Abstract BackgroundProtein S-nitrosylation (SNO) plays a key role in transferring nitric oxide-mediated signals in both animals and plants and has emerged as an important mechanism for regulating protein functions and cell signaling of all main classes of protein. It is involved in several biological processes including immune response, protein stability, transcription regulation, post translational regulation, DNA damage repair, redox regulation, and is an emerging paradigm of redox signaling for protection against oxidative stress. The development of robust computational tools to predict protein SNO sites would contribute to further interpretation of the pathological and physiological mechanisms of SNO. ResultsUsing an intermediate fusion-based stacked generalization approach, we integrated embeddings from supervised embedding layer and contextualized protein language model (ProtT5) and developed a tool called pLMSNOSite (protein language model-based SNO site predictor). On an independent test set of experimentally identified SNO sites, pLMSNOSite achieved values of 0.340, 0.735 and 0.773 for MCC, sensitivity and specificity respectively. These results show that pLMSNOSite performs better than the compared approaches for the prediction of S-nitrosylation sites. ConclusionTogether, the experimental results suggest that pLMSNOSite achieves significant improvement in the prediction performance of S-nitrosylation sites and represents a robust computational approach for predicting protein S-nitrosylation sites. pLMSNOSite could be a useful resource for further elucidation of SNO and is publicly available athttps://github.com/KCLabMTU/pLMSNOSite.
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ProTaxoVis—protein taxonomic visualisation of presence
Abstract Background:Protein presence information is an essential component of biological pathway identification. Presence of certain enzymes in an organism points towards the metabolic pathways that occur within it, whereas the absence of these enzymes indicates either the existence of alternative pathways or a lack of these pathways altogether. The same inference applies to regulatory pathways such as gene regulation and signal transduction. Protein presence information therefore forms the basis for biological pathway studies, and patterns in presence-absence across multiple organisms allow for comparative pathway analyses. Results:Here we present ProTaxoVis, a novel bioinformatic tool that extracts protein presence information from database queries and maps it to a taxonomic tree or heatmap. ProTaxoVis generates a large-scale overview of presence patterns in taxonomic clades of interest. This overview reveals protein distribution patterns, and this can be used to deduce pathway evolution or to probe other biological questions. ProTaxoVis combines and filters sequence query results to extract information on the distribution of proteins and translates this information into two types of visual outputs: taxonomic trees and heatmaps. The trees supplement their topology with scaled pie-chart representations per node of the presence of target proteins and combinations of these proteins, such that patterns in taxonomic groups can easily be identified. The heatmap visualisation shows presence and conservation of these proteins for a user-determined set of species, allowing for a more detailed view over a larger group of proteins as compared to the trees. ProTaxoVis also allows for visual quality checks of hits based on a coverage plot and a length histogram, which can be used to determine e-value and minimum protein length cutoffs. Tabular output of resulting data from the query, combined, and heatmap building step are saved and easily accessible for further analyses. Conclusions:We evaluate our tool with the phosphoribosyltransferases, a transferase enzyme family with notable distribution patterns amongst organisms of varying complexities and across Eukaryota, Bacteria, and Archaea. ProTaxoVis is open-source and available at:https://github.com/MolecularBioinformatics/ProTaxoVis.
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- Award ID(s):
- 2312378
- PAR ID:
- 10609969
- Publisher / Repository:
- GitHub
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 26
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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