Deep Learning Methods for De Novo Peptide Sequencing.

Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi-layer neural networks to translate from observed mass spectra to corresponding peptide sequences.

Sequence-to-sequence translation from mass spectra to peptides with a transformer model.

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information-de novo peptide sequencing-is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics.

A learned score function improves the power of mass spectrometry database search.

Motivation: One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database.

Bioadhesive Gauze Embedded with Chitosan-Butein Bioconjugate: A Redox-Active pH Sensor Platform.

With the ever-growing global wound care market, demand for robust redox-active healthcare material is obvious for the construction of wearable sensor platforms. Surface reactive functional group-rich material like chitosan holds huge potential for electrochemical biosensor application. Herein, a metal-free redox-active chitosan-butein (CSB) bioconjugate is processed into epidermal bioadhesive electrode material useful for pH sensors promising toward wound site analysis.

Optimal Drug Regimen and Combined Drug Therapy and Its Efficacy in the Treatment of COVID-19: A Within-Host Modeling Study.

The COVID-19 pandemic has resulted in more than 524 million cases and 6 million deaths worldwide. Various drug interventions targeting multiple stages of COVID-19 pathogenesis can significantly reduce infection-related mortality. The current within-host mathematical modeling study addresses the optimal drug regimen and efficacy of combination therapies in the treatment of COVID-19.