De Novo Peptide Sequencing: Deep Mining of High-Resolution Mass Spectrometry Data

A Bioinformatics Approach to Mine the Microbial Proteomic Profile of COVID-19 Mass Spectrometry Data

Mass spectrometry (MS) is one of the key technologies used in proteomics. The majority of studies carried out using proteomics have focused on identifying proteins in biological samples such as human plasma to pin down prognostic or diagnostic biomarkers associated with particular conditions or diseases. This study aims to quantify microbial (viral and bacterial) proteins in healthy human plasma. MS data of healthy human plasma were searched against the complete proteomes of all available viruses and bacteria. With this baseline established, the same strategy was applied to characterize the metaproteomic profile of different SARS-CoV-2 disease stages in the plasma of patients. Two SARS-CoV-2 proteins were detected with a high confidence and could serve as the early markers of SARS-CoV-2 infection. The complete bacterial and viral protein content in SARS-CoV-2 samples was compared for the different disease stages. The number of viral proteins was found to increase significantly with the progression of the infection, at the expense of bacterial proteins. This strategy can be extended to aid in the development of early diagnostic tests for other infectious diseases based on the presence of microbial biomarkers in human plasma samples.

Hydrogen deuterium exchange mass spectrometry identifies the dominant paratope in CD20 antigen binding to the NCD1.2 monoclonal antibody

A comparative canine–human therapeutics model is being developed in B-cell lymphoma through the generation of a hybridoma cell that produces a murine monoclonal antibody specific for canine CD20. The hybridoma cell produces two light chains, light chain-3, and light chain-7. However, the contribution of either light chain to the authentic full-length hybridoma derived IgG is undefined. Mass spectrometry was used to identify only one of the two light chains, light chain-7, as predominating in the full-length IgG. Gene synthesis created a recombinant murine–canine chimeric monoclonal antibody expressing light chain-7 that reconstituted the IgG binding to CD20. Using light chain-7 as a reference sequence, hydrogen deuterium exchange mass spectrometry was used to identify the dominant CDR region implicated in CD20 antigen binding. Early in the deuteration reaction, the CD20 antigen suppressed deuteration at CDR3 (V_H). In later time points, deuterium suppression occurred at CDR2 (V_H) and CDR2 (V_L), with the maintenance of the CDR3 (V_H) interaction. These data suggest that CDR3 (V_H) functions as the dominant antigen docking motif and that antibody aggregation is induced at later time points after antigen binding. These approaches define a methodology for fine mapping of CDR contacts using nested enzymatic reactions and hydrogen deuterium exchange mass spectrometry. These data support the further development of an engineered, synthetic canine–murine monoclonal antibody, focused on CDR3 (V_H), for use as a canine lymphoma therapeutic that mimics the human–murine chimeric anti-CD20 antibody Rituximab.

Mass spectrometry-based protein identification in proteomics-a review

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Strategies in 'snake venomics' aiming at an integrative view of compositional, functional, and immunological characteristics of venoms

This work offers a general overview on the evolving strategies for the proteomic analysis of snake venoms, and discusses how these may be combined through diverse experimental approaches with the goal of achieving a more comprehensive knowledge on the compositional, toxic, and immunological characteristics of venoms. Some recent developments in this field are summarized, highlighting how strategies have evolved from the mere cataloguing of venom components (proteomics/venomics), to a broader exploration of their immunological (antivenomics) and functional (toxicovenomics) characteristics. Altogether, the combination of these complementary strategies is helping to build a wider, more integrative view of the life-threatening protein cocktails produced by venomous snakes, responsible for thousands of deaths every year.