Turali-Emre, E. S. | Search bioRxiv

bioRxiv2026

MICAL-Inspired Chiral Vanadate Nanoclusters Decelerate Actin Dynamics in Live Cells

Actin turnover is a fundamental cellular process essential for cell dynamics, whose control is critical for both medicine and biotechnology. However, conventional small molecules modifying actin turnover scramble the structure of actin filaments and display high cellular toxicity. MICAL enzymes oxidizing methionine (Met) residues in actin can potentially address this challenge, but their large size and multiple required cofactors make MICALs manufacturing and utilization difficult. Here we show that redox-active chiral decavanadate nanoclusters with tartaric acid are capable of site-selective actin modulation, mimicking MICALs, while requiring no cofactors, displaying high biocompatibility and being membrane permeable. Decavanadate nanoclusters serve as atomically precise "nano-enzymes" oxidizing three Met residues in globular actin, including Met-176; the latter inhibits the opening of the backdoor segment and prevents depolymerization of actin filaments. The structure of actin filaments formed after nanocluster treatment revealed no structural disturbances as confirmed by cryo-electron microscopy. The biocompatibility and bioactivity of chiral decavanadate nanoclusters was demonstrated by modulation of actin in living NG108-15 cells. Taking advantage of atomically precise structure of the nanoclusters, we show that their docking into actin can be predicted computationally, indicating the possibility of programmable actin modulation using the tools of nanochemistry.

biochemistry↗

bioRxiv2020

Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

BackgroundDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein-protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein-protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy. ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

bioinformatics↗