pFind Studio: a computational solution for mass spectrometry-based proteomics
2020
Scientific reports2020. McClatchy, DB et al.
Scripps Res Inst, Dept Mol Med, La Jolla, CA 92037 USA.
ABSTRACT:Protein degradation is an essential mechanism for maintaining proteostasis in response to internal and external perturbations. Disruption of this process is implicated in many human diseases. We present a new technique, QUAD (Quantification of Azidohomoalanine Degradation), to analyze the global degradation rates in tissues using a non-canonical amino acid and mass spectrometry. QUAD analysis reveals that protein stability varied within tissues, but discernible trends in the data suggest that cellular environment is a major factor dictating stability. Within a tissue, different organelles and protein functions were enriched with different stability patterns. QUAD analysis demonstrated that protein stability is enhanced with age in the brain but not in the liver. Overall, QUAD allows the first global quantitation of protein stability rates in tissues, which will allow new insights and hypotheses in basic and translational research.
Use: pQuant
Journal of Proteome Research2020. Ma, YH et al.
Scripps Res Inst, Dept Chem Physiol & Mol & Cellular Neurobiol, La Jolla, CA 92037 USA.
ABSTRACT:Accumulationofaggregated amyloid beta (Abeta) in the brain is believed to impair multiple cellular pathways and playacentral role in Alzheimer's disease pathology. However, how this process is regulated remains unclear. In theory, measuring protein synthesis is the most direct way to evaluateacell's response to stimuli, but to date, there have been few reliable methods to do this. To identify the protein regulatory networkduringthe developmentofAbeta deposition in AD, we appliedanew proteomic technique to quantitatenewlysynthesizedprotein (NSP) changes in the cerebral cortex and hippocampusof2-, 5-, and 9-month-old APP/PS1 AD transgenic mice. This bio-orthogonal noncanonical amino acid tagging analysis combined PALM (pulse azidohomoalanine labeling in mammals) and HILAQ (heavy isotope labeled AHA quantitation) to revealacomprehensive datasetofNSPs prior to and postAbeta deposition, including the identificationofproteinsnot previously associated with AD, and demonstrated that the patternofdifferentially expressed NSPs is age-dependent. We also found dysregulated vesicle transportation networks including endosomal subunits, coat protein complex I (COPI), and mitochondrial respiratory chain throughout all time points and two brain regions. These results point toapathological dysregulationofvesicle transportation which occurs prior toAbetaaccumulationand the onsetofAD symptoms, which may progressively impact the entire protein network and thereby drive neurodegeneration. This study illustrates key pathway regulation responses to the developmentofAD pathogenesis by directly measuring the changes in protein synthesis and provides unique insights into the mechanisms that underlie AD.
Use: pQuant
Journal of Proteome Research2020. Ma, YH et al.
Scripps Res Inst, Dept Chem Physiol & Mol & Cellular Neurobiol, La Jolla, CA 92037 USA.
ABSTRACT:Accumulationofaggregated amyloid beta (Abeta) in the brain is believed to impair multiple cellular pathways and playacentral role in Alzheimer's disease pathology. However, how this process is regulated remains unclear. In theory, measuring protein synthesis is the most direct way to evaluateacell's response to stimuli, but to date, there have been few reliable methods to do this. To identify the protein regulatory networkduringthe developmentofAbeta deposition in AD, we appliedanew proteomic technique to quantitatenewlysynthesizedprotein (NSP) changes in the cerebral cortex and hippocampusof2-, 5-, and 9-month-old APP/PS1 AD transgenic mice. This bio-orthogonal noncanonical amino acid tagging analysis combined PALM (pulse azidohomoalanine labeling in mammals) and HILAQ (heavy isotope labeled AHA quantitation) to revealacomprehensive datasetofNSPs prior to and postAbeta deposition, including the identificationofproteinsnot previously associated with AD, and demonstrated that the patternofdifferentially expressed NSPs is age-dependent. We also found dysregulated vesicle transportation networks including endosomal subunits, coat protein complex I (COPI), and mitochondrial respiratory chain throughout all time points and two brain regions. These results point toapathological dysregulationofvesicle transportation which occurs prior toAbetaaccumulationand the onsetofAD symptoms, which may progressively impact the entire protein network and thereby drive neurodegeneration. This study illustrates key pathway regulation responses to the developmentofAD pathogenesis by directly measuring the changes in protein synthesis and provides unique insights into the mechanisms that underlie AD.
Use: pQuant
Journal of proteome research2020. Wu, Zhijie et al.
Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53705 USA; Univ Wisconsin, Carbone Canc Ctr, Madison, WI 53705 USA; Univ Wisconsin, Sch Med & Publ Hlth, Dept Cell & Regenerat Biol, Dept Chem, Madison, WI 53705 USA; Univ Wisconsin, Sch Med & Publ Hlth, Human Prote Program, Madison, WI 53705 USA
ABSTRACT:Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.
Use: pTop
Nature communications2020. Yang, Y et al.
Fudan Univ, Dept Chem, Shanghai Stomatol Hosp, Shanghai 200000, Peoples R China.
ABSTRACT:Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.
Use: pDeep
Proteomics2020. Li, K et al.
Baylor Coll Med, Lester & Sue Smith Breast Ctr, Houston, TX 77030 USA.
ABSTRACT:The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at .
Use: pDeep
Analytical chemistry2020. Liu, KY et al.
Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 USA.
ABSTRACT:The ability to predict tandem mass (MS/MS) spectra from peptide sequences can significantly enhance our understanding of the peptide fragmentation process and could improve peptide identification in proteomics. However, current approaches for predicting high-energy collisional dissociation (HCD) spectra are limited to predict the intensities of expected ion types, that is, the a/b/c/x/y/z ions and their neutral loss derivatives (referred to as backbone ions). In practice, backbone ions only account for <70% of total ion intensities in HCD spectra, indicating many intense ions are ignored by current predictors. In this paper, we present a deep learning approach that can predict the complete spectra (both backbone and nonbackbone ions) directly from peptide sequences. We made no assumptions or expectations on which kind of ions to predict but instead predicting the intensities for all possible m/z. Training this model needs no annotations of fragment ion nor any prior knowledge of the fragmentation rules. Our analyses show that the predicted 2+ and 3+ HCD spectra are highly similar to the experimental spectra, with average full-spectrum cosine similarities of 0.820 (+/- 0.088) and 0.786 (+/- 0.085), respectively, very close to the similarities between the experimental replicated spectra. In contrast, the best-performed backbone only models can only achieve an average similarity below 0.75 and 0.70 for 2+ and 3+ spectra, respectively. Furthermore, we developed a multitask learning (MTL) approach for predicting spectra of insufficient training samples, which allows our model to make accurate predictions for electron transfer dissociation (ETD) spectra and HCD spectra of less abundant charges (1+ and 4+).
Use: pDeep
IScience2020. Lou, RH et al.
ShanghaiTech Univ, iHuman Inst, Shanghai 201210, Peoples R China.
ABSTRACT:Data-independent acquisition mass spectrometry (DIA-MS) is a powerful technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein-coupled receptors, ion channels, and transporters) in mouse brain tissues with increases in protein identification of 37%-87% and peptide identification of 58%-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.
Use: pDeep