PocketPipe: A computational pipeline for integrated Pocketome prediction and comparison

Functional characterisation of proteins often depends on specific interactions with other molecules. In the drug discovery scenario, the ability of a protein to bind with drug-like molecule with a high affinity is referred as druggability. Deciphering such druggable binding pockets on proteins plays an important role in structure-based drug designing studies. Moreover, availability of plethora of structural data poses a need automated pipelines which can efficiently integrate robust algorithms towards large-scale pocket identification and comparison. These pipelines have direct applicability on off-target analysis, drug repurposing and structural prioritization of drug targets in pathogenic microbes. However, currently there is a paucity of such efficient pipelines. Hence, by this study a highly optimized shell script based pipeline (PocketPipe) has been developed with seamless integration of robust algorithms namely, P2Rank (predicts binding sites based on machine learning) and PocketMatch-v2.1 (compares binding pockets by residue-based method), for pocketome generation and comparison, respectively. The process of input workflow and various steps carried out by PocketPipe and the output results are well documented in the operating manual. On execution, the pipeline features seamless operability, high scalability, dynamic file handling and results parsing. PocketPipe is distributed freely under GNU GPL license and can be downloaded at https://github.com/inpacdb/PocketPipe.