QSSR Modeling of Bacillus Subtilis Lipase A Peptide Collision Cross-Sections in Ion Mobility Spectrometry: Local Descriptor Versus Global Descriptor.

To investigate the structure-dependent peptide mobility behavior in ion mobility spectrometry (IMS), quantitative structure-spectrum relationship (QSSR) is systematically modeled and predicted for the collision cross section Ω values of totally 162 single-protonated tripeptide fragments extracted from the Bacillus subtilis lipase A. Two different types of structure characterization methods, namely, local and global descriptor as well as three machine learning methods, namely, partial least squares (PLS), support vector machine (SVM) and Gaussian process (GP), are employed to parameterize and correlate the structures and Ω values of these peptide samples. In this procedure, the local descriptor is derived from the principal component analysis (PCA) of 516 physicochemical properties for 20 standard amino acids, which can be used to sequentially characterize the three amino acid residues composing a tripeptide. The global descriptor is calculated using CODESSA method, which can generate > 200 statistically significant variables to characterize the whole molecular structure of a tripeptide. The obtained QSSR models are evaluated rigorously via tenfold cross-validation and Monte Carlo cross-validation (MCCV). A comprehensive comparison is performed on the resulting statistics arising from the systematic combination of different descriptor types and machine learning methods. It is revealed that the local descriptor-based QSSR models have a better fitting ability and predictive power, but worse interpretability, than those based on the global descriptor. In addition, since the QSSR modeling using local descriptor does not consider the three-dimensional conformation of tripeptide samples, the method would be largely efficient as compared to the global descriptor.

First-Principles Collision Cross Section Measurements of Large Proteins and Protein Complexes.

Rotationally averaged collision cross section (CCS) values for a series of proteins and protein complexes ranging in size from 8.6 to 810 kDa are reported. The CCSs were obtained using a native electrospray ionization drift tube ion mobility-Orbitrap mass spectrometer specifically designed to enhance sensitivity while having high-resolution ion mobility and mass capabilities. Periodic focusing (PF)-drift tube (DT)-ion mobility (IM) provides first-principles determination of the CCS of large biomolecules that can then be used as CCS calibrants. The experimental, first-principles CCS values are compared to previously reported experimentally determined and computationally calculated CCS using projected superposition approximation (PSA), the Ion Mobility Projection Approximation Calculation Tool (IMPACT), and Collidoscope. Experimental CCS values are generally in agreement with previously reported CCSs, with values falling within ∼5.5%. In addition, an ion mobility resolution (CCS centroid divided by CCS fwhm) of ∼60 is obtained for pyruvate kinase (MW ∼ 233 kDa); however, ion mobility resolution for bovine serum albumin (MW ∼ 68 kDa) is less than ∼20, which arises from sample impurities and underscores the importance of sample quality. The high resolution afforded by the ion mobility-Orbitrap mass analyzer provides new opportunities to understand the intricate details of protein complexes such as the impact of post-translational modifications (PTMs), stoichiometry, and conformational changes induced by ligand binding.

A modified data filtering strategy for targeted characterization of polymers in complex matrixes using drift tube ion mobility-mass spectrometry: Application to analysis of procyanidins in the grape seed extracts.

BACKGROUNDS: Polymers, widely existing in food or dietary materials, have been attracting researchers, facing challenges, and needing effective strategies on targeted characterization in complex matrixes. METHODS: A modified data filtering strategy (including locating with drift time and m/z ranges, multiple mass defect filtering, validating MS information, and evaluating MS/MS spectra) was developed and applied for procyanidins in the grape seed extracts (GSE) using drift tube ion mobility-mass spectrometry. The procyanidin ions' trendlines were predicted by multi-model regression. Their collision cross-sections (CCSs) were calculated using single-field methods. RESULTS AND DISCUSSION: Totally, 769 CCSs belonging to 686 procyanidins with polymer degrees at 1-15 were characterized. The exponent regression was the most reasonable model (r2 ≥ 0.9379) to reveal the trendlines. The change tendency of CCSs with their polymer degrees, charge states, and linkage types were investigated. CONCLUSION: This study provided an innovative strategy for targeted characterization of polymers in complex matrixes.

