Scholarly Works, Center for Soft Matter and Biological Physics

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Analytical interaction potential for Lennard-Jones rods
Wang, Junwen; Seidel, Gary D.; Cheng, Shengfeng (American Physical Society, 2025-01-03)
An analytical form has been derived using Ostrogradsky's integration method for the interaction between two thin rods of finite lengths in arbitrary relative configurations in a three-dimensional space, each treated as a line of point particles interacting through the Lennard-Jones 12-6 potential. Simplified analytical forms for coplanar, parallel, and collinear rods are also derived. Exact expressions for the force and torque between the rods are obtained. Similar results for a point particle interacting with a thin rod are provided. These interaction potentials can be widely used for analytical descriptions and computational modeling of systems involving rodlike objects such as liquid crystals, colloids, polymers, elongated viruses and bacteria, and filamentous materials including carbon nanotubes, nanowires, biological filaments, and their bundles.
Analytical sphere-thin rod interaction potential
Wang, Junwen; Cheng, Shengfeng (Springer, 2025-05)
A compact analytical form is derived through an integration approach for the interaction between a sphere and a thin rod of finite and infinite lengths, with each object treated as a continuous medium of material points interacting by the Lennard-Jones 12-6 potential and the total interaction potential as a summation of the pairwise potential between material points on the two objects. Expressions for the resultant force and torque are obtained. Various asymptotic limits of the analytical sphere–rod potential are discussed. The integrated potential is applied to investigate the adhesion between a sphere and a thin rod. When the rod is sufficiently long and the sphere sufficiently large, the equilibrium separation between the two (defined as the distance from the center of the sphere to the axis of the rod) is found to be well approximated as a+0.787σ, where a is the radius of the sphere and σ is the unit of length of the Lennard–Jones potential. Furthermore, the adhesion between the two is found to scale with a √a.
Effect of particle shape on stratification in drying films of binary colloidal mixtures
Liu, Binghan; Grest, Gary S.; Cheng, Shengfeng (AIP Publishing, 2025-07-21)
The role of particle shape in evaporation-induced auto-stratification in polydisperse colloidal suspensions is explored with molecular dynamics simulations of mixtures of spheres and aspherical particles. A unified framework based on the competition between diffusion and diffusiophoresis is proposed to understand the effects of shape and size dispersity. In general, particles diffusing more slowly (e.g., larger particles) tend to accumulate more strongly at the evaporation front. However, larger particles have larger surface areas and therefore greater diffusiophoretic mobility. Hence, they are more likely to be driven away from the evaporation front via diffusiophoresis. For a rapidly dried bidisperse suspension containing small and large spheres, the competition leads to “small-on-top” stratification. Here, we employ a computational model in which the diffusion coefficient is inversely proportional to particle mass. For a mixture of spheres and aspherical particles with similar mass, the diffusion contrast is reduced, and the spheres are always enriched at the evaporation front as they have the smallest surface area for a given mass and, therefore, the lowest diffusiophoretic mobility. For a mixture of solid and hollow spheres that have the same outer radius and thus the same surface area, the diffusiophoretic contrast is suppressed, and the system is dominated by diffusion. Consequently, the solid spheres, which have a larger mass and diffuse more slowly, accumulate on top of the hollow spheres. Finally, for a mixture of thin disks and long rods that differ significantly in shape but have similar mass and surface area, both diffusion and diffusiophoresis contrasts are suppressed, and the mixture does not stratify.
Analytical Interaction Potentials for Disks in Two Dimensions
Liu, Binghan; Wang, Junwen; Grest, Gary S.; Cheng, Shengfeng (2025-11-25)
Compact analytical forms are derived for the interactions involving thin disks in two dimensions using an integration approach. These include interactions between a disk and a material point, between two disks, and between a disk and a wall. Each object is treated as a continuous medium of materials points interacting by the Lennard-Jones 12-6 potential. By integrating this potential in a pairwise manner, expressions for the potentials and resultant forces between extended objects are obtained. All the results are validated with numerical integrations. The analytical potentials are implemented in LAMMPS and used to simulate two-dimensional suspension of disks with an explicit solvent modeled as a Lennard-Jones liquid. In monodisperse disk suspensions, a disorderto- order transition of disk packing is observed as the area fraction of disks is increased or as the solvent evaporates. In bidisperse disk suspensions being rapidly dried, stratification is found with the smaller disks enriched at the evaporation front. Such “small-on-top” stratification echoes the similar phenomenon occurring in three-dimensional polydisperse colloidal suspensions that undergo fast drying. These potentials can be applied to a wide range of two-dimensional systems involving disk-like objects.
