Biomaths (Mathematics) – EPSRC CMPH Seminar



Vladimir Gligorijevic1 Tomasz Kosciolek2 Richard Bonneau1

1 Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, USA

2 Małopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland


The human gut microbiome is estimated to harbor over 3 million unique protein-coding gene families. Only a fraction of them are experimentally annotated and therefore require computational predictions. Community-wide benchmarking efforts such as CAFA show that homology-based function annotation approaches are lacking and require more sophisticated approaches. In this talk, we will introduce deepFRI (deep learning-based functional residue identification), our recently proposed method based on Graph Convolutional Networks [1]. We use protein families, within a deep learning structure prediction framework, to predict protein 3D structures. We show that deepFRI achieves state-of-the-art accuracy in predicting gene ontology terms of proteins with predicted 3D  structural models. We are now in a position to functionally annotate microbial genomes and metagenomes with higher coverage and accuracy. We may also start addressing microbe-microbe and host-microbiome protein-protein interactions to determine the mechanisms of microbiota-induced immune response. An added value of deepFRI is its ability to produce region-specific structure predictions through class activation mapping.

[1] Gligorijevic, V., Renfrew, P.D., Kosciolek, T., Leman, J.K., Cho, K., Vatanen, T., Berenberg, D., Taylor, B.C., Fisk, I.M., Xavier, R.J. and Knight, R., 2019. Structure-Based Function Prediction using Graph Convolutional Networks. bioRxiv, p.786236.