Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

McIlwain, Sean J.; Wu, Zhijie; Wetzel, Molly; Belongia, Daniel; Jin, Yutong; Wenger, Kent; Ong, Irene M.; Ge, Ying

doi:10.1021/jasms.0c00035

Cited by 20 publications

(23 citation statements)

References 27 publications

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“…Therefore, the high-resolution glycoform mapping presented in Figure 5 illustrates a unique strength of this native top-down MS approach to achieve isotopically resolved and high-accuracy characterization of highly heterogeneous glycoproteins, a major challenge in intact glycoprotein analysis. 36 , 66 …”

Section: Resultsmentioning

confidence: 99%

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry

et al. 2021

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes an extensively glycosylated surface spike (S) protein to mediate host cell entry, and the S protein glycosylation plays key roles in altering the viral binding/function and infectivity. However, the molecular structures and glycan heterogeneity of the new O-glycans found on the S protein regional-binding domain (S-RBD) remain cryptic because of the challenges in intact glycoform analysis by conventional bottom-up glycoproteomic approaches. Here, we report the complete structural elucidation of intact O-glycan proteoforms through a hybrid native and denaturing top-down mass spectrometry (MS) approach employing both trapped ion mobility spectrometry (TIMS) quadrupole time-of-flight and ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR)-MS. Native top-down TIMS-MS/MS separates the protein conformers of the S-RBD to reveal their gas-phase structural heterogeneity, and top-down FTICR-MS/MS provides in-depth glycoform analysis for unambiguous identification of the glycan structures and their glycosites. A total of eight O-glycoforms and their relative molecular abundance are structurally elucidated for the first time. These findings demonstrate that this hybrid top-down MS approach can provide a high-resolution proteoform-resolved mapping of diverse O-glycoforms of the S glycoprotein, which lays a strong molecular foundation to uncover the functional roles of their O-glycans. This proteoform-resolved approach can be applied to reveal the structural O-glycoform heterogeneity of emergent SARS-CoV-2 S-RBD variants as well as other O-glycoproteins in general.

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Section: Resultsmentioning

confidence: 99%

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry

et al. 2021

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“…Therefore, the high resolution glycoform mapping presented in Figure 5 illustrates a unique strength of this native top-down MS approach to achieve isotopically resolved and high accuracy characterization of highly heterogenous glycoproteins, a major challenge in intact glycoprotein analysis. 40,65 We also identified and characterized core 1 (Galβ1-3GalNAc-Ser/Thr) O-glycan structures such as GalNAcGalNeuAc (15+ most abundant charge state, centered at 1723.8 m/z) (Figure S7), and GalNAcGal(NeuAc)2 (15+ most abundant charge state, centered at 1743.2 m/z) (Figure S8). Interestingly, these two Core 1 O-glycans were previously reported for the S-RBD as potential modifications, however the previous studies were not able to resolve the exact glycoforms due to the challenges arising from inferring intact glycoprotein structures from peptide digests and the signal low abundance of O-glycans under conventional MS analysis.…”

Section: Comprehensive Characterization Of S-rbd O-glycoforms By High Resolution Top-down Msmentioning

confidence: 99%

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Native Top-Down Mass Spectrometry

Roberts

Mann

Melby

et al. 2021

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes an extensively glycosylated surface spike (S) protein to mediate host cell entry and the S protein glycosylation is strongly implicated in altering viral binding/function and infectivity. However, the structures and relative abundance of the new O-glycans found on the S protein regional-binding domain (S-RBD) remain cryptic because of the challenges in intact glycoform analysis. Here, we report the complete structural characterization of intact O-glycan proteoforms using native top-down mass spectrometry (MS). By combining trapped ion mobility spectrometry (TIMS), which can separate the protein conformers of S-RBD and analyze their gas phase structural variants, with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS analysis, the O-glycoforms of the S-RBD are comprehensively characterized, so that seven O-glycoforms and their relative molecular abundance are structurally elucidated for the first time. These findings demonstrate that native top-down MS can provide a high-resolution proteoform-resolved mapping of diverse O-glycoforms of the S glycoprotein, which lays a strong molecular foundation to uncover the functional roles of their O-glycans. This proteoform-resolved approach can be applied to reveal the structural O-glycoform heterogeneity of emergent SARS-CoV-2 S-RBD variants, as well as other O-glycoproteins in general.

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“…The lung CT image

contains two ROI

to be enhanced [ 51 , 52 ], where

and

are the left and right lung regions, respectively.

is the convolution kernel size of

(in this paper,

is set).…”

Section: Image Preprocessingmentioning

confidence: 99%

COVID-19 deep classification network based on convolution and deconvolution local enhancement

Fang

Wang

2021

Computers in Biology and Medicine

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Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.

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Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy

Cited by 20 publications

References 27 publications

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Top-Down Mass Spectrometry

Structural O-Glycoform Heterogeneity of the SARS-CoV-2 Spike Protein Receptor-Binding Domain Revealed by Native Top-Down Mass Spectrometry

COVID-19 deep classification network based on convolution and deconvolution local enhancement

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