Monte Carlo ray-tracing simulations for diffractive optics
Ellepola, Kalani H.; Rajapaksha, Tharindu D.; Remley, Emma E.; Nguyen, Minh L. P.; Macdonnell, Dave G.; Leckey, John P.; Nguyen, Vinh Q. (Optica Publishing Group, 2026-02-09)
Diffractive optic elements offer significant advantages in optical system design, enabling lightweight and compact architectures compared with conventional refractive and reflective components. However, accurately modeling wave-optical effects in such systems remains challenging because characteristic wavelengths of light are much smaller than the overall dimensions of typical optical assemblies. Conventional ray-tracing methods generally neglect these effects, while full-wave simulations become computationally prohibitive for large-scale systems. To overcome these limitations, we introduce a numerical implementation of the Monte Carlo ray-tracing approach based on the Huygens–Fresnel principle to predict key optical parameters, including focusing efficiency, focal spot size, and diffraction patterns with high fidelity. This approach is validated through systematic comparisons of dedicated experimental, theoretical, and numerical results, demonstrating accurate performance over a broad range of optical configurations. We further demonstrate that photon sieves incorporating large numbers of pinholes distributed across Fresnel zones can focus light into spots smaller than the smallest pinhole diameter while strongly suppressing higher diffractive orders and sidelobes. These results highlight the potential of the ray-tracing approach as a practical tool for both the design and optimization of next-generation diffractive optical elements.
Characterization and Modeling of Interfacial Photogating Effect in Graphene Field-Effect Transistor Photodetectors on Silicon
Howe, Leslie; Ellepola, Kalani H.; Jahan, Nusrat; Talbert, Brady; Li, James; Cooney, Michael P.; Nguyen, Vinh Q. (ACS, 2025-01)
Infrared photodetection of silicon is prevented by the bandgap energy at wavelengths longer than approximately 1100 nm (∼1.12 eV) at room temperature, while silicon is the most used in modern electronics. Of particular interest is the performance of silicon for photodetectors in the infrared region beyond the silicon bandgap. Here, we demonstrate graphene field-effect transistor photodetectors on silicon with high photoconductive gain and photodetection capability extending to the infrared region. These devices have a photoresponsivity of >106 A/W for excitation above the silicon bandgap energy and yield a value of 35 A/W for infrared detection at a wavelength of 1530 nm. The high photosensitivity of the devices originates from the photogating effect in the nanostructures and a long Urbach tail extending into the infrared region. A model to explain the mechanism of the photoconductive gain is proposed, which shows that the gain results from modulation of the surface charge region under illumination. The gain strongly depends on the excitation power, due to carrier capture processes occurring over the barriers associated with the surface charge region, in agreement with the experimental data. This model properly explains the photoresponse behavior of graphene field-effect transistors on silicon.
Interfacial Photogating of Graphene Field-Effect Transistor for Photosensory Biomolecular Detection
Howe, Leslie; Wang, Yifei; Ellepola, Kalani H.; Ho, Vinh X.; Dohmen, Rosalie L.; Pinto, Marlo M.; Hoff, Wouter D.; Cooney, Michael P.; Vinh, Nguyen Q. (Wiley-VCH, 2025-05)
The photogating effect, induced by a light-driven gate voltage, modulates the potential energy of the active channel in field-effect transistors, leading to a high photoconductive gain of these devices. The effect is particularly pronounced in low-dimensional structures, especially in graphene field-effect transistors. Along with unusual optical and electrical properties, graphene with ultra-high carrier mobility and a highly sensitive surface generates a strong photogating effect in the structure, making it an excellent element for detecting light-sensitive biomolecules. In this work, graphene field-effect transistor biosensors is demonstrated for the rapid detection of photoactive yellow protein in an aqueous solution under optical illumination. The devices exhibit millisecond-scale response times and achieve a detection limit below 5.8 fM under blue-light excitation, consistent with the absorption characteristics of the protein. The photogating effect in graphene field-effect transistors provides a promising approach for developing high-performance, light-sensitive biosensors for biomolecular detection applications.
