test sample study reveals common problems in mass spectrometry-based proteomics.

We performed a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in liquid chromatography-mass spectrometry-based proteomics. We distributed an equimolar test sample, comprising 20 highly purified recombinant human proteins, to 27 laboratories. Each protein contained one or more unique tryptic peptides of 1,250 Da to test for ion selection and sampling in the mass spectrometer. Of the 27 labs, members of only 7 labs initially reported all 20 proteins correctly, and members of only 1 lab reported all tryptic peptides of 1,250 Da. Centralized analysis of the raw data, however, revealed that all 20 proteins and most of the 1,250 Da peptides had been detected in all 27 labs. Our centralized analysis determined missed identifications (false negatives), environmental contamination, database matching and curation of protein identifications as sources of problems. Improved search engines and databases are needed for mass spectrometry-based proteomics.

A Bayesian approach to peptide identification using accurate mass and time tags from LC-FTICR-MS proteomics experiments.

In high-throughput proteomics, one promising approach presently being explored is the Accurate Mass and Time (AMT) tag approach, in which reversed-phase liquid chromatography coupled to high accuracy mass spectrometry provide measurements of both the masses and chromatographic retention times of tryptic peptides in complex mixtures. These measurements are matched to the mass and predicted retention times of peptides in library. There are two varieties of peptides in the library: peptides whose retention time predictions are derived from previous peptide identifications and therefore are of high precision, and peptides whose retention time predictions are derived from a sequence-based model and therefore have lower precision. We present a Bayesian statistical model that provides probability estimates for the correctness of each match by separately modeling the data distributions of correct matches and incorrect matches. For matches to peptides with high-precision retention time predictions, the model distinguishes correct matches from incorrect matches with high confidence. For matches to peptides having low-precision retention time predictions, match probabilities do not approach certainty; however, even moderate probability matches may provide biologically interesting findings, motivating further investigations.

Quantification of uncertainty of peptide retention time predictions from a sequence-based model in LC-MS/MS proteomics experiments.

In high-throughput mass spectrometry-based proteomics, it is necessary to employ separations to reduce sample complexity prior to mass spectrometric peptide identification. Interest has begun to focus on using information from separations to aid in peptide identification. One of the most common separations is reversed-phase liquid chromatography, in which peptides are separated on the basis of their chromatographic retention time. We apply a sequence-based model of peptide hydrophobicity to the problem of predicting peptide retention times, first fitting the model parameters using a large set of peptide identifications and then testing its predictions using a set of completely different peptide identifications. We demonstrate that not only does the model provide reasonably accurate predictions, it also provides a quantification of the uncertainty of its predictions. The model may therefore be used to provide checks on future tentative peptide identifications, even when the peptide species in question has never been observed before.

The protein microscope: incorporating mass spectrometry into cell biology.

Mass spectrometry has come into its own as an extremely powerful tool for the study of whole proteomes. So why are not more cell biologists embracing it with open arms?

Organellar proteomics to create the cell map.

The elucidation of a complete, accurate, and permanent representation of the proteome of the mammalian cell may be achievable piecemeal by an organellar based approach. The small volume of organelles assures high protein concentrations. Providing isolated organelles are homogenous, this assures reliable protein characterization within the sensitivity and dynamic range limits of current mass spec based analysis. The stochastic aspect of peptide selection by tandem mass spectrometry for sequence determination by fragmentation is dealt with by multiple biological replicates as well as by prior protein separation on 1-D gels. Applications of this methodology to isolated synaptic vesicles, clathrin coated vesicles, endosomes, phagosomes, endoplasmic reticulum, and Golgi apparatus, as well as Golgi-derived COPI vesicles, have led to mechanistic insight into the identity and function of these organelles.

Methods for peptide identification by spectral comparison.

BACKGROUND: Tandem mass spectrometry followed by database search is currently the predominant technology for peptide sequencing in shotgun proteomics experiments. Most methods compare experimentally observed spectra to the theoretical spectra predicted from the sequences in protein databases. There is a growing interest, however, in comparing unknown experimental spectra to a library of previously identified spectra. This approach has the advantage of taking into account instrument-dependent factors and peptide-specific differences in fragmentation probabilities. It is also computationally more efficient for high-throughput proteomics studies. RESULTS: This paper investigates computational issues related to this spectral comparison approach. Different methods have been empirically evaluated over several large sets of spectra. First, we illustrate that the peak intensities follow a Poisson distribution. This implies that applying a square root transform will optimally stabilize the peak intensity variance. Our results show that the square root did indeed outperform other transforms, resulting in improved accuracy of spectral matching. Second, different measures of spectral similarity were compared, and the results illustrated that the correlation coefficient was most robust. Finally, we examine how to assemble multiple spectra associated with the same peptide to generate a synthetic reference spectrum. Ensemble averaging is shown to provide the best combination of accuracy and efficiency. CONCLUSION: Our results demonstrate that when combined, these methods can boost the sensitivity and specificity of spectral comparison. Therefore they are capable of enhancing and complementing existing tools for consistent and accurate peptide identification.