Recent progress in food flavor analysis using gas chromatography-ion mobility spectrometry (GC-IMS).

Rapid, nondestructive, high-throughput testing and screening of volatile ingredients plays an important role in food flavor analysis. Gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It has a fast response, high sensitivity, easy operation, and low cost. In this article, a brief introduction to the working principle of GC-IMS is presented. A summary of recent studies of different food flavor analysis applications is also provided, including food classification and adulteration, the evaluation of food freshness and spoilage, off-flavor detection, monitoring the processing of food products, and evaluation of aroma changes during food storage. Finally, future directions of GC-IMS are proposed.

Urinary volatile organic compounds and faecal microbiome profiles in colorectal cancer.

AIM: Volatile organic compounds (VOCs) are potential biomarkers for diagnosing colorectal cancer (CRC). We characterized urinary VOCs from CRC patients, their spouses/cohabitors (spouses) and first-degree relatives (relatives) to determine any differences. Correlation with stool-derived microbiomes was also undertaken. METHODS: Urine from 56 CRC patients, 45 spouses and 37 relatives was assayed using liquid chromatography, field asymmetric ion mobility spectrometry (FAIMS), mass spectrometer technology. Analysis was performed using five-fold cross-validation and a random forest classifier. Faecal microbiome 16S rRNA was sequenced using Illumina MiSeq protocols and analysed using UPARSE and QIIME pipelines. VOC and microbiome profiles were also compared before and after cancer treatment. RESULTS: Urinary VOC profiles of CRC patients were indistinguishable from either spouses or relatives. When spouses and relatives were grouped together to form a larger non-cancer control group (n = 82), their VOC profiles became distinguishable from those of CRC patients (n = 56) with 69% sensitivity and specificity, area under the curve 0.72 (P < 0.001). Microbiome analysis identified > 1300 operational taxonomic units across all groups. The analysis of similarity R value was 0.067 (P < 0.001), with significantly different bacterial abundances in 82 operational taxonomic units (6.2%) by Kruskal-Wallis testing. CRC patients' VOC or stool microbiome profiles were unchanged after treatment. CONCLUSION: Although CRC patients' urinary VOC profiles cannot be differentiated from those of spouses or relatives they can be differentiated from a larger non-cancer control group. Comparison of the groups' microbiomes confirmed differences in bacterial species abundance. The current FAIMS-based assay can detect a unique, but modest, signal in CRC patients' urinary VOCs, which remains unaltered after treatment.

Urinary volatile organic compound markers and colorectal anastomotic leakage.

AIM: Inflammatory markers such as serum C-reactive protein (CRP) are used as routine markers to detect anastomotic leakage following colorectal surgery. However, CRP is characterized by a relatively low predictive value, emphasizing the need for the development of novel diagnostic approaches. Volatile organic compounds (VOCs) are gaseous metabolic products deriving from all conceivable bodily excrements and reflect (alterations in) the patient's physical status. Therefore, VOCs are increasingly considered as potential non-invasive diagnostic biomarkers. The aim of this study was to assess the diagnostic accuracy of urinary VOCs for colorectal anastomotic leakage. METHODS: In this explorative multicentre study, urinary VOC profiles of 22 patients with confirmed anastomotic leakage and 27 uneventful control patients following colorectal surgery were analysed by field asymmetric ion mobility spectrometry (FAIMS). RESULTS: Urinary VOCs of patients with anastomotic leakage could be distinguished from those of control patients with high accuracy: area under the receiver operating characteristics curve 0.91 (95% CI 0.81-1.00, P < 0.001), sensitivity 86% and specificity 93%. Serum CRP was significantly increased in patients with a confirmed anastomotic leak but with lower diagnostic accuracy compared to VOC analysis (area under the receiver operating characteristics curve 0.82, 95% CI 0.68-0.95, P < 0.001). Combining VOCs and CRP did not result in a significant improvement of the diagnostic performance compared to VOCs alone. CONCLUSION: Analysis by FAIMS allowed for discrimination between urinary VOC profiles of patients with a confirmed anastomotic leak and control patients following colorectal surgery. A superior accuracy compared to CRP and apparently high specificity was observed, underlining the potential as a non-invasive biomarker for the detection of colorectal anastomotic leakage.