Deep learning reveals how cells pull, buckle, and navigate fibrous environments
Padhi, Abinash; Daw, Arka; Agashe, Atharva; Sawhney, Medha; Talukder, Maahi M.; Pour, Mehran M. H.; Jafari, Mohammad; Genin, Guy M.; Alisafaei, Farid; Kale, Sohan; Karpatne, Anuj; Nain, Amrinder (National Academy of Sciences, 2025-11-25)
Cells in tissues navigate fibrous environments fundamentally differently than they do on flat substrates, but the establishment of cell forces in physiological fibrous settings remains poorly understood. Although factors such as the stiffness of the extracellular matrix (ECM) are known to drive behaviors, including cell motility on flat nonfibrous substrates, the interplay between fiber architecture and stiffness in fibrous ECM is not known. Here, we find that in fibrous environments, the directionality of mechanical forces overrides ECM stiffness as the primary regulator of contractility in migrating cells. Using an approach combining phase microscopy with deep learning to map forces in real time, termed deep learning-enabled live-cell fiber-force microscopy (DLFM), we reveal that when cells transition between anisotropic and isotropic stress fields, their contractility significantly drops despite encountering stiffer ECM, contrary to the behavior of cells on flat nonfibrous substrates. Unlike the peripheral adhesions observed on flat nonfibrous substrates, cells in fibrous matrices form force-generating adhesions throughout their body, stabilized by out-of-plane mechanical components unique to fiber geometry. Cells exhibit distinct force signatures during migration, division, and differentiation, with temporal signatures that predict stem cell fate. These findings, enabled by combining deep learning and the mechanics of cells and fibers, explain long-standing paradoxical behavior of cells navigating deformable fibrous environments, how they can pull and tug at them, and identify tension anisotropy as a master regulator of cell behavior, with implications for cancer invasion, tissue engineering, and regenerative medicine.
Invading activity fronts stabilize excitable systems against stochastic extinction
Distefano, Kenneth; Shabani, Sara; Täuber, Uwe C. (2025-11-13)
Stochastic chemical reaction or population dynamics in finite systems often terminates in an absorbing state. Yet in large spatially extended systems, the time to reach species extinction (or fixation) becomes exceedingly long. Tuning control parameters may diminish the survival probability, rendering species coexistence susceptible to stochastic extinction events. In inhomogeneous settings, where a vulnerable subsystem is diffusively coupled to an adjacent stable patch, the former is reanimated through continuous influx from the interfaces, provided the absorbing region sustains spreading activity fronts. We demonstrate this generic elimination of finite-size extinction instabilities via immigration flux in predator-prey, epidemic spreading, and cyclic competition models.
Slow spatial migration can help eradicate cooperative antimicrobial resistance in time-varying environments
Hernández-Navarro, Lluís; Distefano, Kenneth; Täuber, Uwe C.; Mobilia, Mauro (2025-01-03)
Antimicrobial resistance (AMR) is a global threat and combating its spread is of paramount importance. AMR often results from a cooperative behaviour with shared protection against drugs. Microbial communities generally evolve in volatile environments and spatial structures. Migration, fluctuations, and environmental variability thus have significant impacts on AMR, whose maintenance in static environments is generally promoted by migration. Here, we demonstrate that this picture changes dramatically in time-fluctuating spatially structured environments. To this end, we consider a two-dimensional metapopulation model consisting of demes in which drug-resistant and sensitive cells evolve in a time-changing environment in the presence of a toxin against which protection can be shared. Cells migrate between neighbouring demes and hence connect them. When the environment varies neither too quickly nor too slowly, the dynamics is characterised by bottlenecks causing fluctuation-driven local extinctions, a mechanism countered by migration that rescues AMR. Through simulations and mathematical analysis, we investigate how migration and environmental variability influence the probability of resistance eradication. We determine the near-optimal conditions for the fluctuation-driven AMR eradication, and show that slow but nonzero migration speeds up the clearance of resistance and can enhance its eradication probability. We discuss our study’s impact on laboratory-controlled experiments.
