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Abstract Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence‐based predicted contact or distance information is used. Contact‐assisted or distance‐assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query–template alignment. We present a new distance‐ and orientation‐based covariational threading method called DisCovER by effectively integrating information from inter‐residue distance and orientation along with the topological network neighborhood of a query–template alignment. Our method first selects a subset of templates using standard profile‐based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance‐ and orientation‐based query–template alignment using an iterative double dynamic programming framework. Multiple large‐scale benchmarking results on query proteins classified as weakly homologous from the continuous automated model evaluation experiment and from the current literature show that our method outperforms several existing state‐of‐the‐art threading approaches, and that the integration of the neighborhood effect with the inter‐residue distance and orientation information synergistically contributes to the improved performance of DisCovER. DisCovER is freely available athttps://github.com/Bhattacharya-Lab/DisCovER.
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This content will become publicly available on December 1, 2026
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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