3DMolMS: prediction of tandem mass spectra from 3D molecular conformations.

MOTIVATION: Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds' 3D conformations, and thus neglected critical structural information. RESULTS: We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. AVAILABILITY AND IMPLEMENTATION: The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.

Global optimization radiometric calibration method for an infrared system with a wide dynamic range.

Considering the conventional calibration restriction of the complicated calibration procedures, narrow dynamic range, and less correlation in the calibration data, a global optimization radiometric calibration method is proposed in this paper. First, a unified database is generated by integrating different gray-level images, neutral density attenuators, integration times, and target radiations under the deduced infrared physical model. Then, the calibration coefficients are automatically learned through the relative error backward propagation network. Finally, experiments are conducted on a large-aperture ground-based infrared system to evaluate the effectiveness of the proposed method. The results indicate the proposed method can solve the problem of learning imbalance with large fluctuations of infrared radiation, ensure global measurement precision with a simpler calibration procedure, and accurately measure the internal stray radiation of the optical system.

GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching.

Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.

Longitudinal Serum Proteomics Characterization of CD19-CAR-T Cell Therapy for B-Cell Malignancies.

Chimeric antigen receptor (CAR)-modified T cells have demonstrated remarkable efficacy in treating B-cell leukemia. However, treated patients may potentially develop side effects, such as cytokine release syndrome (CRS), the mechanisms of which remain unclear. Here, we collected 43 serum samples from eight patients with B-cell acute lymphoblastic leukemia (B-ALL) before and five time points after CD19-specific CAR-T cell treatment. Using TMTpro 16-plex-based quantitative proteomics, we quantified 1151 proteins and profiled the longitudinal proteomes analysis of each patient. Seven days after therapy, we found the most dysregulated inflammatory proteins. Lipid metabolism proteins, including APOA1, decreased after therapy, reached their minimum after 7 days, and then gradually recovered. Hence, APOA1 has been selected as a potential biomarker of the CRS disease progression. Furthermore, we identified CD163 as a potential biomarker of CRS severity. These two biomarkers were successfully validated using targeted proteomics in an independent cohort. Our study provides new insights into CAR-T cell therapy-induced CRS. The biomarkers we identified may help develop targeted drugs and monitoring strategies.

Relationship between body mass index and physical fitness index in Chinese college students: Results from a cross-sectional survey.

BACKGROUND: To explore the cross-sectional relationship between the body mass index (BMI) and physical fitness index (PFI) of Chinese college students. METHODS: In total, 30 497 (male 16 197, 55.5%) Chinese college students aged 19-22 were tested for height, weight and five physical fitness indicators (50-m sprint, sit and reach, standing long jump, 1000/800-m run, pull-up/bent-leg sit-up). Stratified according to age and gender, according to BMI percentile, divided into BMI < 10th Percentile (A), 10th ≤ BMI < 25th Percentile (B), 25th ≤ BMI < 75th Percentile (C), 75th ≤ BMI < 90th Percentile (D), BMI ≥ 90th Percentile (E), a total of 5 groups, and the PFI composed of 5 indicators of physical fitness was calculated according to age and gender. The comparison of PFI between different BMI groups was carried out using the effect size, and the non-linear relationship between BMI-Z and PFI was analyzed. RESULTS: The relationship between BMI-Z and PFI of male and female college students in China showed an inverted "U"-shaped curve. With comparable BMI, PFI change was greater in males than in females. CONCLUSION: The relationship between BMI and PFI of Chinese college students is nonlinear. The physical fitness level of college students who are underweight or overweight and those with obesity is lower than that of normal-weight students.

Nr2e3 is a genetic modifier that rescues retinal degeneration and promotes homeostasis in multiple models of retinitis pigmentosa.

Recent advances in viral vector engineering, as well as an increased understanding of the cellular and molecular mechanism of retinal diseases, have led to the development of novel gene therapy approaches. Furthermore, ease of accessibility and ocular immune privilege makes the retina an ideal target for gene therapies. In this study, the nuclear hormone receptor gene Nr2e3 was evaluated for efficacy as broad-spectrum therapy to attenuate early to intermediate stages of retinal degeneration in five unique mouse models of retinitis pigmentosa (RP). RP is a group of heterogenic inherited retinal diseases associated with over 150 gene mutations, affecting over 1.5 million individuals worldwide. RP varies in age of onset, severity, and rate of progression. In addition, ~40% of RP patients cannot be genetically diagnosed, confounding the ability to develop personalized RP therapies. Remarkably, Nr2e3 administered therapy resulted in reduced retinal degeneration as observed by increase in photoreceptor cells, improved electroretinogram, and a dramatic molecular reset of key transcription factors and associated gene networks. These therapeutic effects improved retinal homeostasis in diseased tissue. Results of this study provide evidence that Nr2e3 can serve as a broad-spectrum therapy to treat multiple forms of RP.

A Meta-proteogenomic Approach to Peptide Identification Incorporating Assembly Uncertainty and Genomic Variation.

Matching metagenomic and/or metatranscriptomic data, currently often under-used, can be useful reference for metaproteomic tandem mass spectra (MS/MS) data analysis. Here we developed a software pipeline for identification of peptides and proteins from metaproteomic MS/MS data using proteins derived from matching metagenomic (and metatranscriptomic) data as the search database, based on two novel approaches Graph2Pro (published) and Var2Pep (new). Graph2Pro retains and uses uncertainties of metagenome assembly for reference-based MS/MS data analysis. Var2Pep considers the variations found in metagenomic/metatranscriptomic sequencing reads that are not retained in the assemblies (contigs). The new software pipeline provides one stop application of both tools, and it supports the use of metagenome assembly from commonly used assemblers including MegaHit and metaSPAdes. When tested on two collections of multi-omic microbiome data sets, our pipeline significantly improved the identification rate of the metaproteomic MS/MS spectra by about two folds, comparing to conventional contig- or read-based approaches (the Var2Pep alone identified 5.6% to 24.1% more unique peptides, depending on the data set). We also showed that identified variant peptides are important for functional profiling of microbiomes. All results suggested that it is important to take into consideration of the assembly uncertainties and genomic variants to facilitate metaproteomic MS/MS data interpretation.

A Fast and Memory-Efficient Spectral Library Search Algorithm Using Locality-Sensitive Hashing.

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.

Full-Spectrum Prediction of Peptides Tandem Mass Spectra using Deep Neural Network.

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+).

msCRUSH: Fast Tandem Mass Spectral Clustering Using Locality Sensitive Hashing.

Large-scale proteomics projects often generate massive and highly redundant tandem mass spectra. Spectral clustering algorithms can reduce the redundancy in these data sets and thus speed up database searching for peptide identification, a major bottleneck for proteomic data analysis. The key challenge of spectral clustering is to reduce the redundancy in the MS/MS spectra data while retaining sufficient sensitivity to identify peptides from the clustered spectra. We present the software msCRUSH, which implements a novel spectral clustering algorithm based on the locality sensitive hashing technique. When tested on a large-scale proteomic data set consisting of 23.6 million spectra (including 14.4 million spectra of charge 2+), msCRUSH runs 6.9-11.3 times faster than the state-of-the-art spectral clustering software, PRIDE Cluster, while achieving higher clustering sensitivity and comparable accuracy. Using the consensus spectra reported by msCRUSH, commonly used spectra search engines MSGF+ and Mascot can identify 3 and 1% more unique peptides, respectively, compared with the identification results from the raw MS/MS spectra at the same false discovery rate (1% FDR) of peptide level. msCRUSH is implemented in C++ and is released as open-source software.

Computational identification of micro-structural variations and their proteogenomic consequences in cancer.

Motivation: Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. Results: We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Availability and implementation: MiStrVar is available for download at https://bitbucket.org/compbio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. Contact: cenksahi@indiana.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Identification of N-terminal protein processing sites by chemical labeling mass spectrometry.