Fixation and extinction in time-fluctuating spatially structured metapopulations
Asker, Matthew; Swailem, Mohamed; Täuber, Uwe C.; Mobilia, Mauro (American Physical Society, 2025-11-21)
Bacteria evolve in volatile environments and complex spatial structures. Migration, fluctuations, and environmental variability therefore have a significant impact on the evolution of microbial populations. Here, we consider a class of spatially explicit metapopulation models arranged as regular (circulation) graphs where wild-type and mutant cells compete in a time-fluctuating environment in which demes (subpopulations) are connected by slow cell migration. The carrying capacity is the same at each deme and endlessly switches between two values associated with harsh and mild environmental conditions. It is known that environmental variability can lead to population bottlenecks, following which the population is prone to fluctuation-induced extinction. Here, we analyze how slow migration, spatial structure, and fluctuations affect the phenomena of fixation and extinction on clique, cycle, and square lattice metapopulations. When the carrying capacity remains large, bottlenecks are weak and deme extinction can be ignored. The dynamics is thus captured by a coarse-grained description within which the probability and mean time of fixation are obtained analytically. This allows us to show that, in contrast to what happens in static environments, the mutant fixation probability depends on the rate of migration. We also show that the fixation probability and mean fixation time can exhibit a nonmonotonic dependence on the switching rate. When the carrying capacity is small under harsh conditions, bottlenecks are strong, and the metapopulation evolution is shaped by the coupling of deme extinction and strain competition. This yields rich dynamical scenarios, among which we identify the best conditions to eradicate mutants without dooming the metapopulation to extinction. We offer an interpretation of these findings in the context of an idealized treatment strategy and discuss possible generalizations of our models.
Agent-based Monte Carlo simulations for reaction-diffusion models, population dynamics, and epidemic spreading
Swailem, Mohamed; Dobramysl, Ulrich; Mukhamadiarov, Ruslan I.; Täuber, Uwe C. (AIP Publishing, 2025-08)
We provide an overview of Monte Carlo algorithms based on Markovian stochastic dynamics of interacting and reacting many-particle systems not in thermal equilibrium. These agent-based simulations are an effective way of introducing students to current research without requiring much prior knowledge or experience. By starting from the direct visualization of the data, students can gain immediate insight into emerging macroscopic features of a complex system and subsequently apply more sophisticated data analysis to quantitatively characterize its rich dynamical properties, both in the stationary and transient regimes. We utilize simulations of reaction–diffusion systems, stochastic models for population dynamics and epidemic spreading, to exemplify how interdisciplinary computational research can be effectively utilized in bottom-up undergraduate and graduate education through learning by doing. We also give helpful hints for the practical implementation of Monte Carlo algorithms, provide sample codes, explain some typical data analysis tools, and describe various potential error sources, pitfalls, and tips for avoiding them.
Inclusion of Water Multipoles into the Implicit Solvation Framework Leads to Accuracy Gains
Tolokh, Igor S.; Folescu, Dan E.; Onufriev, Alexey V. (American Chemical Society, 2024-06-11)
The current practical "workhorses" of the atomistic implicit solvation-the Poisson-Boltzmann (PB) and generalized Born (GB) models-face fundamental accuracy limitations. Here, we propose a computationally efficient implicit solvation framework, the Implicit Water Multipole GB (IWM-GB) model, that systematically incorporates the effects of multipole moments of water molecules in the first hydration shell of a solute, beyond the dipole water polarization already present at the PB/GB level. The framework explicitly accounts for coupling between polar and nonpolar contributions to the total solvation energy, which is missing from many implicit solvation models. An implementation of the framework, utilizing the GAFF force field and AM1-BCC atomic partial charges model, is parametrized and tested against the experimental hydration free energies of small molecules from the FreeSolv database. The resulting accuracy on the test set (RMSE similar to 0.9 kcal/mol) is 12% better than that of the explicit solvation (TIP3P) treatment, which is orders of magnitude slower. We also find that the coupling between polar and nonpolar parts of the solvation free energy is essential to ensuring that several features of the IWM-GB model are physically meaningful, including the sign of the nonpolar contributions.
