Petr Kouba presents Fast End-to-End Learning on Protein Surfaces

On 2021-10-21 11:00:00 at
Online reading group on "Fast end-to-end learning on protein surfaces"
Sverrisson et al, CVPR 2021

Paper abstract: Proteins’ biological functions are defined by the geometric
and chemical structure of their 3D molecular surfaces. Recent works have shown
that geometric deep learning can be used on mesh-based representations of
proteins to identify potential functional sites, such as binding targets for
potential drugs. Unfortunately though, the use of meshes as the underlying
representation for protein structure has multiple drawbacks including the need
to pre-compute the input features and mesh connectivities. This becomes a
bottleneck for many important tasks in protein science.

In this paper, we present a new framework for deep learning on protein
structures that addresses these limitations. Among the key advantages of our
method are the computation and sampling of the molecular surface on-the-fly
the underlying atomic point cloud and a novel efficient geometric convolutional
layer. As a result, we are able to process large collections of proteins in an
end-to-end fashion, taking as the sole input the raw 3D coordinates and
types of their atoms, eliminating the need for any hand-crafted pre-computed

To showcase the performance of our approach, we test it on two tasks in the
field of protein structural bioinformatics: the identification of interaction
sites and the prediction of protein-protein interactions. On both tasks, we
achieve state-of-the-art performance with much faster run times and fewer
parameters than previous models. These results will considerably ease the
deployment of deep learning methods in protein science and open the door for
end-to-end differentiable approaches in protein modeling tasks such as function
prediction and design.

Paper url:

Instructions for participants: The reading group studies the literature in the
field of pattern recognition and computer vision. At each meeting one or more
papers are prepared for presentation by a single person, the presenter. The
meetings are open to anyone, disregarding their background. It is assumed that
everyone attending the reading group has, at least briefly, read the paper –
not necessarily understanding everything. Attendants should preferably send
questions about the unclear parts to the speaker at least one day in advance.
During the presentation we aim to have a fruitful discussion, a critical
analysis of the paper, as well as brainstorming for creative extensions.

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