RATIONALE: Proteins undergo post-translational modifications and proteolytic processing that can affect their biological function. Processing often involves the loss of single residues. Cleavage of signal peptides from the N-terminus is commonly associated with translocation. Recent reports have suggested that other processing sites also exist. METHODS: The secreted proteins from S. aureus N315 were precipitated with trichloroacetic acid (TCA) and amidinated with S-methyl thioacetimidate (SMTA). Amidinated proteins were digested with trypsin and analyzed with a high-resolution orbitrap mass spectrometer. RESULTS: Sixteen examples of Staphylococcus aureus secretory proteins that lose an N-terminal signal peptide during their export were identified using this amidination approach. The N-termini of proteins with and without methionine were identified. Unanticipated protein cleavages due to sortase and an unknown protease were also uncovered. CONCLUSIONS: A simple N-terminal amidination based mass spectrometry approach is described that facilitates identification of the N-terminus of a mature protein and the discovery of unexpected processing sites.

Identification of ethyl pyruvate as a NLRP3 inflammasome inhibitor that preserves mitochondrial integrity.

BACKGROUND: The NLRP3 inflammasome, a cytosolic complex that mediates the maturation of IL-1ß and IL-18 as well as the release of high mobility group box 1 (HMGB1), contributes to the lethality of endotoxic shock. Ethyl pyruvate (EP) was previously shown to inhibit HMGB1 release and promote survival during endotoxemia and experimental sepsis. However, the underlying protective mechanism remains elusive. RESULT: EP dose-dependently inhibited the ATP-, nigericin-, alum-, and silica-induced caspase-1 activation and HMGB1 release in mouse macrophages. EP failed to inhibit DNA transfection- or Salmonella Typhimurium-induced caspase-1 activation and HMGB1 release. Mechanistically, EP significantly attenuated mitochondrial damage and cytoplasmic translocation of mitochondrial DNA, a known NLRP3 ligand, without influencing the potassium efflux, the lysosomal rupture or the production of mitochondrial reactive oxygen species (mtROS). CONCLUSION: Ethyl pyruvate acts as a novel NLRP3 inflammasome inhibitor that preserves the integrity of mitochondria during inflammation.

A Graph-Centric Approach for Metagenome-Guided Peptide and Protein Identification in Metaproteomics.

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro.

XLSearch: a Probabilistic Database Search Algorithm for Identifying Cross-Linked Peptides.

Chemical cross-linking combined with mass spectrometric analysis has become an important technique for probing protein three-dimensional structure and protein-protein interactions. A key step in this process is the accurate identification and validation of cross-linked peptides from tandem mass spectra. The identification of cross-linked peptides, however, presents challenges related to the expanded nature of the search space (all pairs of peptides in a sequence database) and the fact that some peptide-spectrum matches (PSMs) contain one correct and one incorrect peptide but often receive scores that are comparable to those in which both peptides are correctly identified. To address these problems and improve detection of cross-linked peptides, we propose a new database search algorithm, XLSearch, for identifying cross-linked peptides. Our approach is based on a data-driven scoring scheme that independently estimates the probability of correctly identifying each individual peptide in the cross-link given knowledge of the correct or incorrect identification of the other peptide. These conditional probabilities are subsequently used to estimate the joint posterior probability that both peptides are correctly identified. Using the data from two previous cross-link studies, we show the effectiveness of this scoring scheme, particularly in distinguishing between true identifications and those containing one incorrect peptide. We also provide evidence that XLSearch achieves more identifications than two alternative methods at the same false discovery rate (availability: https://github.com/COL-IU/XLSearch ).

Impact of Amidination on Peptide Fragmentation and Identification in Shotgun Proteomics.

Peptide amidination labeling using S-methyl thioacetimidate (SMTA) is investigated in an attempt to increase the number and types of peptides that can be detected in a bottom-up proteomics experiment. This derivatization method affects the basicity of lysine residues and is shown here to significantly impact the idiosyncracies of peptide fragmentation and peptide detectability. The unique and highly reproducible fragmentation properties of SMTA-labeled peptides, such as the strong propensity for forming b1 fragment ions, can be further exploited to modify the scoring of peptide-spectrum pairs and improve peptide identification. To this end, we have developed a supervised postprocessing algorithm to exploit these characteristics of peptides labeled by SMTA. Our experiments show that although the overall number of identifications are similar, the SMTA modification enabled the detection of 16-26% peptides not previously observed in comparable CID/HCD tandem mass spectrometry experiments without SMTA labeling.

