한빛사 논문
Taeheon Lee1, Sangseon Lee2, Minji Kang3 & Sun Kim4,5,6,7,*
1Looxid Labs, Seoul 06628, Republic of Korea. 2BK21 FOUR Intelligence Computing, Seoul National University, Seoul 08826, Republic of Korea. 3Department of Computer Science, Stanford University, Stanford, CA 94305, USA. 4Bioinformatics Institute, Seoul National University, Seoul 08826, Republic of Korea. 5Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Republic of Korea. 6Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Republic of Korea. 7Institute of Engineering Research, Seoul National University, Seoul 08826, Republic of Korea.
*Correspondence to Sun Kim.
Abstract
GPCR proteins belong to diverse families of proteins that are defined at multiple hierarchical levels. Inspecting relationships between GPCR proteins on the hierarchical structure is important, since characteristics of the protein can be inferred from proteins in similar hierarchical information. However, modeling of GPCR families has been performed separately for each of the family, subfamily, and sub-subfamily level. Relationships between GPCR proteins are ignored in these approaches as they process the information in the proteins with several disconnected models. In this study, we propose DeepHier, a deep learning model to simultaneously learn representations of GPCR family hierarchy from the protein sequences with a unified single model. Novel loss term based on metric learning is introduced to incorporate hierarchical relations between proteins. We tested our approach using a public GPCR sequence dataset. Metric distances in the deep feature space corresponded to the hierarchical family relation between GPCR proteins. Furthermore, we demonstrated that further downstream tasks, like phylogenetic reconstruction and motif discovery, are feasible in the constructed embedding space. These results show that hierarchical relations between sequences were successfully captured in both of technical and biological aspects.
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