Signals of detailed balance violation in nonequilibrium stationary states: subtle, manifest, and extraordinary
Zia, Royce K. P. (IOP Publishing, 2024-07-12)
The evolution of physical systems are often modeled by simple Markovian processes. When settled into stationary states, the probability distributions of such systems are time independent, by definition. However, they do not necessarily fall within the framework of equilibrium statistical mechanics. Instead, they may be non-equilibrium steady states (NESS). One distinguishing feature of NESS is the presence of time reversal asymmetry (TRA) and persistent probability current loops. These loops lead naturally to the notion of probability angular momenta, which play a role on the same footing as the noise covariance in stochastic processes. Illustrating with simulations of simple models and physical data, we present ways to detect these signals of TRA, from the subtle to the prominent.
Membrane Composition Modulates Vp54 Binding: A Combined Experimental and Computational Study
Guo, Wenhan; Dong, Rui; Okedigba, Ayoyinka O.; Sanchez, Jason E.; Agarkova, Irina V.; Abisamra, Elea-Maria; Jelinsky, Andrew; Riekhof, Wayne; Noor, Laila; Dunigan, David D.; Van Etten, James L.; Capelluto, Daniel G. S.; Xiao, Chuan; Li, Lin (MDPI, 2025-10-03)
The recruitment of peripheral membrane proteins is tightly regulated by membrane lipid composition and local electrostatic microenvironments. Our experimental observations revealed that Vp54, a viral matrix protein, exhibited preferential binding to lipid bilayers enriched in anionic lipids such as phosphatidylglycerol (PG) and phosphatidylserine (PS), compared to neutral phosphatidylcholine/phosphatidylethanolamine liposomes, and this occurred in a curvature-dependent manner. To elucidate the molecular basis of this selective interaction, we performed a series of computational analyses including helical wheel projection, electrostatic potential calculations, electric field lines simulations, and electrostatic force analysis. Our results showed that the membrane-proximal region of Vp54 adopted an amphipathic α-helical structure with a positively charged interface. In membranes containing PG or PS, electrostatic potentials at the interface were significantly more negative, enhancing attraction with Vp54. Field line and force analyses further confirmed that both the presence and spatial clustering of anionic lipids intensify membrane–Vp54 electrostatic interactions. These computational findings align with experimental binding data, jointly demonstrating that membrane lipid composition and organization critically modulate Vp54 recruitment. Together, our findings highlight the importance of electrostatic complementarity and membrane heterogeneity in peripheral protein targeting and provide a framework applicable to broader classes of membrane-binding proteins.
Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA
Kolesnikov, Egor S.; Xiong, Yeyue; Onufriev, Alexey V. (American Chemical Society, 2024-09-16)
The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex(3+)) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 k(B)T, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex(3+)) that was revealed by recent experiments.
Optimal Dielectric Boundary for Binding Free Energy Estimates in the Implicit Solvent
Forouzesh, Negin; Ghafouri, Fatemeh; Tolokh, Igor S.; Onufriev, Alexey V. (American Chemical Society, 2024-12-10)
Accuracy of binding free energy calculations utilizing implicit solvent models is critically affected by parameters of the underlying dielectric boundary, specifically, the atomic and water probe radii. Here, a multidimensional optimization pipeline is used to find optimal atomic radii, specifically for binding calculations in the implicit solvent. To reduce overfitting, the optimization target includes separate, weighted contributions from both binding and hydration free energies. The resulting five-parameter radii set, OPT_BIND5D, is evaluated against experiment for binding free energies of 20 host-guest (H-G) systems, unrelated to the types of structures used in the training. The resulting accuracy for this H-G test set (root mean square error of 2.03 kcal/mol, mean signed error of -0.13 kcal/mol, mean absolute error of 1.68 kcal/mol, and Pearson's correlation of r = 0.79 with the experimental values) is on par with what can be expected from the fixed charge explicit solvent models. Best agreement with the experiment is achieved when the implicit salt concentration is set equal or close to the experimental conditions.
