Biology subjects

Ying, Y. M.

Publications and source records attributed to Ying, Y. M..

3 recordsLinked to original sources

bioRxiv2026

Transforming macromolecular structures into simulations of self-assembly

Macromolecular self-assembly is a fundamental process in living and engineered systems, producing molecular machines like the ribosome or highly symmetric viral capsids. Thanks to sources like the Protein Data Bank (PDB) and AlphaFold3, the final target complexes are often known, but these static structures do not provide information on the self-assembly process directly. Computational models provide critical tools to study these essential pathways of self-assembly, but substantial coarse-graining of assembly subunits is necessary to achieve computational tractability of these relatively slow processes while retaining multi-valency. While rule-based or local interactions overcome the often-prohibitive enumeration of all possible assembly intermediates, they must ensure global structural constraints are met. We here demonstrate ioNERDSS, a user-friendly Python package that transforms 3D atomic structures into coarse-grained models for immediate simulation with the stochastic reaction-diffusion NERDSS software, converting static structures into time-resolved assembly trajectories. NERDSS uses rule-based interactions to simulate multi-component self-assembly at the minutes timescales and without limits to complex size or growth pathways. With ioNERDSS, each protein chain is defined by a rigid subunit with discrete interfaces and explicit orientational constraints that enforce a structured assembly. Repeated subunits (such as in viral capsids) are regularized to preserve the target topology across distinct stochastic assembly pathways, supporting assembly of structures with thousands of subunits. We initialize pairwise binding affinities using open-source machine-learned prediction tools, and our default coarse-grained (CG) models are all constrained by thermodynamic reversibility to reach an equilibrium steady-state. The binding rates and subunit abundances necessary to perform simulations are initialized at default values but represent the key variables (along with affinities) that cells and thus users would tune to control productive assembly. Benchmarking on over 40,000 PDB structures shows that the majority of CG models stochastically assemble into target structures. The ioNERDSS Python library links directly to open-source tools for visualization and analysis to facilitate fast and user-friendly structure validation and analysis of output for thermodynamic, kinetic, and nonequilibrium drivers of macromolecular self-assembly.

biophysics↗

bioRxiv2025

A membrane-driven biochemical oscillator tunable by the volume to surface area ratio

Oscillations are ubiquitous features of biological organisms, playing crucial roles in processes from circadian rhythms to developmental patterning. Protein-based biochemical oscillators have particular applications in synthetic biology because they can access fast and slow timescales that are independent from the transcription-translation machinery required of genetic oscillators. Here, we introduce and model such a mass-conserving biochemical oscillator using mass-action reaction kinetics that exploits dynamic changes to membrane phospholipid concentrations to drive proteins on and off the membrane in robust, tunable rhythms. Importantly, the oscillations rely on amplification of reactions on the membrane via dimensional reduction, and they are therefore tunable by variations in the volume-to-surface area ratio (V/A) of the system. With components inspired by the endocytic machinery, we show that a wide range of physiologically relevant biochemical rates can produce oscillations in part due to this independent geometric control. A broad computational screen of the high-dimensional parameter space reveals that oscillations require relatively strict enzyme kinetic design rules for low V/A but much more permissive kinetics for larger V/A. We validate that oscillations persist with more realistic reaction-diffusion simulations that captures explicit diffusion and stochastic, integer valued copy numbers, in overall good agreement with the period and amplitude of the deterministic oscillators. Because the oscillations rely on time-dependent changes to the surface properties and not post-translational modifications to the protein subunits, we demonstrate that it can be coupled to a self-assembling trimer, driving not only changes in localization but trimer yield. Our analysis establishes this membrane-localization oscillator as a new, geometry tunable and programmable timing module and suggests a potential for geometry sensing in engineered or cell-free systems.

biophysics↗

bioRxiv2024

Parallelization of particle-based reaction-diffusion simulations using MPI

Particle-based reaction-diffusion models offer a high-resolution alternative to the continuum reaction-diffusion approach, capturing the discrete and volume-excluding nature of molecules undergoing stochastic dynamics. These methods are thus uniquely capable of simulating explicit self-assembly of particles into higher-order structures like filaments, spherical cages, or heterogeneous macromolecular complexes, which are ubiquitous across living systems and in materials design. The disadvantage of these high-resolution methods is their increased computational cost. Here we present a parallel implementation of the particle-based NERDSS software using the Message Passing Interface (MPI) and spatial domain decomposition, achieving close to linear scaling for up to 96 processors in the largest simulation systems. The scalability of parallel NERDSS is evaluated for bimolecular reactions in 3D and 2D, for self-assembly of trimeric and hexameric complexes, and for protein lattice assembly from 3D to 2D, with all parallel test cases producing accurate solutions. We demonstrate how parallel efficiency depends on the system size, the reaction network, and the limiting timescales of the system, showing optimal scaling only for smaller assemblies with slower timescales. The formation of very large assemblies represents a challenge in evaluating reaction updates across processors, and here we restrict assembly sizes to below the spatial decomposition size. We provide the parallel NERDSS code open source, with detailed documentation for developers and extension to other particle-based reaction-diffusion software.

biophysics↗