Glycosaminoglycan Analysis by Cryogenic Messenger-Tagging IR Spectroscopy Combined with IMS-MS

We combine ion mobility spectrometry with cryogenic, messenger-tagging, infrared spectroscopy and mass spectrometry to . identify different isomeric disaccharides of chondroitin sulfate (CS) and heparan sulfate (HS), which are representatives of two major subclasses of glycosaminoglycans. Our analysis shows that while CS and HS disaccharide isomers have similar drift times, they can be uniquely distinguished by their vibrational spectrum between,similar to 3200 and 3700 cm(-1) due to their different OH hydrogen-bonding patterns. We suggest that this combination of techniques is well suited to identify and characterize glycan isomers directly, which presents tremendous challenges for existing methods

Cryogenic Vibrational Spectroscopy Provides Unique Fingerprints for Glycan Identification

The structural characterization of glycans by mass spectrometry is particularly challenging. This is because of the high degree of isomerism in which glycans of the same mass can differ in their stereochemistry, attachment points, and degree of branching. Here we show that the addition of cryogenic vibrational spectroscopy to mass and mobility measurements allows one to uniquely identify and characterize these complex biopolymers. We investigate six disaccharide isomers that differ in their stereochemistry, attachment point of the glycosidic bond, and monosaccharide content, and demonstrate that we can identify each one unambiguously. Even disaccharides that differ by a single stereogenic center or in the monosaccharide sequence order show distinct vibrational fingerprints that would clearly allow their identification in a mixture, which is not possible by ion mobility spectrometry/mass spectrometry alone. Moreover, this technique can be applied to larger glycans, which we demonstrate by distinguishing isomeric branched and linear pentasaccharides. The creation of a database containing mass, collision cross section, and vibrational fingerprint measurements for glycan standards should allow unambiguous identification and characterization of these biopolymers in mixtures, providing an enabling technology for all fields of glycoscience

Cryogenic IR spectroscopy combined with ion mobility spectrometry for the analysis of human milk oligosaccharides

We report here our combination of cryogenic, messenger-tagging, infrared (IR) spectroscopy with ion mobility spectrometry (IMS) and mass spectrometry (MS) as a way to identify and analyze a set of human milk oligosaccharides (HMOs) ranging from trisaccharides to hexasaccharides. The added dimension of IR spectroscopy provides a diagnostic fingerprint in the OH and NH stretching region, which is crucial to identify these oligosaccharides, which are difficult to distinguish by IMS alone. These results extend our previous work in demonstrating the generality of this combined approach for distinguishing subtly different structural and regioisomers of glycans of biologically relevant size

High-throughput bacterial SNP typing identifies distinct clusters of Salmonella Typhi causing typhoid in Nepalese children.

BACKGROUND: Salmonella Typhi (S. Typhi) causes typhoid fever, which remains an important public health issue in many developing countries. Kathmandu, the capital of Nepal, is an area of high incidence and the pediatric population appears to be at high risk of exposure and infection. METHODS: We recently defined the population structure of S. Typhi, using new sequencing technologies to identify nearly 2,000 single nucleotide polymorphisms (SNPs) that can be used as unequivocal phylogenetic markers. Here we have used the GoldenGate (Illumina) platform to simultaneously type 1,500 of these SNPs in 62 S. Typhi isolates causing severe typhoid in children admitted to Patan Hospital in Kathmandu. RESULTS: Eight distinct S. Typhi haplotypes were identified during the 20-month study period, with 68% of isolates belonging to a subclone of the previously defined H58 S. Typhi. This subclone was closely associated with resistance to nalidixic acid, with all isolates from this group demonstrating a resistant phenotype and harbouring the same resistance-associated SNP in GyrA (Phe83). A secondary clone, comprising 19% of isolates, was observed only during the second half of the study. CONCLUSIONS: Our data demonstrate the utility of SNP typing for monitoring bacterial populations over a defined period in a single endemic setting. We provide evidence for genotype introduction and define a nalidixic acid resistant subclone of S. Typhi, which appears to be the dominant cause of severe pediatric typhoid in Kathmandu during the study period

A method for characterizing polysaccharides

A method for a structural characterization of polysaccharides, comprising steps of characterizing a polysaccharide by physical measurements comprising a determination of a mass of the polysaccharide by means of mass spectrometry, a further determination of a rotationally averaged cross section of the polysaccharide by means of ion mobility spectrometry, and an infrared spectrum of the polysaccharide by means of cryogenic, messenger-tagging IR spectroscopy

Position of Proline Mediates the Reactivity of S‑Palmitoylation

Palmitoylation, a post-translational modification in which a saturated 16-carbon chain is added predominantly to a cysteine residue, participates in various biological functions. The position of proline relative to other residues being post-translationally modified has been previously reported as being important. We determined that proline is statistically enriched around cysteines known to be S-palmitoylated. The goal of this work was to determine how the position of proline influences the palmitoylation of the cysteine residue. We established a mass spectrometry-based approach to investigate time- and temperature-dependent kinetics of autopalmitoylation <i>in vitro</i> and to derive the thermodynamic parameters of the transition state associated with palmitoylation; to the best of our knowledge, our work is the first to study the kinetics and activation properties of the palmitoylation process. We then used these thermochemical parameters to determine if the position of proline relative to the modified cysteine is important for palmitoylation. Our results show that peptides with proline at the −1 position of cysteine in their sequence (PC) have lower enthalpic barriers and higher entropic barriers in comparison to the same peptides with proline at the +1 position of cysteine (CP); interestingly, the free-energy barriers for both pairs are almost identical. Molecular dynamics studies demonstrate that the flexibility of the cysteine backbone in the PC-containing peptide when compared to the CP-containing peptide explains the increased entropic barrier and decreased enthalpic barrier observed experimentally

Development of in silico models to predict viscosity and mouse clearance using a comprehensive analytical data set collected on 83 scaffold-consistent monoclonal antibodies

ABSTRACTBiologic drug discovery pipelines are designed to deliver protein therapeutics that have exquisite functional potency and selectivity while also manifesting biophysical characteristics suitable for manufacturing, storage, and convenient administration to patients. The ability to use computational methods to predict biophysical properties from protein sequence, potentially in combination with high throughput assays, could decrease timelines and increase the success rates for therapeutic developability engineering by eliminating lengthy and expensive cycles of recombinant protein production and testing. To support development of high-quality predictive models for antibody developability, we designed a sequence-diverse panel of 83 effector functionless IgG1 antibodies displaying a range of biophysical properties, produced and formulated each protein under standard platform conditions, and collected a comprehensive package of analytical data, including in vitro assays and in vivo mouse pharmacokinetics. We used this robust training data set to build machine learning classifier models that can predict complex protein behavior from these data and features derived from predicted and/or experimental structures. Our models predict with 87% accuracy whether viscosity at 150 mg/mL is above or below a threshold of 15 centipoise (cP) and with 75% accuracy whether the area under the plasma drug concentration–time curve (AUC0–672 h) in normal mouse is above or below a threshold of 3.9 × 106 h x ng/mL