AlphaFold predictions of fold-switched conformations are driven by structure memorization.

Recent work suggests that AlphaFold (AF)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics.

AlphaFold2 has more to learn about protein energy landscapes.

Recent work suggests that AlphaFold2 (AF2)-a deep learning-based model that can accurately infer protein structure from sequence-may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. Using several implementations of AF2, including two published enhanced sampling approaches, we generated >280,000 models of 93 fold-switching proteins whose experimentally determined conformations were likely in AF2's training set.

Disordered proteins mitigate the temperature dependence of site-specific binding free energies.

Biophysical characterization of protein-protein interactions involving disordered proteins is challenging. A common simplification is to measure the thermodynamics and kinetics of disordered site binding using peptides containing only the minimum residues necessary. We should not assume, however, that these few residues tell the whole story.

The cellular function of ROP GTPase prenylation is important for multicellularity in the moss Physcomitrium patens.

A complete picture of how signaling pathways lead to multicellularity is largely unknown. Previously, we generated mutations in a protein prenylation enzyme, GGB, and showed that it is essential for maintaining multicellularity in the moss Physcomitrium patens. Here, we show that ROP GTPases act as downstream factors that are prenylated by GGB and themselves play an important role in the multicellularity of P.

Development and optimization of an in vivo electrocardiogram recording method and analysis program for adult zebrafish.

Clinically pertinent electrocardiogram (ECG) data from model systems, such as zebrafish, are crucial for illuminating factors contributing to human cardiac electrophysiological abnormalities and disease. Current zebrafish ECG collection strategies have not adequately addressed the consistent acquisition of high-quality traces or sources of phenotypic variation that could obscure data interpretation. Thus, we developed a novel platform to ensure high-quality recording of in vivo subdermal adult zebrafish ECGs and zebrafish ECG reading GUI (zERG), a program to acquire measurements from traces that commercial software cannot examine owing to erroneous peak calling.