Buckling transitions and soft-phase invasion of two-component icosahedral shells

Prediction of the Trimer Protein Interface Residue Pair by CNN-GRU Model Based on Multi-Feature Map

Most life activities of organisms are realized through protein-protein interactions, and these interactions are mainly achieved through residue-residue contact between monomer proteins. Consequently, studying residue-residue contact at the protein interaction interface can contribute to a deeper understanding of the protein-protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor (AAIPF(k)) to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein-protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein-protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue-residue contact at the trimer protein interface.

Critical adsorption of polyelectrolytes onto highly oppositely charged surfaces: Effects of charge renormalization

The critical adsorption conditions of polyelectrolytes (PEs) onto planar, cylindrical, and spherical surfaces were obtained by solving the Edwards equation using the Wentzel-Kramers-Brillouin (WKB) method. It demonstrated to provide a suitable analytical approach for all three geometries, in conformity with some experimental results for weakly charged micelles. However, our Monte Carlo simulations implementing approximate solutions of the nonlinear Poisson-Boltzmann equation for highly charged surfaces indicated recently the emergence of a limiting value of ionic strength due to a nonlinear dependence of the electrostatic (ES) potential on the surface-charge density σ. Beyond this limiting ionic strength, the PE adsorption no longer occurs, shifting the standard paradigm. In this work, we employ the concept of a renormalized charge and use the WKB method to study the effects of this nonlinearity on the critical adsorption conditions, density profile, and adsorbed layer of PE segments, all in comparison with the results of the linear Debye-Hückel (DH) approach. Charge renormalization makes it possible to use the known WKB solutions in the DH regime also for surfaces with high σ, introducing a saturation effect observed in the nonlinear case. The larger ES screening affects the density profile and the adsorbed layer of PEs, promoting a more dispersed distribution of PEs at higher surface-charge densities. Our analytical results for the critical adsorption curve reproduce the limiting ionic strength for high σ and also recover the DH regime at low σ.

Mechanical fatigue testing in silico: Dynamic evolution of material properties of nanoscale biological particles

Biological particles have evolved to possess mechanical characteristics necessary to carry out their functions. We developed a computational approach to "fatigue testing in silico", in which constant-amplitude cyclic loading is applied to a particle to explore its mechanobiology. We used this approach to describe dynamic evolution of nanomaterial properties and low-cycle fatigue in the thin spherical encapsulin shell, thick spherical Cowpea Chlorotic Mottle Virus (CCMV) capsid, and thick cylindrical microtubule (MT) fragment over 20 cycles of deformation. Changing structures and force-deformation curves enabled us to describe their damage-dependent biomechanics (strength, deformability, stiffness), thermodynamics (released and dissipated energies, enthalpy, and entropy) and material properties (toughness). Thick CCMV and MT particles experience material fatigue due to slow recovery and damage accumulation over 3-5 loading cycles; thin encapsulin shells show little fatigue due to rapid remodeling and limited damage. The results obtained challenge the existing paradigm: damage in biological particles is partially reversible owing to particle's partial recovery; fatigue crack may or may not grow with each loading cycle and may heal; and particles adapt to deformation amplitude and frequency to minimize the energy dissipated. Using crack size to quantitate damage is problematic as several cracks might form simultaneously in a particle. Dynamic evolution of strength, deformability, and stiffness, can be predicted by analyzing the cycle number (N) dependent damage, [Formula: see text] , where α is a power law and Nf is fatigue life. Fatigue testing in silico can now be used to explore damage-induced changes in the material properties of other biological particles. STATEMENT OF SIGNIFICANCE: Biological particles possess mechanical characteristics necessary to perform their functions. We developed "fatigue testing in silico" approach, which employes Langevin Dynamics simulations of constant-amplitude cyclic loading of nanoscale biological particles, to explore dynamic evolution of the mechanical, energetic, and material properties of the thin and thick spherical particles of encapsulin and Cowpea Chlorotic Mottle Virus, and the microtubule filament fragment. Our study of damage growth and fatigue development challenge the existing paradigm. Damage in biological particles is partially reversible as fatigue crack might heal with each loading cycle. Particles adapt to deformation amplitude and frequency to minimize energy dissipation. The evolution of strength, deformability, and stiffness, can be accurately predicted by analyzing the damage growth in particle structure.

Shape control of deformable charge-patterned nanoparticles

Deformable nanoparticles (NPs) offer unprecedented opportunities as dynamic building blocks that can spontaneously reconfigure during assembly in response to environmental cues. Designing reconfigurable materials based on deformable NPs hinges on an understanding of the shapes that can be engineered in these NPs. We solve for the low-energy shapes of charge-patterned deformable NPs by using molecular dynamics-based simulated annealing to minimize a coarse-grained model Hamiltonian characterized with NP elastic and electrostatic energies subject to a volume constraint. We show that deformable spherical NPs of radius 50 nm whose surface is tailored with octahedrally distributed charged patches and double-cap charged patches adapt their shape differently in response to changes in surface charge coverage and ionic strength. We find shape transitions to rounded octahedra, faceted octahedra, faceted bowls, oblate spheroids, spherocylinders, dented beans, and dimpled rounded bowls. We demonstrate that similar shape transitions can be achieved in deformable NPs of different sizes. The effects of counterion condensation on the free-energetic drive associated with the observed deformations are examined via Manning model calculations that utilize simulation-derived estimates for the NP Coulomb energy under salt-free conditions. The charge-pattern-based shape control of deformable NPs has implications for the design of responsive nanocontainers and for assembling reconfigurable materials whose functionality hinges on the shape-shifting properties of their nanoscale building blocks.

Effect of Mechanical Properties on Multicomponent Shell Patterning

Self-organized shells are fundamental in biological compartmentalization. They protect genomic material or enclose enzymes to aid the metabolic process. Studies of crystalline shells have shown the importance of the mechanical properties of building units in the shell morphology. However, the mechanism underlying the morphology of multicomponent assemblies is still poorly understood. Here, we analyze multicomponent closed shells that have different mechanical properties. By minimizing elastic energy, we show that heterogeneous bending rigidities regulate the surface pattern into circular, spikes, and ridge shapes. Interestingly, our continuum elasticity model recovers the patterns that have been proposed in bacterial microcompartments (BMCs), which are self-organized protein shells that aid the breakdown of complex molecules and allow bacteria to survive in hostile environments. In addition, our work elucidates the principles of pattern formation that can be used to design and engineer multicomponent microcompartments with a specific surface distribution of the components.

Computational and Experimental Approaches to Controlling Bacterial Microcompartment Assembly

Bacterial microcompartments compartmentalize the enzymes that aid chemical and energy production in many bacterial species. They are postulated to help bacteria survive in hostile environments. Metabolic engineers are interested in repurposing these organelles for non-native functions. Here, we use computational, theoretical, and experimental approaches to determine mechanisms that effectively control microcompartment self-assembly. We find, via multiscale modeling and mutagenesis studies, the interactions responsible for the binding of hexamer-forming proteins in a model system, the propanediol utilization bacterial microcompartments from Salmonella enterica serovar Typhimurium LT2. We determine how the changes in the microcompartment hexamer protein preferred angles and interaction strengths can modify the assembled morphologies. We demonstrate that such altered strengths and angles are achieved via amino acid mutations. A thermodynamic model provides guidelines to design microcompartments of various morphologies. These findings yield insight in controlled protein assembly and provide principles for assembling microcompartments for biochemical or energy applications as nanoreactors.