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Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory.

ABSTRACT: BACKGROUND: Disulfide bonds constitute one of the most important cross-linkages in proteins and significantly influence protein structure and function. At the state-of-the-art, various methodological frameworks have been proposed for identification of disulfide bonds. These include among others, mass spectrometry-based methods, sequence-based predictive approaches, as well as techniques like crystallography and NMR. Each of these frameworks has its advantages and disadvantages in terms of pre-requisites for applicability, throughput, and accuracy. Furthermore, the results from different methods may concur or conflict in parts. RESULTS: In this paper, we propose a novel and theoretically rigorous framework for disulfide bond determination based on information fusion from different methods using an extended formulation of Dempster-Shafer theory. A key advantage of our approach is that it can automatically deal with concurring as well as conflicting evidence in a data-driven manner. Using the proposed framework, we have developed a method for disulfide bond determination that combines results from sequence-based prediction and mass spectrometric inference. This method leads to more accurate disulfide bond determination than any of the constituent methods taken individually. Furthermore, experiments indicate that the method improves the accuracy of bond identification as compared to leading extant methods at the state-of-the-art. Finally, the proposed framework is extensible in that results from any number of approaches can be incorporated. Results obtained using this framework can especially be useful in cases where the complexity of the bonding patterns coupled with specificities of the fragmentation pattern or limitations of computational models impair any single method to perform consistently across a diverse set of molecules.

SUBMITTER: Singh R

PROVIDER: S-EPMC3549834 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory.

Singh Rahul R Murad William W

BMC bioinformatics 20130121

<h4>Background</h4>Disulfide bonds constitute one of the most important cross-linkages in proteins and significantly influence protein structure and function. At the state-of-the-art, various methodological frameworks have been proposed for identification of disulfide bonds. These include among others, mass spectrometry-based methods, sequence-based predictive approaches, as well as techniques like crystallography and NMR. Each of these frameworks has its advantages and disadvantages in terms of ...[more]

PMID: 23368815

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Project description:BackgroundThe COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior.ObjectiveIn this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19.MethodsWe designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users' insomnia experiences, using logistic regression.ResultsWe extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval.ConclusionsThe proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

Project description:In the biotechnology industry, the generation of incorrectly folded recombinant proteins, either from an E.coli expression system or from an overexpressed CHO cell line (disulfide scrambling), is often a great concern as such incorrectly folded forms may not be completely removed in the final product. Thus, significant efforts have been devoted to map disulfide bonds to ensure drug quality. Similar to ECD, disulfide bond cleavages are preferred over peptide backbone fragmentation in ETD. Thus, an online LC-MS strategy combining collision-induced dissociation (CID-MS(2)), electron-transfer dissociation (ETD-MS(2)), and CID of an isolated product ion derived from ETD (MS(3)) has been used to characterize disulfide-linked peptides. Disulfide-linked peptide ions were identified by CID and ETD fragmentation, and the disulfide-dissociated (or partially dissociated) peptide ions were characterized in the subsequent MS(3) step. The online LC-MS approach is successfully demonstrated in the characterization of disulfide linkages of recombinant human growth hormone (Nutropin), a therapeutic monoclonal antibody, and tissue plasminogen activator (Activase). The characterization of disulfide-dissociated or partially dissociated peptide ions in the MS(3) step is important to assign the disulfide linkages, particularly, for intertwined disulfide bridges and the unexpected disulfide scrambling of tissue plasminogen activator. The disulfide-dissociated peptide ions are shown to be obtained either directly from the ETD fragmentation of the precursors (disulfide-linked peptide ions) or indirectly from the charge-reduced species in the ETD fragmentation of the precursors. The simultaneous observation of disulfide-linked and disulfide-dissociated peptide ions with high abundance not only provided facile interpretation with high confidence but also simplified the conventional approach for determination of disulfide linkages, which often requires two separate experiments (with and without chemical reduction). The online LC-MS with ETD methodology represents a powerful approach to aid in the characterization of the correct folding of therapeutic proteins.

Dataset Information

Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory.

Publications

Protein disulfide topology determination through the fusion of mass spectrometric analysis and sequence-based prediction using Dempster-Shafer theory.

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