Manifold learning visualization of metabotropic glutamate receptors

Abstract

G-Protein-Coupled Receptors (GPCRs) are cell membrane proteins with a key role in biological processes. GPCRs of class C, in particular, are of great interest in pharmacology. The lack of knowledge about their 3-D structures means they must be investigated through their primary amino acid sequences. Sequence visualization can help to explore the existing receptor sub-groupings at different partition levels. In this paper, we focus on Metabotropic Glutamate Receptors (mGluR), a subtype of class C GPCRs. Different versions of a probabilistic manifold learning model are employed to comparatively sub-group and visualize them through different transformations of their sequences.

Peer Reviewed

Postprint (author's final draft)