In Proteomics, only the de novo peptide sequencing approach allows a partial amino acid sequence of a peptide to be found from a MS/MS spectrum. In this article a preliminary work is presented to discover a complete protein sequence from spectral data (MS and MS/MS spectra). For the moment, our approach only uses MS spectra. A Genetic Algorithm (GA) has been designed with a new evaluation function which works directly with a complete MS spectrum as input and not with a mass list like the other methods using this kind of data. Thus the mono isotopic peak extraction step which needs a human intervention is deleted. The goal of this approach is to discover the sequence of unknown proteins and to allow a better understanding of the differences between experimental proteins and proteins from databases.
Improving the Results of De novo Peptide Identification via Tandem Mass Spectrometry Using a Genetic Programming-based Scoring Function for Re-ranking Peptide-Spectrum Matches
We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic sig…
Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules. This larger number of molecules and the associated larger dynamic range have major implications in the development of proteomics technologies. We examine these implications for both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and single-molecule counting and provide estimates on how many molecules are routinely measured in proteomics experiments by LC-MS/MS. We review strategies that have been helpful for counting billions of protein molecules by LC-MS/MS and suggest that these strategies can benef…
Jiangsu University of Technology（江苏理工学院）；Changzhou University（常州大学）；China University of Mining and Technology（中国矿业大学）
De novo peptide sequencing from mass spectrometry data is an important method for protein identification. Recently, various deep learning approaches were applied for de novo peptide sequencing and DeepNovoV2 is one of the represetative models. In this study, we proposed an enhanced model, DePS, which can improve the accuracy of de novo peptide sequencing even with missing signal peaks or large number of noisy peaks in tandem mass spectrometry data. It is showed that, for the same test set of DeepNovoV2, the DePS model achieved excellent results of 74.22%, 74.21% and 41.68% for amino acid recal…
Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids, but single-cell protein analysis remains limited. Mass-spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: Efficiently delivering proteins/peptides to MS detectors, identifying their sequences, and scaling the analysis to many thousands of si…
Metaproteomics are becoming widely used in microbiome research for gaining insights into the functional state of the microbial community. Current metaproteomics studies are generally based on high-throughput tandem mass spectrometry (MS/MS) coupled with liquid chromatography. The identification of peptides and proteins from MS data involves the computational procedure of searching MS/MS spectra against a predefined protein sequence database and assigning top-scored peptides to spectra. Existing computational tools are still far from being able to extract all the information out of large MS/MS …
Nanobodies are small antibody fragments derived from camelids that selectively bind to antigens. These proteins have marked physicochemical properties that support advanced therapeutics, including treatments for SARS-CoV-2. To realize their potential, bottom-up proteomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been proposed to identify antigen-specific nanobodies at the proteome scale, where a critical component of this pipeline is matching nanobody peptides to their begotten tandem mass spectra. While peptide-spectrum matching is a well-studied problem, we show the …
Exploring the Utility of MALDI-TOF Mass Spectrometry and Antimicrobial Resistance in Hospital Outbreak Detection
Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；School of Public Health, University of Pittsburgh（匹兹堡大学公共卫生学院）
De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data. Existing approaches for de novo analysis enumerate MS evidence for all amino acid classes during inference. It leads to over-trimming on receptive fields of MS data and restricts MS evidence associated with following undecoded amino acids. Our approach, DPST, circumvents these limitations with two key components: (1) A confidence value aggregation encoder to sketch spectrum representations according to amino-acid-based connectivity among MS; (2) A global-local fusion decoder to…
Optimizing Mirror-Image Peptide Sequence Design for Data Storage via Peptide Bond Cleavage Prediction
School of Computer Engineering and Science（计算机工程与科学学院）；School of Medicine（医学院）；School of Computer and information Engineering（计算机与信息工程学院）；Institute for Artificial Intelligence（人工智能研究院）
Accurate de novo sequencing of the modified proteome with OmniNovo
Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究所）；Tongji University（同济大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Electronic Engineering（电子工程系）；Tsinghua University（清华大学）；Research Institute of lntelligent Complex Systems（智能复杂系统研究所）；Fudan University（复旦大学）；University of British Columbia（不列颠哥伦比亚大学）；School of Medicine（医学院）；Westlake University（西湖大学）；Shanghai Innovation Institute（上海创新研究院）；Zhejiang University（浙江大学）；Stony Brook University（石溪大学）
Automating LC-MS/MS mass chromatogram quantification. Wavelet transform based peak detection and automated estimation of peak boundaries and signal-to-noise ratio using signal processing methods
Technische Universität Dresden (TUD)（德累斯顿工业大学）；Medical School Berlin (MSB)（柏林医科大学）；Pfizer Germany GmbH（辉瑞德国有限公司）
