GOKNOT is the acronym for “Modelling the formation of a gordian knot in Human Ubiquitin Hydrolase”.
In this project I aim at studying a protein, Human Ubiquitin Hydrolase, that is highly expressed in the human brain, and that has been connected to neurodegenerative diseases such as Parkinson’s and Alzheimer’s.
Another interesting fact is that this protein folds into a self-entangled native conformation, forming a gordian knot with its backbone. There are relatively few proteins that self-tie while folding, and their existence is indeed a puzzling fact,s as they seems in contradiction with some simplicity principles that we assume when trying to understand protein folding.
For example: how is it possible that an amino-acid sequence encodes in itself the exact directives to reproducibly and efficiently form knots, just like we do with shoelaces?
The aim of GOKNOT is to find out more about this entangled folding, by using computer simulations. Since detailed molecular simulations of these phenomena would require ages of computer time, we want to adopt a multi-scale methodology. In brief, in our initial simulations we describe the system in a very simple, minimalistic way, to figure out as much as possible by keeping low computational costs. Then, exploiting what we have learned with these simple descriptions, we increase the detail of the next calculations.
A fully atomistic simulation of entangled protein folding is still unaccessible with computer resources available today, however special techniques, such as enhanced sampling, can reduce the computational time making our purposes less unrealistic. The use of these methods needs some preliminary understanding of the systems, which we will reach exploiting the minimalistic, cheap simulations named few lines above.
A crucial tool used for this study is the PolymerMD software, which implements a very simple molecular dynamics model for entangled protein folding. You can find some more detail, plus the link to the open-source code here.
GOKNOT is carried out in close collaboration with Raffaello Potestio, at the University of Trento.