STRPsearch: fast detection of structured tandem repeat proteins

Abstract

Motivation: State-of-the-art prediction methods are generating millions of publicly available protein structures. Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. STRPs exhibit distinct propensities for secondary structure and form regular tertiary structures, often comprising large molecular assemblies. They can perform important and diverse biological functions due to their highly degenerated sequences, which maintain a similar structure while displaying a variable number of repeat units. This suggests a disconnection between structural size and protein function. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation is the most accurate method for detecting and classifying them, making it impossible to inspect millions of structures. Results: We present STRPsearch, a novel computational tool for rapid identification, classification, and mapping of STRPs. Leveraging the manually curated entries in RepeatsDB as the known conformational space of the STRPs, STRPsearch utilizes the latest advancements in structural alignment techniques for a fast and accurate detection of repeated structural motifs in protein structures, followed by an innovative approach to map units and insertions through the generation of TM-score graphs. STRPsearch can serve researchers in structural bioinformatics and protein science as an efficient and practical tool for analysis and detection of STRPs. Availability and implementation: STRPsearch is coded in Python, all the scripts and the associated documentation are available at https://github.com/BioComputingUP/STRPsearch . Contact: alexander.monzon@unipd.it , silvio.tosatto@unipd.it Supplementary information: Supplementary data are available..