arXiv subjects

Huan-Xiang Zhou

Publications and source records attributed to Huan-Xiang Zhou.

7 recordsLinked to original sources

arXiv2026

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets

Biomolecular condensates formed by phase separation often exhibit viscoelastic behavior, yet their shape recovery and fusion dynamics are frequently interpreted using purely viscous models. Here, we develop a unified theoretical and computational framework to quantify how viscoelasticity governs these two processes. We combine analytical theory for small-deformation shape recovery with axisymmetric finite-element simulations based on the Oldroyd-B constitutive model to systematically investigate both shape recovery and droplet fusion under comparable conditions. Our results show that, although both processes are driven by capillary forces, they are fundamentally distinct in their underlying physics. Shape recovery is governed by global viscocapillary relaxation of a single connected interface and follows single- or multi-exponential decay depending on the relative magnitude of the viscocapillary timescale and the stress relaxation time. In contrast, droplet fusion is intrinsically a multistage process involving localized curvature-driven neck formation, rapid bridge expansion, and a transition to global relaxation. We demonstrate that viscoelasticity introduces an additional intrinsic timescale that governs the competition between capillary driving and stress relaxation, characterized by the Deborah number. This leads to enhanced intermediate-stage fusion dynamics and modified relaxation behavior compared to Newtonian droplets. Furthermore, we show that the presence of an exterior fluid introduces additional hydrodynamic dissipation, significantly slowing the fusion process. Finally, we compare the computationally predicted droplet fusion in the Newtonian and viscoelastic cases with a stretched-exponential empirical formula. Deviations observed in viscoelastic regimes highlight the limitations of purely viscous descriptions and the need for models incorporating stress relaxation.

physics.flu-dyn↗

arXiv2026

Determinants of Phase-Separation Propensities, Material States, and Material Properties of Biomolecular Condensates

Phase separation of various materials has been studied for one and a half centuries. In the last two decades, phase separation of proteins and nucleic acids has received enormous attention, due its relevance to cellular functions. However, many of the observations on the resulting biomolecular condensates lack a theoretical underpinning. The first goal of this Account is to put forward theoretical frameworks for the phase-separation propensities, material states, and material properties of biomolecular condensates. Using these frameworks, I rationalize mechanistic interpretations from our recent experimental and computational studies, and synthesize these studies with prior literature to draw new conclusions. For phase-separation propensities, I relate the threshold (or saturation) concentration to the excess chemical potential in the dense phase, which in turn depends on intermolecular interaction strength and valency. For material states, I posit that liquid droplets form via complete phase separation, whereas amorphous dense liquids, reversible aggregates, and gels arise from premature termination of spinodal decomposition, due to overly weak or overly strong interactions or directional interactions. In particular, gels and aggregates are different forms of dynamically arrested states, with gels driven by tip growth via directional interactions whereas aggregates driven by monomer addition at interior sites to maximize valency. For material properties, I highlight the crucial roles of the stress relaxation time, which is determined by the mean lifetime of intermolecular bonds in a condensate. This relaxation time dictates how the condensate manifests viscoelasticity, including shear thickening and shear thinning, and accounts for the wide variation in zero-shear viscosity among different condensates.

cond-mat.soft↗

arXiv2026

Complex Effects of Salt on Small-Angle X-ray Scattering of BSA Originate From the Interplay of Ions and Hydration Water

Salts are an integral part of the environment for living systems and, therefore, understanding their effects on proteins and other biomolecules is of fundamental interest. Small-angle X-ray scattering (SAXS) of protein solutions can provide valuable information on salt effects, but extracting this information has been a significant challenge. For example, SAXS data of bovine serum albumin (BSA) at various salt concentrations were fit to three different spherical models. Here we combined the newly developed FMAPIq approach with explicit-solvent all-atom molecular dynamics simulations to show that the complex effects of salt on the SAXS of BSA originate from the interplay of ions and hydration water, leading to a general picture of protein-ion-water interactions.

q-bio.BM↗

arXiv2025

A Fixed-Volume Variant of Gibbs-Ensemble Monte Carlo Yields Significant Speedup in Binodal Calculation

Gibbs-ensemble Monte Carlo (GEMC) is a powerful method for calculating the gas-liquid binodals of simple models and small molecules, but is too demanding computationally for realistic models of proteins. Here we discover that the main reason for long simulations is that volume exchange is very slow to achieve, and develop a variant GEMC without volume exchange. The key is to determine an appropriate initial density. Test of this fixed-volume GEMC method on Lennard-Jones and patchy particles shows enormous speedup without any loss of accuracy in predicted binodals. The fast speed of fixed-volume GEMC promises many applications.

physics.chem-ph↗

arXiv2023

Power Law in a Bounded Range: Estimating the Lower and Upper Bounds from Sample Data

Power law distributions are widely observed in chemical physics, geophysics, biology, and beyond. The independent variable x of these distributions has an obligatory lower bound and in many cases also an upper bound. Estimating these bounds from sample data is notoriously difficult, with a recent method involving O(N^3) operations, where N denotes sample size. Here I develop an approach for estimating the lower and upper bounds that involves O(N) operations. The approach centers on calculating the mean values, x_min and x_max, of the smallest x and the largest x in N-point samples. A fit of x_min or x_max as a function of N yields the estimate for the lower or upper bound. Application to synthetic data demonstrates the accuracy and reliability of this approach.

physics.chem-ph↗

arXiv2017

Using the Fast Fourier Transform in Binding Free Energy Calculations

According to implicit ligand theory, the standard binding free energy is an exponential average of the binding potential of mean force (BPMF), an exponential average of the interaction energy between the ligand apo ensemble and a rigid receptor. Here, we use the Fast Fourier Transform (FFT) to efficiently estimate BPMFs by calculating interaction energies as rigid ligand configurations from the apo ensemble are discretely translated across rigid receptor conformations. Results for standard binding free energies between T4 lysozyme and 141 small organic molecules are in good agreement with previous alchemical calculations based on (1) a flexible complex (R ~ 0.9 for 24 systems) and (2) flexible ligand with multiple rigid receptor configurations (R ~ 0.8 for 141 systems). While the FFT is routinely used for molecular docking, to our knowledge this is the first time that the algorithm has been used for rigorous binding free energy calculations.

q-bio.BM↗

arXiv2005

Rugged Metropolis Sampling with Simultaneous Updating of Two Dynamical Variables

The Rugged Metropolis (RM) algorithm is a biased updating scheme, which aims at directly hitting the most likely configurations in a rugged free energy landscape. Details of the one-variable (RM$_1$) implementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables (RM$_2$). In a test with Met-Enkephalin in vacuum RM$_2$ improves conventional Metropolis simulations by a factor of about four. Correlations between three or more dihedral angles appear to prevent larger improvements at low temperatures. We also investigate a multi-hit Metropolis scheme, which spends more CPU time on variables with large autocorrelation times.

cond-mat.stat-mech↗