Quantitative proteomics analysis of the secretory pathway.

We report more than 1400 proteins of the secretory-pathway proteome and provide spatial information on the relative presence of each protein in the rough and smooth ER Golgi cisternae and Golgi-derived COPI vesicles. The data support a role for COPI vesicles in recycling and cisternal maturation, showing that Golgi-resident proteins are present at a higher concentration than secretory cargo. Of the 1400 proteins, 345 were identified as previously uncharacterized. Of these, 230 had their subcellular location deduced by proteomics. This study provides a comprehensive catalog of the ER and Golgi proteomes with insight into their identity and function.

Characterization of an RNA granule from developing brain.

In brain, mRNAs are transported from the cell body to the processes, allowing for local protein translation at sites distant from the nucleus. Using subcellular fractionation, we isolated a fraction from rat embryonic day 18 brains enriched for structures that resemble amorphous collections of ribosomes. This fraction was enriched for the mRNA encoding beta-actin, an mRNA that is transported in dendrites and axons of developing neurons. Abundant protein components of this fraction, determined by tandem mass spectrometry, include ribosomal proteins, RNA-binding proteins, microtubule-associated proteins (including the motor protein dynein), and several proteins described only as potential open reading frames. The conjunction of RNA-binding proteins, transported mRNA, ribosomal machinery, and transporting motor proteins defines these structures as RNA granules. Expression of a subset of the identified proteins in cultured hippocampal neurons confirmed that proteins identified in the proteomics were present in neurites associated with ribosomes and mRNAs. Moreover many of the expressed proteins co-localized together. Time lapse video microscopy indicated that complexes containing one of these proteins, the DEAD box 3 helicase, migrated in dendrites of hippocampal neurons at the same speed as that reported for RNA granules. Although the speed of the granules was unchanged by activity or the neurotrophin brain-derived neurotrophic factor, brain-derived neurotrophic factor, but not activity, increased the proportion of moving granules. These studies define the isolation and composition of RNA granules expressed in developing brain.

Multicomponent internal recalibration of an LC-FTICR-MS analysis employing a partially characterized complex peptide mixture: systematic and random errors.

In high-throughput proteomics, a promising current approach is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) of tryptic peptides from complex mixtures of proteins. To apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error expected in the measured masses. Here, we analyze by LC-FTICR-MS a complex mixture of peptides derived from a sample previously characterized by LC-QTOF-MS. Application of a Bayesian probability model of the data and partial knowledge of the composition of the sample suffice to estimate both the systematic and random errors in measured masses.

Increasing peptide identification in tandem mass spectrometry through automatic function switching optimization.

Comprehensive proteomic studies that employ MS directed peptide sequencing are limited by optimal peptide separation and MS and tandem MS data acquisition routines. To identify the optimal parameters for data acquisition, we developed a system that models the automatic function switching behavior of a mass spectrometer using an MS-only dataset. Simulations were conducted to characterize the number and the quality of simulated fragmentation as a function of the data acquisition routines and used to construct operating curves defining tandem mass spectra quality and the number of peptides fragmented. Results demonstrated that one could optimize for quality or quantity, with the number of peptides fragmented decreasing as quality increased. The predicted optimal operating curve indicated that significant improvements can be realized by selecting the appropriate data acquisition parameters. The simulation results were confirmed experimentally by testing 10 LC MS/MS data acquisition parameter sets on an LC-Q-TOF-MS. Database matching of the experimental fragmentation returned peptide scores consistent with the predictions of the model. The results of the simulations of mass spectrometer data acquisition routines reveal an inverse relationship between the quality and the quantity of peptide identifications and predict an optimal operating curve that can be used to select an optimal data acquisition parameter for a given (or any) sample.

Tandem MS analysis of brain clathrin-coated vesicles reveals their critical involvement in synaptic vesicle recycling.

Tandem MS has identified 209 proteins of clathrin-coated vesicles (CCVs) isolated from rat brain. An overwhelming abundance of peptides were assigned to the clathrin coat with a 1:1 stoichiometry observed for clathrin heavy and light chains and a 2:1 stoichiometry of clathrin heavy chain with clathrin adaptor protein heterotetramers. Thirty-two proteins representing many of the known components of synaptic vesicles (SVs) were identified, supporting that a main function for brain CCVs is to recapture SVs after exocytosis. A ratio of vesicle-N-ethylmaleimide-sensitive factor attachment protein receptors to target-N-ethylmaleimide-sensitive factor attachment protein receptors, similar to that previously detected on SVs, supports a single-step model for SV sorting during CCV-mediated recycling of SVs. The uncovering of eight previously undescribed proteins, four of which have to date been linked to clathrin-mediated trafficking, further attests to the value of the current organelle-based proteomics strategy.

