Daoutidis, P. | bioRxiv Science

bioRxiv2026

StrucNS reveals interaction-weighted network topology as the driving predictor of absolute stability of natural and de novo proteins

MotivationFolded protein function requires stability, yet mapping structure and sequence to a fitness landscape remains difficult. The protein fold is the physical realization of complex, spatially-sensitive physicochemical interactions among residues; quantitatively elucidating how these subtle relationships dictate thermodynamic stability remains challenging. We present StrucNS, a mathematical framework that identifies principles governing protein fitness by employing network science to learn physicochemical relationships between residues and their stability contributions directly from the protein fold. Representing the fold as a network topology, we utilize an inverse approach: while the fold is traditionally viewed as the phenotypic consequence of the underlying chemical forces, we use the topology to decode the very physicochemical dependencies that govern protein stability. Unlike protein language models reliant on high-dimensional evolutionary embeddings, StrucNS extracts these signals directly from the interaction-weighted network topology. Independence from evolutionary history uniquely suits StrucNS for de novo design prediction. ResultsDespite reduced dimensionality and training depth, StrucNS outperforms ESM-2 and ProteinMPNN on predicting mutational stability. StrucNS outperforms supervised UniRep in predicting absolute stability of de novo designs. Feature analysis reveals network topology as the key driver of predictive power, contributing 59% of model importance. SHAP analysis reveals two highly influential features as high degree and low modularity of polar/hydrophobic mixed subnetworks, which highlights the importance of connectivity between the hydrophobic core and protein surface to drive stability contrary to the conventional focus on the hydrophobic core. Revelation of predictive topological features underscores the utility of an interpretable model. AvailabilitySource codes are available: https://github.com/Hackel-Group-CEMS/StrucNS.

bioinformatics↗

bioRxiv2025

The Adolescent Functional Connectome is Dynamically Controlled by a Sparse Core of Cognitive and Topological Hubs

Fundamental mechanisms that control the brains ability to dynamically respond to cognitive demands are poorly understood, especially during periods of accelerated neural and cognitive maturation, such as adolescence. Using a sparsity-promoting feedback control framework we investigated the controllability of the adolescence functional connectome. Critical feedback costs associated with a regions control action on itself and the rest of the brain were estimated using resting-state fMRI data from an early longitudinal sample in the Adolescent Brain Cognitive Development (ABCD) study (n = 1394; median (IQR) age = 10.1 (1.1) years at baseline and 12.1 (1.1) years at follow-up). A highly reproducible, core set of predominantly highly connected regions retained their control action over the connectome under high feedback costs. They included posterior visual areas, retrosplenial cortex, cuneus and precuneus, superior parietal lobule, temporal ventral cortex and dorsolateral and lateral prefrontal cortices, i.e., both developed and developing brain regions. These regions were central to the topological organization of the connectome, consistently engaged during spontaneous coordination of resting-state networks, and overlapped with cognitive and topological brain hubs that play ubiquitous roles in cognitive function and the organization of the connectome. Also, most received (integrated) and distributed approximately equal amounts of neural information. These regions control action was developmentally stable, i.e., critical feedback costs did not change significantly during puberty, suggesting that, despite ongoing maturation and topological changes in the adolescent brain, fundamental mechanisms of system controllability may be well developed to facilitate information processing and response to cognitive demands.

neuroscience↗