Sosale, M. | Search bioRxiv

bioRxiv2026

Studying the effect of conserved tyrosine phosphorylation within SH2 domains

SH2 domains are phosphotyrosine-binding modules that play a critical role in cell signaling by mediating protein-protein interactions. While tyrosine phosphorylation has been shown to impact SH2 domain function in signaling, the specific effects of phosphorylation at different sites within the domain remain poorly understood. In this study, we selected two conserved regions of tyrosine phosphorylation within SH2 domains, near conserved binding interface residues, and developed approaches to evaluate the impact of those sites on ligand binding. Using a modified dot blot assay to screen phosphomimic mutations, we studied specific tyrosine residues within the PTPN11-N, LYN, and SYK-C SH2 domains, finding that the PTPN11 N-terminal site (Y63) modulates the specificity, reducing binding of physiologically relevant substrates. Our findings provide new insights into the regulatory mechanisms governing SH2 domain function and highlight the importance of site-specific phosphorylation in modulating protein-protein interactions in cell signaling pathways.

molecular biology↗

bioRxiv2021

SongExplorer: A deep learning workflow for discovery and segmentation of animal acoustic communication signals

Many animals produce distinct sounds or substrate-borne vibrations, but these signals have proved challenging to segment with automated algorithms. We have developed SongExplorer, a web-browser based interface wrapped around a deep-learning algorithm that supports an interactive workflow for (1) discovery of animal sounds, (2) manual annotation, (3) supervised training of a deep convolutional neural network, and (4) automated segmentation of recordings. Raw data can be explored by simultaneously examining song events, both individually and in the context of the entire recording, watching synced video, and listening to song. We provide a simple way to visualize many song events from large datasets within an interactive low-dimensional visualization, which facilitates detection and correction of incorrectly labelled song events. The machine learning model we implemented displays higher accuracy than existing heuristic algorithms and similar accuracy as two expert human annotators. We show that SongExplorer allows rapid detection of all song types from new species and of novel song types in previously well-studied species.

animal behavior and cognition↗