Functional annotation of proteins for signaling network inference in non-model species

Abstract

Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

“…MiProGut provides an improved resource for protein sequence identification, but functional annotation is required to understand the role of these proteins, both in relation to the microbiome, and their impact on the host 78,79 . The majority (64.4%) of MiProGut proteins lacked informative functional annotation when annotated with eggNOG-mapper (Figure 2b).…”