Upregulated miR-17 Regulates Hypoxia-Mediated Human Pulmonary Artery Smooth Muscle Cell Proliferation and Apoptosis by Targeting Mitofusin 2.

BACKGROUND Pulmonary arterial hypertension (PAH) is a fatal disease characterized by impaired regulation of pulmonary artery vascular growth and remodeling. Aberrant expression of miR-17 has been shown to be involved in the pathogenesis of PAH, but its underlying molecular mechanism has not been elucidated. MATERIAL AND METHODS Mitofusin 2 (MFN2) expression was determined by qRT-PCR. The protein expression levels of MFN2, proliferating cell nuclear antigen (PCNA), and pro-apoptotic protein cleaved Caspase-3 were measured using Western blot analysis. Cell proliferation and apoptosis were assessed by CellTiter-Glo reagent and flow cytometry, respectively. Caspase-3/7 activity was measured using an Apo-ONE Homogeneous Caspase-3/7 assay kit. The regulation of miR-17 on MFN2 expression was assessed using luciferase reporter assay system. RESULTS miR-17 expression was upregulated in human pulmonary artery smooth muscle cells (hPASMCs) treated with hypoxia and lung tissues of PAH patients. Inhibition of miR-17 suppressed hypoxia-induced proliferation and promoted apoptosis in hPASMCs. miR-17 inhibited MFN2 expression by binding to its 3'-UTR. Decreased cell viability and increased apoptosis and Caspase-3 activity were observed in the anti-miR-17 + siNC group compared with the anti-miR-NC + siNC group. The expression of cleaved Caspase-3 was upregulated and the expression of PCNA was downregulated in the anti-miR-17 + siNC group. Moreover, these alterations were attenuated by knockdown of MFN2. CONCLUSIONS miR-17 regulates proliferation and apoptosis in hPASMCs through MFN2 modulation. We found that miR-17 acts as a potential regulator of proliferation and apoptosis of hPASMCs, and that it might be developed as a promising new strategy for the treatment of PAH.

Computational Methods in Mass Spectrometry-Based Proteomics.

This chapter introduces computational methods used in mass spectrometry-based proteomics, including those for addressing the critical problems such as peptide identification and protein inference, peptide and protein quantification, characterization of posttranslational modifications (PTMs), and data-independent acquisitions (DIA). The chapter concludes with emerging applications of proteomic techniques, such as metaproteomics, glycoproteomics, and proteogenomics.

[Assessment of efficacy-toxicity-syndrome correlation based on anti-inflammation of Sophorae Tonkinensis Radix et Rhizoma in excess-heat mice].

Models of throat excess-heat mice were established and different dosages of water extract of STRR were ig given to mice to observe anti-inflammatory effect and its mechanism. The activities of ALT, AST and the contents of TNF-α, T3, rT3, T4, SOD, MDA, PEG2, NO, NOS, Cr, BUN, GSH and GSH-Px in serum were tested while liver index, kidney index, spleen index and thymus index were measured. The anti-inflammatory efficacy accompanied by side effects and mechanisms of water extract of Sophorae Tonkinensis Radix et Rhizoma (STRR) in excess-heat mice were investigated to clear safety dose-dependence range and the relationship of efficacy, toxicity and syndrome. In the experiment, water extract of STRR showed a strong inhibitory effect on ear edema by croton oil in throat excess-heat mice. The activities of ALT, AST in serum and liver index were all higher than that of normal group after multiple administration. PEG2, SOD, MDA, NO, NOS, GSH and GSH-Px had obvious changes. According to the results, water extract of STRR has an anti-inflammatory effect on acute inflammation in throat excess-heat mice and it is stronger than that in normal mice. The anti-inflammatory effect of STRR is related to the reduction of inflammatory mediators release. Side effects and hepatotoxicity will be produced on clinical efficacy dosage. The mechanisms of anti-inflammation and hepatotoxicity are all in connection with oxidative damnification.