High-resolution crystal structure of Arthrobacter aurescens chondroitin AC lyase: an enzyme-substrate complex defines the catalytic mechanism.

Chondroitin lyases (EC 4.2.2.4 and EC 4.2.2.5) are glycosaminoglycan-degrading enzymes that act as eliminases. Chondroitin lyase AC from Arthrobacter aurescens (ArthroAC) is known to act on chondroitin 4-sulfate and chondroitin 6-sulfate but not on dermatan sulfate. Like other chondroitin AC lyases, it is capable of cleaving hyaluronan. We have determined the three-dimensional crystal structure of ArthroAC in its native form as well as in complex with its substrates (chondroitin 4-sulfate tetrasaccharide, CS(tetra) and hyaluronan tetrasaccharide) at resolution varying from 1.25 A to 1.9A. The primary sequence of ArthroAC has not been previously determined but it was possible to determine the amino acid sequence of this enzyme from the high-resolution electron density maps and to confirm it by mass spectrometry. The enzyme-substrate complexes were obtained by soaking the substrate into the crystals for varying lengths of time (30 seconds to ten hours) and flash-cooling the crystals. The electron density map for crystals soaked in the substrate for as short as 30 seconds showed the substrate clearly and indicated that the ring of central glucuronic acid assumes a distorted boat conformation. This structure strongly supports the lytic mechanism where Tyr242 acts as a general base that abstracts the proton from the C5 position of glucuronic acid while Asn183 and His233 neutralize the charge on the glucuronate acidic group. Comparison of this structure with that of chondroitinase AC from Flavobacterium heparinum (FlavoAC) provides an explanation for the exolytic and endolytic mode of action of ArthroAC and FlavoAC, respectively.

TUC-4b, a novel TUC family variant, regulates neurite outgrowth and associates with vesicles in the growth cone.

The TUC (TOAD-64/Ulip/CRMP) proteins are homologs of UNC-33, a protein that is required for axon extension and guidance in Caenorhabditis elegans. The TUC proteins are expressed in newly born neurons in the developing nervous system and have been implicated in semaphorin signaling and neuronal polarity. Here, we identify several new variants of the TUC family, each of which is expressed during distinct periods of neural development. We cloned and characterized TUC-4b, a variant of TUC-4a that includes a unique N-terminal extension. The functional relevance of this N-terminal domain is demonstrated by the finding that overexpression of TUC-4b, but not TUC-4a, results in increased neurite length and branching. Furthermore, whereas TUC-4a is expressed throughout life, TUC-4b is expressed exclusively during embryonic development. TUC-4b is localized to SV2 (synaptic vesicle protein 2)-positive vesicles in the central domain of the growth cone, suggesting a potential role in growth cone vesicle transport. Furthermore, TUC-4b interacts with the SH3A (Src homology 3A) domain of intersectin, an endocytic-exocytic adaptor protein. Together, these data suggest that TUC-4b can regulate neurite extension and branching through a mechanism that may involve membrane transport in the growth cone.

Enthoprotin: a novel clathrin-associated protein identified through subcellular proteomics.

Despite numerous advances in the identification of the molecular machinery for clathrin-mediated budding at the plasma membrane, the mechanistic details of this process remain incomplete. Moreover, relatively little is known regarding the regulation of clathrin-mediated budding at other membrane systems. To address these issues, we have utilized the powerful new approach of subcellular proteomics to identify novel proteins present on highly enriched clathrin-coated vesicles (CCVs). Among the ten novel proteins identified is the rat homologue of a predicted gene product from human, mouse, and Drosophila genomics projects, which we named enthoprotin. Enthoprotin is highly enriched on CCVs isolated from rat brain and liver extracts. In cells, enthoprotin demonstrates a punctate staining pattern that is concentrated in a perinuclear compartment where it colocalizes with clathrin and the clathrin adaptor protein (AP)1. Enthoprotin interacts with the clathrin adaptors AP1 and with Golgi-localized, gamma-ear-containing, Arf-binding protein 2. Through its COOH-terminal domain, enthoprotin binds to the terminal domain of the clathrin heavy chain and stimulates clathrin assembly. These data suggest a role for enthoprotin in clathrin-mediated budding on internal membranes. Our study reveals the utility of proteomics in the identification of novel vesicle trafficking proteins.