Colorectal cancer and adenoma screening using urinary volatile organic compound (VOC) detection: early results from a single-centre bowel screening population (UK BCSP).

BACKGROUND: The United Kingdom (UK) bowel cancer screening programme has reduced mortality from colorectal cancer (CRC), but poor uptake with stool-based tests and lack of specificity of faecal occult blood testing (FOBT), has prompted investigation for a more suitable screening test. The aim of this study was to investigate the feasibility of a urinary volatile organic compounds (VOC)-based screening tool for CRC. METHODS: The urine from FOBT-positive patients was analysed using field asymmetric ion mobility spectrometry (FAIMS) and gas chromatography coupled with ion mobility spectrometry (GC-IMS). Data were analysed using a machine learning algorithm to calculate the test accuracy for correct classification of CRC against adenomas and other gastrointestinal pathology. RESULTS: One hundred and sixty-three patients were enrolled in the study. Test accuracy was high for differentiating CRC from control: area under the curve (AUC) 0.98 (95% CI 0.93-1) and 0.82 (95% CI 0.67-0.97) using FAIMS and GC-IMS respectively. Correct classification of CRC from adenoma was high with AUC range 0.83-0.92 (95% CI 0.43-1.0). Classification of adenoma from control was poor with AUC range 0.54-0.61 (95% CI 0.47-0.75) using both analytical modalities. CONCLUSIONS: CRC was correctly distinguished from adenomas or no bowel pathology using urinary VOC markers, within the bowel screening population. This pilot study demonstrates the potential of this method for CRC detection, with higher test uptake and superior sensitivity than FOBT. In addition, this is the first application of GC-IMS in CRC detection which has shown high test accuracy and usability.

mineXpert: Biological Mass Spectrometry Data Visualization and Mining with Full JavaScript Ability.

Biological mass spectrometry mainly comprises three fields of endeavor, namely, proteomics, metabolomics, and structural biology. In each of these specialties, the mass spectrometrist needs to access MS1 mass spectral data, although not necessarily on the same basis. For example, the bottom-up proteomics scientist will occasionally access MS1 data to perform data inspection, quality assessments, and quantitation measurements, whereas top-down proteomics, structural biology, or metabolomics scientists will actually spend most of their time mining profile-mode MS1 data. Furthermore, the advent of ion mobility-mass spectrometry imposes new manners of mass spectral data visualization. An open-source MS1-only mass data visualization software for the desktop was developed to allow scientists to visualize conventional and drift time mass data. Various mass data integrations are possible, allowing a thorough mass spectral data scrutiny. Isotopic cluster calculations are easily carried over from the chemical formula up to the display of the mass spectrum. Deconvolution of mass peaks can be achieved with a simple mouse drag. Flexible reporting of data inspection events and of mining discoveries is provided. Very large sparse data sets can be sliced into smaller chunks replicating the original data without data loss. Task automation is achieved in a JavaScript environment. This project allows users of mass spectrometry facilities to inspect and mine their MS1 mass data outside of these facilities without having to resort to the closed-source vendor software shipped with the instruments. mineXpert requires no proprietary software whatsoever once the mass spectrometry data have been converted to mzML. The reference implementation is version 5.8.2 or greater. Reference material, a detailed user manual, and video tutorials are available at http://www.msxpertsuite.org .