EdeepSADPr: an extensive deep-learning architecture for prediction of the in situ crosstalks of serine phosphorylation and ADP-ribosylation.

The in situ post-translational modification (PTM) crosstalk refers to the interactions between different types of PTMs that occur on the same residue site of a protein. The crosstalk sites generally have different characteristics from those with the single PTM type. Studies targeting the latter's features have been widely conducted, while studies on the former's characteristics are rare. For example, the characteristics of serine phosphorylation (pS) and serine ADP-ribosylation (SADPr) have been investigated, whereas those of their in situ crosstalks (pSADPr) are unknown. In this study, we collected 3,250 human pSADPr, 7,520 SADPr, 151,227 pS and 80,096 unmodified serine sites and explored the features of the pSADPr sites. We found that the characteristics of pSADPr sites are more similar to those of SADPr compared to pS or unmodified serine sites. Moreover, the crosstalk sites are likely to be phosphorylated by some kinase families (e.g., AGC, CAMK, STE and TKL) rather than others (e.g., CK1 and CMGC). Additionally, we constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. We also used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. The best classifiers had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites, respectively. The lowest prediction accuracy was achieved by separating pSADPr and SADPr sites, which is consistent with the observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/. We expect our investigation will promote a comprehensive understanding of crosstalks.

Nurses' Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions.

BACKGROUND: Web-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns. OBJECTIVE: The objective of this study was to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic. METHODS: We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses' evolving perspectives based on temporal changes. RESULTS: We identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns. CONCLUSIONS: The analysis demonstrates that professional web-based forums capture nuanced details about nurses' work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises.

CNNArginineMe: A CNN structure for training models for predicting arginine methylation sites based on the One-Hot encoding of peptide sequence.

Protein arginine methylation (PRme), as one post-translational modification, plays a critical role in numerous cellular processes and regulates critical cellular functions. Though several in silico models for predicting PRme sites have been reported, new models may be required to develop due to the significant increase of identified PRme sites. In this study, we constructed multiple machine-learning and deep-learning models. The deep-learning model CNN combined with the One-Hot coding showed the best performance, dubbed CNNArginineMe. CNNArginineMe performed best in AUC scoring metrics in comparisons with several reported predictors. Additionally, we employed CNNArginineMe to predict arginine methylation proteome and performed functional analysis. The arginine methylated proteome is significantly enriched in the amyotrophic lateral sclerosis (ALS) pathway. CNNArginineMe is freely available at https://github.com/guoyangzou/CNNArginineMe.

Engineering of Src Homology 2 Domain Leading to Sulfotyrosine Recognition With a High Affinity by Integrating a Distinctive Selection Theme and Next-Generation Sequencing.

Tyrosine sulfation plays a vital role in various biochemical reactions. Although sulfated tyrosine (sTyr) has a similar structure to phosphotyrosine (pTyr), the number of available sTyr sites is significantly less than that of pTyr sites, mainly because of the lack of effective sTyr probes. A few sTyr binders were identified on the basis of structural similarity by engineering the pTyr-binding pocket of an Src Homology 2 (SH2) domain through phage selections against sTyr peptides. Nevertheless, they still interact with pTyr peptides with comparable affinity. This study aims to identify sTyr superbinders using the SH2 domain as a template. We created a distinctive phage selection scheme that separately covered selections against sTyr and pTyr peptides, followed by next-generation sequencing (NGS). After selections, phage pools showed strong enzyme-linked immunosorbent assay (ELISA) signal intensities for both modified peptides, indicating that the variants evolved with a high affinity for these peptides, which causes difficulty in identifying sTyr-specific binders. In contrast, NGS data from selected pools showed significant differences, suggesting the enrichment of sTyr-specific variants during selections. Accordingly, we obtained the sTyr features based on NGS data analysis and prioritized a few potential sTyr binders. The variant SH2-4 showed a stronger affinity for sTyr than pTyr and was superior to previous sTyr binders as measured by the Biolayer Interferometry assay. In summary, we described the strategy of integrating NGS data mining with a novel selection scheme to identify sTyr superbinders.

Designing for the Co-Use of Consumer Health Technology in Self-Management of Adolescent Overweight and Obesity: Mixed Methods Qualitative Study.

BACKGROUND: Overweight and obesity in adolescents has reached epidemic proportions in the United States. Consumer health technology (CHT) can serve as a behavioral and social support tool for the management of overweight in adolescence. Recognizing CHT as a social support tool during design enables input from multiple stakeholders who engage in shared co-use to reinforce and empower adolescents in their self-management efforts. OBJECTIVE: This study aimed to explore design requirements and enabling factors for the use of CHT as a social support tool for patients (as primary users) and parents and health care providers (as co-users). Our model incorporates key components of the unified theory of acceptance and use of technology (UTAUT) within the framework of the obesity care model (OCM) by recognizing patient self-management as the central process with the influence of their care support network on CHT use and outcomes. METHODS: This study was part of a larger two-staged usability study combining focus group, semistructured interviews, and usability walkthroughs of CHT mockups from adolescents (BMI in the 85th-99th percentile range), parents, and physicians. In phase 1, 48 adolescents between the ages of 12 and 17 years, 10 of their parents, and 6 health care providers participated in identifying design requirements and enabling factors for the use of a potential CHT. In phase 2, 70 adolescents and 10 health care providers evaluated the CHT mockups and indicated enabling factors and willingness to use the proposed CHT. RESULTS: Our qualitative analysis identified adolescents' intention for the use of CHT in alignment with UTAUT elements of performance expectancy, effort expectancy, and facilitating conditions. Our reconceptualization of social influence identified the expectations and envisioned roles of parents and health care providers as co-users and influencing factors on the co-use of CHT in managing overweight in adolescence. Parents were expected to monitor, to provide guidance and motivation, and to suggest modifications in daily habits, for example, recipes and meals, whereas health care providers were expected to encourage and monitor progress in a clinical setting. These expected roles and co-use patterns were congruent among all 3 stakeholders; the co-use of CHT was desired to be minimally invasive for parents and health care providers and controlled by the adolescents. CONCLUSIONS: Our study integrates and extends the perspectives of 2 seminal models to explore design features and social influence roles for the successful user-centered design of CHT for weight self-management in adolescents. Although the co-users (ie, adolescents, parents, health care providers) suggested differing features consistent with their roles, role definitions were congruent. All users recognized the adolescent as the primary user with differential, supportive use from parents and health care providers. This multistakeholder approach can guide successful CHT design that reinforces the collective perspective of self-management.