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

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s