P36: A Novel Feature-Preserving Spatial Mapping for Deep
Learning Classification of Ras Structures
SessionPoster Reception
Event Type
ACM Student Research Competition
Poster
Reception
TimeTuesday, November 14th5:15pm -
7pm
LocationFour Seasons Ballroom
DescriptionA protein’s 3D structure determines its functionality,
and is therefore a topic of great importance. This work
leverages the power of Convolutional Neural Networks
(CNNs) to classify proteins and extract features
directly from their 3D structures. So far, researchers
have been unable to fully exploit 3D structural
information with 2D CNNs, partly because it is difficult
to encode 3D data into the 2D format that can be
ingested by such networks. We designed and implemented a
novel method that maps 3D models to 2D data grids as a
preprocessing step for 2D CNN use. Our experiments
focused on the Ras protein family, which has been linked
to various forms of cancer. Our trained CNNs are able to
distinguish between two branches within the Ras family,
HRas and KRas, which are similar in sequence and
structure. Analysis of saliency maps suggests
classification is accomplished by detection of
structurally and biologically-meaningful sites.
