Detecting misfolded non-covalent lasso entanglements in protein structures, simulation trajectories, and mass spectrometry data

A previously overlooked class of protein entanglements, non-covalent lasso entanglements (NCLEs), has been found to play a role in widespread protein misfolding. However, understanding the influence NCLEs have on biological processes is hindered by the absence of dedicated algorithms and computational tools to detect and characterize these geometries in protein structures, molecular dynamics simulations, and in comparison to experimental data from limited proteolysis (LiP) and cross-linking (XL) mass spectrometry (MS). Here, we present EntDetect, a software tool designed to: (1) identify non-redundant NCLEs in protein structures, (2) detect misfolded states by comparing NCLE changes through pairwise comparisons of structures, (3) extract structural ensembles consistent with experimental signals from LiP-MS and XL-MS, and (4) investigate proteome-wide protein misfolding using high-throughput MS data. We demonstrate the utility of EntDetect on a simulated structural ensemble of phosphoglycerate kinase (PGK), alongside corresponding LiP- and XL-MS experimental data. Additionally, we detail the application of EntDetect to detect misfolding associated with native NCLEs on a proteome-wide MS dataset and select candidate proteins for further investigation. This protocol is intended for biophysicists, structural biologists, and molecular biologists with domain knowledge of protein structure, mass spectrometry proteomics data, and beginner experience with Python who want to interpret their experimental observations and computer simulations results through the presence and potential misfolding of NCLE topologies. EntDetect is open-source and freely available (https://github.com/obrien-lab-psu/EntDetect). NCLEweb is also available which is a webserver that identifies NCLEs within a given user-uploaded structure (https://www.ncleweb.org/).