Computational science research into 'protein folding'

Tessellate Data Science supports 'protein folding' research via Folding@home. This computational approach uses mathematical descriptions and numerical methods, to be able to run investigations. With folding, 3-dimensional structures (and also the evolution) of building-blocks of life are explored.

Tessellate Data Science actively supports 'protein folding' research using a computational approach. Such computational science uses mathematical-based descriptions and methods to predict the 3-dimensional structures (and also evolution) of these biological building-blocks of life.

These techniques include both mechanistic (Folding@home) and machine-learning (RoseTTAFold) approaches. Both research organisations have developed their own easily-executed software packages, allowing anyone with a computer to contribute to research with the click of a mouse.

We have our own computational node (based on more common GPU hardware within a 'workstation'-type desktop) where we run both Folding@home [1] and RoseTTAFold [2] code.

TDS also aims to increase the understanding of the larger-scale behaviour of folding donators by developing software to track more quantitative aspects of these volunteers, and their respective nodes contribution to these computational research efforts.

We believe we are contributing in a very impactful way by helping to reduce burdens of viruses that affect people all over the  world. We are grateful for all the vaccinations that save millions of people's lives, while also being mindful that such enormous measures probably could not have been developed without protein-folding research.