Insight into the Initial Stages of the Folding Process in Onconase Revealed by UNRES

Abstract

The unfolded state of proteins presents many challenges to elucidate the structural basis for biological function. This state is characterized by a large degree of structural heterogeneity which makes it difficult to generate structural models. However, recent experiments into the initial folding events of the 104-residue ribonuclease homologue onconase (ONC) were able to identify the regions in the protein that participate in the initial folding of this protein. Therefore, to gain additional structural insight into the unfolded state of proteins, this study utilized molecular dynamics simulations using the UNited-RESidue (UNRES) force field to evaluate whether there is a good agreement between the experimentally determined initial structures and the structures identified by computer simulations along a folding pathway. Indeed, these UNRES simulations accurately identified the two regions experimentally observed to form the initial native structure along the folding pathway of ONC. In addition, these regions are determined to be chain folding initiation sites (CFIS) according to methods developed previously. Subsequent self-organization maps (SOM) analysis has revealed key structural states involved in these early folding events.

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“…A Self-Organizing Map (SOM) is an unsupervised learning method that allows the visualization of multidimensional data in a low-dimensional representation . Several applications of SOMs to the analysis of biomolecular simulations can be found in the literature ranging from clustering of ligand poses in virtual screening to analysis of pathways in enhanced sampling MD simulations. − …”

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

“…A Self-Organizing Map (SOM) is an unsupervised learning method that allows visualization of multidimensional data in a low-dimensional representation and their clustering by keeping similar input data close to each other in the map [77][78][79] . Several applications of SOMs to the analysis of biomolecular simulations can be found in the literature ranging from clustering of ligand poses in virtual screening 80 to clustering of protein conformations from MD trajectories 77,81 and analysis of pathways in enhanced sampling MD simulations [82][83][84] . In this work, we used the PathDetect-SOM tool 85 to investigate molecular features of the sampled bound states and recognize differences in the configurations sampled during the MetaD simulations.…”

“…[77][78][79] Several applications of SOMs to the analysis of biomolecular simulations can be found in the literature ranging from the clustering of ligand poses in virtual screening 80 to the clustering of protein conformations from MD trajectories 77,81 and analysis of pathways in enhanced sampling MD simulations. [82][83][84] In this work, we used the PathDetect-SOM tool 85 to investigate molecular features of the sampled bound states and recognize differences in the configurations sampled during the MetaD simulations. For the SOM training, we used a 10 × 10 toroidal SOM (with periodicity across the boundaries) with a hexagonal lattice shape.…”