Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations

Abstract

Nowadays, molecular dynamics (MD) simulations of proteins with hundreds of thousands of snapshots are commonly produced using modern GPUs. However, due to the abundance of data, analyzing transport tunnels present in the internal voids of these molecules, in all generated snapshots, has become challenging. Here, we propose to combine the usage of CAVER3, the most popular tool for tunnel calculation, and the TransportTools Python3 library into a divide-and-conquer approach to speed up tunnel calculation and reduce the hardware resources required to analyze long MD simulations in detail. By slicing an MD trajectory into smaller pieces and performing a tunnel analysis on these pieces by CAVER3, the runtime and resources are considerably reduced. Next, the TransportTools library merges the smaller pieces and gives an overall view of the tunnel network for the complete trajectory without quality loss.