Moritz, S. | Search bioRxiv

bioRxiv2026

Early origin of sugar sensing in jawed vertebrates

Sweet taste guides animals to consume carbohydrate-rich foods, and many different vertebrate groups, from fish to mammals, rely on sugar-rich fruits or nectar produced by flowering plants (angiosperms). Although the genes encoding T1R2-T1R3, the receptor pair that mammals use to sense sugars, exist in the genomes of many vertebrates, their functions are unclear--and whether sugar sensing arose once early in vertebrate evolution or independently in different lineages after angiosperms evolved is currently unknown. Here, we combined ancestral reconstruction and receptor functional profiling to examine the evolutionary history of T1R taste receptors--including recently-described non-canonical receptors--across all major vertebrate clades. Our results pinpoint the origin of sugar sensing to before the emergence of angiosperms and uncover a myriad of alternative T1R-based sugar-sensing mechanisms, suggesting multiple independent T1R trajectories and revealing uncharted sensory diversity across vertebrates.

evolutionary biology↗

bioRxiv2019

Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (< 2.5[A] resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict C atoms along a proteins backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a proteins structure. This model predicts secondary structure elements (SSEs), backbone structure, and C atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each evaluated protein. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with C placements. A helix-refinement algorithm made further improvements to the -helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map C traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6[A] and 4.4[A] resolution. It outperformed several state-of-the-art prediction methods including RosettaES, MAINMAST, and a Phenix based method by producing the most complete prediction models, as measured by percentage of found C atoms. This method accurately predicted 88.5% (mean) of the C atoms within 3[A] of a proteins backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average RMSD of 1.23[A] for all 50 experimental density maps which is similar to the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction.

molecular biology↗