Scientist Next Door

In this period of lock down caused by COVID-19, children are home schooled. We have joined the “Scientist Next Door ” project, organised by scientists from the university of Edinburgh and Durham, to share our excitement for science with them!

We hope to hold group video calls with families and discuss things we find interesting, share ideas and resources. And, as Scientists Next Door,  after the lockdown is over, it would be great to meet in person!

If you are a scientist, consider joining too. If you are parent, get in touch!


Protein docking with JabberDock

We are very excited to present JabberDock, our new protein-protein docking algorithm. JabberDock is capable of accommodating for rearrangements upon binding including side chain reorientations and backbone flexibility. To do this, it leverages Spatial and Temporal Intensity (STID) maps, our single volumetric representation for proteins surface, electrostatics and local dynamics. JabberDock is freely available on Github, and is presented in the following article:

Rudden L.P., Degiacomi M.T. (2019), Protein docking using a single representation for protein surface, electrostatics and local dynamics. Journal of Chemical Theory and Computation

This publication not only complies to the Palatinate Challenge but, more importantly, it is the first article of Lucas Rudden, PhD student in our group. Congratulations!

Welcome Lorenza

Today we welcome Lorenza Pacini, PhD student in ENS Lyon under the supervision of Dr. Claire Lesieur and Prof. Laurent Vuillon (Université Savoie Mont Blanc). Lorenza will spend one month with us, developing methods to model protein fibrils. Looking forward to some collaborative software development!

Well done Sam!

Sam successfully completed his Masters project with us, presenting a great poster on his study of peptide-lipid interactions by molecular dynamics simulations. Congratulations!

Chaperone-regulated mechanosensation

Small Heat Shock proteins such as HspB1 are molecular chaperones in charge of preventing harmful misfolding of proteins under stress conditions. Work led by the Benesch and Gehmlich groups shows, from muscle fibers down to single molecules, that phosphorylation of HspB1 alters its intramolecular dynamics, facilitating its binding to the mechanosensitive Filamin C. In good correspondence with NMR data, our calculations reveal that over over the course of microsecond-long simulations the N-terminus of HspB1 detaches from the rest of the protein.

Collier M.T., Alderson, T.R., de Villiers, C.P., Nicholls, D. , Gastall, H.Y., Allison, T.M., Degiacomi M.T., Jiang, H., Mlynek, G., Fürst, D.O., van der Ven, P.F.M., Djinovic-Carugo, K., Baldwin, A.J., Watkins, H., Gehmlich, K., Benesch, J.L.P. (2019), HspB1 phosphorylation regulates its intramolecular dynamics and mechanosensitive molecular chaperone interaction with filamin C. Science Advances, 5(5)

What can one do with a neural network trained on molecular dynamics simulations?

GAIn recent years, generative neural networks have been gaining popularity owing to their ability to produce believable fake data including photos, video and even news. The creativity of these neural networks lies on their capacity of generating something new based on collection of examples provided as input. We show that autoencoders, a kind of neural network, can be exploited to generate meaningful protein conformations.

Protein molecular function and malfunction in an organism is often linked to their interconversion between states caused by changes in environmental conditions or binding of ligands such as drugs or other proteins. In this context, we demonstrate two possible usages for an autoencoder: (1) predicting the transition path between two states sampled by MD simulations, when no sampling of the intermediates is available; (2) coupling the autoencoder with POWer, our protein docking algorithm, to help the prediction of proteins’ arrangement into a complex when the subunits undergo substantial conformational change.

M.T. Degiacomi, “Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space”, Structure, 2019