Source code for the paper 'Computational Discovery and Systematic Analysis of Protein Entangling Motifs in Nature: From Algorithm to Database'

Nontrivial protein topology has become a unique dimension of protein engineering for manipulating proteins’ stability and dynamics. However, topological proteins are rare in nature and their design, synthesis and applications have been hindered by the limited number of available entangling motifs as synthetic templates. It is awfully difficult to discover such motifs in nature since entangling is a delicate structural feature not apparent in their sequences. Herein, we used the ESMFold model to screen the UniProt Knowledgebase sequence database for potential protein entangling motifs. Automatic filtration steps were developed to assess the reliability, extent of chain intertwining, and suitability in designing topological proteins of predicted structures, leading to a database of 1115 entangling protein motifs. In our database, 73.3% of C2 entangling motifs and 80.1% of C3 entangling motifs were reported with low structural similarity to known protein structures. The entangled structures in the database could be categorized into different groups and their functional and biological significances were analyzed. The results were summarized as an online database in a web platform to allow easy access to this expanded toolbox of entangling motifs, which shall greatly spur research endeavors in protein topology engineering.