Biology subjects

Vasilenko, E. O.

Publications and source records attributed to Vasilenko, E. O..

3 recordsLinked to original sources

bioRxiv2026

Blockiness as Determinant of Liquid Phase Formation by Polypeptide-Like Polymers in Stickers-Spacers Paradigm

Liquid-liquid phase separation (LLPS) driven by intrinsically disordered protein regions underlies the formation of membraneless organelles. The role of these structures in cell biology is an active area of research, yet their physical understanding remains less developed than that of protein assemblies with well-defined, rigid tertiary structures. The amino acid composition of disordered regions, particularly the abundance of charged residues, is known to modulate phase behavior. These factors govern accessible conformations, the assembly of compact sticker clusters, and the porosity of the resulting polymer networks. In contrast, the spatial distribution of side-chain volumes remains a comparatively understudied sequence determinant. Here, we employ a model that captures the patterning of side-chain volumes and interaction strengths without specifying chemical identity to show how sequence blockiness modulates the phase-nucleation propensity, connectivity, and compactness of polypeptide-like polymer assemblies. Using a library of polymers with identical composition but permuted residue arrangements, we show that clustering of bulky residues generates cooperative sticker domains and identify the threshold blockiness required to stabilize polymer networks. Coarse-grained simulations and mean-field theory reveal that this effect arises from an interplay among steric repulsion, solvent entropy, and higher-valency interactions, whereby blocky patterns promote the formation of multi-node percolated networks. Segregation of stickers and spacers is therefore one of the sequence parameters governing LLPS which may be useful for interpreting the evolution of disordered protein sequences, high-throughput proteome screening, and engineering of stimuli-responsive biomaterials. These rules may be exploited to predetermine condensate stability, viscoelasticity, and molecular exchange rates without altering chemical functionality.

biophysics↗

bioRxiv2025

Insights into structures of peptide aggregation nuclei from concentration dependence of lag time

Many peptides and proteins self-assemble into large fibrillar aggregates, reaching sizes of several micrometers. This process typically involves nucleation, formation of transient oligomeric species ranging from dimers to assemblies comprising hundreds of monomers. The roles of these heterogeneous oligomers in initiating fibril growth vary significantly, as only a subset converts into primary nuclei, the smallest assemblies capable of spontaneous elongation. Consequently, the initial stages of peptide aggregation remain a critical challenge in elucidating amyloid fibril formation. Here, we analyze the aggregation lag time as a function of initial peptide concentration, employing linear regression on a double logarithmic scale to derive the critical nucleus size from the slope. We compare potential shifts in kinetic regimes with critical concentrations associated with the assembly of larger oligomers. Although peptide length has no clear correlation with nucleus size, extended helical regions may promote the formation of larger nuclei, stabilizing preliminary oligomers and prolonging the lag phase. We quantified the contributions of higher-order oligomers as on- and off-pathway species across multiple peptides, identifying a common critical micelle concentration range. We prove that linear growth models can not capture the weak concentration dependence of lag times for several peptides. We demonstrate that the inclusion of capping and fragmentation mechanisms substantially improves the plausibility of the model. Knowledge of nucleus size facilitates molecular dynamics simulations to capture transitions to fibril-prone conformations. The insights advance our understanding of amyloid nucleation, identifying toxic aggregates in human neuroglial cells, and research on drug safety and biotechnological applications.

biophysics↗

bioRxiv2024

Computational model of primitive nervous system controlling chemotaxis in early multicellular heterotrophs

AO_SCPLOWBSTRACTC_SCPLOWThis paper presents a model to study a hypothetical role of a simple nervous systems in chemotaxis in early multicellular heterotrophs. The model views the organism as a network of motor units connected by flexible fibers and driven by realistic neuron excitation functions. Through numerical simulations, we identified the parameters that maximize the survival time of the modeled organism, focusing on its ability to efficiently locate and consume food. This synchronization enhances the ability of the modeled organism to navigate toward food and avoid harmful conditions. The model is described using basic mechanical principles and highlights the relationship between motor activity and energy balance. Our results suggest that even early prototypes of neural networks might provide significant survival advantages by optimizing movement and energy use. This study offers insights into how the first primitive nervous systems might have functioned. By publishing the code used in the simulations, we hope to contribute to the toolkit of computational methods and models used for exploration of neural origin and evolution.

biophysics↗