Mechanistic modeling of mitosis: Insights from three collaborative case studies
Chen, Jing; Cimini, Daniela (Elsevier, 2025-11-01)
Mechanistic mathematical modeling has become an essential tool in modern biological research due to its powerful ability to integrate diverse data, generate hypotheses, and guide experimental design. It is particularly valuable for studying complex cellular mechanisms involving numerous interacting components. While the full dynamics of such systems usually elude direct experimental observation, modeling provides a means to integrate fragmented data with reasonable and/or informed assumptions into coherent mechanistic frameworks, simulate system behavior, and identify promising directions for further experimentation. When closely integrated with experiments, modeling can greatly accelerate progress in cell biology. However, the value of modeling is not automatic—it must be earned through careful model construction, critical interpretation of results, and thoughtful design of follow-up experiments. To demystify this process, we review three of our collaborative projects in mitosis, drawing on our experiences as a modeler and an experimentalist. We describe how the projects were initiated, why specific modeling approaches were chosen, how models were developed and refined, how model predictions guided new experiments, and how integrated modeling and experimentation led to deeper mechanistic insights. Finally, we emphasize that at the heart of every successful collaboration lies human connection. Productive cross-disciplinary communication is fundamental to bridging experimental and modeling perspectives and fully realizing the potential of integrative approaches in modern cell biology.
Cholesterol modulates membrane elasticity via unified biophysical laws
Kumarage, Teshani; Gupta, Sudipta; Morris, Nicholas B.; Doole, Fathima T.; Scott, Haden L.; Stingaciu, Laura-Roxana; Pingali, Sai Venkatesh; Katsaras, John; Khelashvili, George; Doktorova, Milka; Brown, Michael F.; Ashkar, Rana (Springer, 2025-07)
Cholesterol and lipid unsaturation underlie a balance of opposing forces that features prominently in adaptive cell responses to diet and environmental cues. These competing factors have resulted in contradictory observations of membrane elasticity across different measurement scales, requiring chemical specificity to explain incompatible structural and elastic effects. Here, we demonstrate that - unlike macroscopic observations - lipid membranes exhibit a unified elastic behavior in the mesoscopic regime between molecular and macroscopic dimensions. Using nuclear spin techniques and computational analysis, we find that mesoscopic bending moduli follow a universal dependence on the lipid packing density regardless of cholesterol content, lipid unsaturation, or temperature. Our observations reveal that compositional complexity can be explained by simple biophysical laws that directly map membrane elasticity to molecular packing associated with biological function, curvature transformations, and protein interactions. The obtained scaling laws closely align with theoretical predictions based on conformational chain entropy and elastic stress fields. These findings provide unique insights into the membrane design rules optimized by nature and unlock predictive capabilities for guiding the functional performance of lipid-based materials in synthetic biology and real-world applications.
Soybean Lectin Cross-Links Membranes by Binding Sulfatide in a Curvature-Dependent Manner
Okedigba, Ayoyinka O.; Ng, Emery L.; Deegbey, Mawuli; Rosso, M. Luciana; Ngo, William; Xiao, Ruoshi; Huang, Haibo; Zhang, Bo; Welborn, Valerie Vaissier; Capelluto, Daniel G. S. (American Chemical Society, 2025-05-24)
Soybean (Glycine max) is a key source of plant-based protein, yet its nutritional value is impacted by antinutritional factors, including lectins. Whereas soybean lectin is known to bind N-acetyl-d-galactosamine (GalNAc), its lipid interactions remain unexplored. Using a novel purification method, we isolated lectin from soybean meals and characterized its interactions with GalNAc and the glycosphingolipid sulfatide. Isothermal titration calorimetry revealed micromolar affinity for GalNAc, whereas most GalNAc derivatives displayed weak or no binding. Lectin exhibited high-affinity binding to sulfatide in a membrane curvature-dependent manner. Binding of lectin to sulfatide promoted cross-linking of sulfatide-containing vesicles. Whereas sulfatide interaction was independent of GalNAc binding, suggesting distinct binding sites, vesicle cross-linking was inhibited by the sugar. Molecular dynamics simulations identified a consensus sulfatide-binding site in lectin. These findings highlight the dual ligand-binding properties of soybean lectin and may provide strategies to mitigate its antinutritional effects and improve soybean meal processing.

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