Dataset Information

Trilobite compound eyes with crystalline cones and rhabdoms show mandibulate affinities.

ABSTRACT: Most knowledge about the structure, function, and evolution of early compound eyes is based on investigations in trilobites. However, these studies dealt mainly with the cuticular lenses and little was known about internal anatomy. Only recently some data on crystalline cones and retinula cells were reported for a Cambrian trilobite species. Here, we describe internal eye structures of two other trilobite genera. The Ordovician Asaphus sp. reveals preserved crystalline cones situated underneath the cuticular lenses. The same is true for the Devonian species Archegonus (Waribole) warsteinensis, which in addition shows the fine structure of the rhabdom in the retinula cells. These results suggest that an apposition eye with a crystalline cone is ancestral for Trilobita. The overall similarity of trilobite eyes to those of myriapods, crustaceans, and hexapods corroborates views of a phylogenetic position of trilobites in the stem lineage of Mandibulata.

SUBMITTER: Scholtz G

PROVIDER: S-EPMC6555793 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trilobite compound eyes with crystalline cones and rhabdoms show mandibulate affinities.

Scholtz Gerhard G Staude Andreas A Dunlop Jason A JA

Nature communications 20190607 1

Most knowledge about the structure, function, and evolution of early compound eyes is based on investigations in trilobites. However, these studies dealt mainly with the cuticular lenses and little was known about internal anatomy. Only recently some data on crystalline cones and retinula cells were reported for a Cambrian trilobite species. Here, we describe internal eye structures of two other trilobite genera. The Ordovician Asaphus sp. reveals preserved crystalline cones situated underneath ...[more]

PMID: 31175282

Similar Datasets

Project description:Predicting compound-protein affinity is beneficial for accelerating drug discovery. Doing so without the often-unavailable structure data is gaining interest. However, recent progress in structure-free affinity prediction, made by machine learning, focuses on accuracy but leaves much to be desired for interpretability. Defining intermolecular contacts underlying affinities as a vehicle for interpretability; our large-scale interpretability assessment finds previously used attention mechanisms inadequate. We thus formulate a hierarchical multiobjective learning problem, where predicted contacts form the basis for predicted affinities. We solve the problem by embedding protein sequences (by hierarchical recurrent neural networks) and compound graphs (by graph neural networks) with joint attentions between protein residues and compound atoms. We further introduce three methodological advances to enhance interpretability: (1) structure-aware regularization of attentions using protein sequence-predicted solvent exposure and residue-residue contact maps; (2) supervision of attentions using known intermolecular contacts in training data; and (3) an intrinsically explainable architecture where atomic-level contacts or "relations" lead to molecular-level affinity prediction. The first two and all three advances result in DeepAffinity+ and DeepRelations, respectively. Our methods show generalizability in affinity prediction for molecules that are new and dissimilar to training examples. Moreover, they show superior interpretability compared to state-of-the-art interpretable methods: with similar or better affinity prediction, they boost the AUPRC of contact prediction by around 33-, 35-, 10-, and 9-fold for the default test, new-compound, new-protein, and both-new sets, respectively. We further demonstrate their potential utilities in contact-assisted docking, structure-free binding site prediction, and structure-activity relationship studies without docking. Our study represents the first model development and systematic model assessment dedicated to interpretable machine learning for structure-free compound-protein affinity prediction.

Dataset Information

Trilobite compound eyes with crystalline cones and rhabdoms show mandibulate affinities.

Publications

Trilobite compound eyes with crystalline cones and rhabdoms show mandibulate affinities.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets