QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure.

Sucharita Dey,Emmanuel D Levy,Jaime Prilusky

doi:10.3389/fmolb.2021.787510

Sucharita Dey, Emmanuel D Levy + Show 1 more

Open Access

https://doi.org/10.3389/fmolb.2021.787510

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Abstract

The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

Highlights

Protein self-interactions are prevalent, and they drive the formation of homo-oligomeric structures (Goodsell and Olson, 2000; Levy et al, 2005; Levy and Teichmann, 2013; Marsh and Teichmann, 2015)
The quaternary structures (QSs) resulting from the search of the NR100 dataset are shown as a table and provide an overview of all QS types that exist among homologs, while prioritizing closely related sequences
Those QSs are often annotated by QSalign, and we provide a confidence estimate for these QSs based on their QSalign annotation

Summary

INTRODUCTION

Protein self-interactions are prevalent, and they drive the formation of homo-oligomeric structures (Goodsell and Olson, 2000; Levy et al, 2005; Levy and Teichmann, 2013; Marsh and Teichmann, 2015). The PFAM domain architecture of the query sequence is subsequently used to search for homologs with the same domain architecture This search is executed on a non-redundant set of QSs that we call NR50 (described below). The QSs resulting from the search of the NR100 dataset are shown as a table and provide an overview of all QS types that exist among homologs, while prioritizing closely related sequences (even identical sequences). The downloadable archive contains a list of homologs for each assembly as well as their structure in PDB format files. It provides aligned coordinates for the query and target to enable comparing their structure using a local visualization software. According to the same benchmark, the correction of a QS by transitivity (i.e., query QS shows the same sequence but a different geometry to a valid QS) is more error-prone, with an accuracy of 89%

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT