Collective phase transitions in confined fish schools.

The collective patterns that emerge in schooling fish are often analyzed using models of self-propelled particles in unbounded domains. However, while schooling fish in both field and laboratory settings interact with domain boundaries, these effects are typically ignored. Here, we propose a model that incorporates geometric confinement, by accounting for both flow and wall interactions, into existing data-driven behavioral rules. We show that new collective phases emerge where the school of fish "follows the tank wall" or "double mills." Importantly, confinement induces repeated switching between two collective states, schooling and milling. We describe the group dynamics probabilistically, uncovering bistable collective states along with unintuitive bifurcations driving phase transitions. Our findings support the hypothesis that collective transitions in fish schools could occur spontaneously, with no adjustment at the individual level, and opens venues to control and engineer emergent collective patterns in biological and synthetic systems that operate far from equilibrium.

Measuring and modeling the dynamics of mitotic error correction.

Error correction is central to many biological systems and is critical for protein function and cell health. During mitosis, error correction is required for the faithful inheritance of genetic material. When functioning properly, the mitotic spindle segregates an equal number of chromosomes to daughter cells with high fidelity. Over the course of spindle assembly, many initially erroneous attachments between kinetochores and microtubules are fixed through the process of error correction. Despite the importance of chromosome segregation errors in cancer and other diseases, there is a lack of methods to characterize the dynamics of error correction and how it can go wrong. Here, we present an experimental method and analysis framework to quantify chromosome segregation error correction in human tissue culture cells with live cell confocal imaging, timed premature anaphase, and automated counting of kinetochores after cell division. We find that errors decrease exponentially over time during spindle assembly. A coarse-grained model, in which errors are corrected in a chromosome-autonomous manner at a constant rate, can quantitatively explain both the measured error correction dynamics and the distribution of anaphase onset times. We further validated our model using perturbations that destabilized microtubules and changed the initial configuration of chromosomal attachments. Taken together, this work provides a quantitative framework for understanding the dynamics of mitotic error correction.

HIV-1 capsid shape, orientation, and entropic elasticity regulate translocation into the nuclear pore complex.

Nuclear import and uncoating of the viral capsid are critical steps in the HIV-1 life cycle that serve to transport and release genomic material into the nucleus. Viral core import involves translocating the HIV-1 capsid at the nuclear pore complex (NPC). Notably, the central channel of the NPC appears to often accommodate and allow passage of intact HIV-1 capsid, though mechanistic details of the process remain to be fully understood. Here, we investigate the molecular interactions that operate in concert between the HIV-1 capsid and the NPC that regulate capsid translocation through the central channel. To this end, we develop a "bottom-up" coarse-grained (CG) model of the human NPC from recently released cryo-electron tomography structure and then construct composite membrane-embedded CG NPC models. We find that successful translocation from the cytoplasmic side to the NPC central channel is contingent on the compatibility of the capsid morphology and channel dimension and the proper orientation of the capsid approach to the channel from the cytoplasmic side. The translocation dynamics is driven by maximizing the contacts between phenylalanine-glycine nucleoporins at the central channel and the capsid. For the docked intact capsids, structural analysis reveals correlated striated patterns of lattice disorder likely related to the intrinsic capsid elasticity. Uncondensed genomic material inside the docked capsid augments the overall lattice disorder of the capsid. Our results suggest that the intrinsic "elasticity" can also aid the capsid to adapt to the stress and remain structurally intact during translocation.

3D feasibility of 2D RNA-RNA interaction paths by stepwise folding simulations.

The structure of an RNA, and even more so its interactions with other RNAs, provide valuable information about its function. Secondary structure-based tools for RNA-RNA interaction predictions provide a quick way to identify possible interaction targets and structures. However, these tools ignore the effect of steric hindrance on the tertiary (3D) structure level, and do not consider whether a suitable folding pathway exists to form the interaction. As a consequence, these tools often predict interactions that are unrealistically long and could be formed (in three dimensions) only by going through highly entangled intermediates. Here, we present a computational pipeline to assess whether a proposed secondary (2D) structure interaction is sterically feasible and reachable along a plausible folding pathway. To this end, we simulate the folding of a series of 3D structures along a given 2D folding path. To avoid the complexity of large-scale atomic resolution simulations, our pipeline uses coarse-grained 3D modeling and breaks up the folding path into small steps, each corresponding to the extension of the interaction by 1 or 2 bp. We apply our pipeline to analyze RNA-RNA interaction formation for three selected RNA-RNA complexes. We find that kissing hairpins, in contrast to interactions in the exterior loop, are difficult to extend and tend to get stuck at an interaction length of 6 bp. Our tool, including source code, documentation, and sample data, is available at www.github.com/irenekb/RRI-3D.

Elastic heterogeneity governs asymmetric adsorption-desorption in a soft porous crystal.

Metal-organic frameworks (MOFs), which possess a high degree of crystallinity and a large surface area with tunable inorganic nodes and organic linkers, exhibit high stimuli-responsiveness and molecular adsorption selectivity that enable various applications. The adsorption in MOFs changes the crystalline structure and elastic moduli. Thus, the coexistence of adsorbed/desorbed sites makes the host matrices elastically heterogeneous. However, the role of elastic heterogeneity in the adsorption-desorption transition has been overlooked. Here, we show the asymmetric role of elastic heterogeneity in the adsorption-desorption transition. We construct a minimal model incorporating adsorption-induced lattice expansion/contraction and an increase/decrease in the elastic moduli. We find that the transition is hindered by the entropic and energetic effects which become asymmetric in the adsorption process and desorption process, leading to the strong hysteretic nature of the transition. Furthermore, the adsorbed/desorbed sites exhibit spatially heterogeneous domain formation, implying that the domain morphology and interfacial area between adsorbed/desorbed sites can be controlled by elastic heterogeneity. Our results provide a theoretical guideline for designing soft porous crystals with tunable adsorption hysteresis and the dispersion and domain morphology of adsorbates using elastic heterogeneity.

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA.

Bacteriophage T7 gp4 helicase has served as a model system for understanding mechanisms of hexameric replicative helicase translocation. The mechanistic basis of how nucleoside 5'-triphosphate hydrolysis and translocation of gp4 helicase are coupled is not fully resolved. Here, we used a thermodynamically benchmarked coarse-grained protein force field, Associative memory, Water mediated, Structure and Energy Model (AWSEM), with the single-stranded DNA (ssDNA) force field 3SPN.2C to investigate gp4 translocation. We found that the adenosine 5'-triphosphate (ATP) at the subunit interface stabilizes the subunit-subunit interaction and inhibits subunit translocation. Hydrolysis of ATP to adenosine 5'-diphosphate enables the translocation of one subunit, and new ATP binding at the new subunit interface finalizes the subunit translocation. The LoopD2 and the N-terminal primase domain provide transient protein-protein and protein-DNA interactions that facilitate the large-scale subunit movement. The simulations of gp4 helicase both validate our coarse-grained protein-ssDNA force field and elucidate the molecular basis of replicative helicase translocation.

Driving force and pathway in polyelectrolyte complex coacervation.

There is notable discrepancy between experiments and coarse-grained model studies regarding the thermodynamic driving force in polyelectrolyte complex coacervation: experiments find the free energy change to be dominated by entropy, while simulations using coarse-grained models with implicit solvent usually report a large, even dominant energetic contribution in systems with weak to intermediate electrostatic strength. Here, using coarse-grained, implicit-solvent molecular dynamics simulation combined with thermodynamic analysis, we study the potential of mean force (PMF) in the two key stages on the coacervation pathway for symmetric polyelectrolyte mixtures: polycation-polyanion complexation and polyion pair-pair condensation. We show that the temperature dependence in the dielectric constant of water gives rise to a substantial entropic contribution in the electrostatic interaction. By accounting for this electrostatic entropy, which is due to solvent reorganization, we find that under common conditions (monovalent ions, room temperature) for aqueous systems, both stages are strongly entropy-driven with negligible or even unfavorable energetic contributions, consistent with experimental results. Furthermore, for weak to intermediate electrostatic strengths, this electrostatic entropy, rather than the counterion-release entropy, is the primary entropy contribution. From the calculated PMF, we find that the supernatant phase consists predominantly of polyion pairs with vanishingly small concentration of bare polyelectrolytes, and we provide an estimate of the spinodal of the supernatant phase. Finally, we show that prior to contact, two neutral polyion pairs weakly attract each other by mutually induced polarization, providing the initial driving force for the fusion of the pairs.

Functional enzyme-polymer complexes.

SignificanceThe use of biological enzyme catalysts could have huge ramifications for chemical industries. However, these enzymes are often inactive in nonbiological conditions, such as high temperatures, present in industrial settings. Here, we show that the enzyme PETase (polyethylene terephthalate [PET]), with potential application in plastic recycling, is stabilized at elevated temperature through complexation with random copolymers. We demonstrate this through simulations and experiments on different types of substrates. Our simulations also provide strategies for designing more enzymatically active complexes by altering polymer composition and enzyme charge distribution.

Likelihood-based non-Markovian models from molecular dynamics.

SignificanceThe analysis of complex systems with many degrees of freedom generally involves the definition of low-dimensional collective variables more amenable to physical understanding. Their dynamics can be modeled by generalized Langevin equations, whose coefficients have to be estimated from simulations of the initial high-dimensional system. These equations feature a memory kernel describing the mutual influence of the low-dimensional variables and their environment. We introduce and implement an approach where the generalized Langevin equation is designed to maximize the statistical likelihood of the observed data. This provides an efficient way to generate reduced models to study dynamical properties of complex processes such as chemical reactions in solution, conformational changes in biomolecules, or phase transitions in condensed matter systems.

Differential interactions of resting, activated, and desensitized states of the α7 nicotinic acetylcholine receptor with lipidic modulators.

The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion channel that modulates neuronal excitability, largely by allowing Ca2+ permeation. Agonist binding promotes transition from a resting state to an activated state, and then rapidly to a desensitized state. Recently, cryogenic electron microscopy (cryo-EM) structures of the human α7 receptor in nanodiscs were reported in multiple conformations. These were selectively stabilized by inhibitory, activating, or potentiating compounds. However, the functional annotation of these structures and their differential interactions with unresolved lipids and ligands remain incomplete. Here, we characterized their ion permeation, membrane interactions, and ligand binding using computational electrophysiology, free-energy calculations, and coarse-grained molecular dynamics. In contrast to nonconductive structures in apparent resting and desensitized states, the structure determined in the presence of the potentiator PNU-120596 was consistent with an activated state permeable to Ca2+. Transition to this state was associated with compression and rearrangement of the membrane, particularly in the vicinity of the peripheral MX helix. An intersubunit transmembrane site was implicated in selective binding of either PNU-120596 in the activated state or cholesterol in the desensitized state. This substantiates functional assignment of all three lipid-embedded α7-receptor structures with ion-permeation simulations. It also proposes testable models of their state-dependent interactions with lipophilic ligands, including a mechanism for allosteric modulation at the transmembrane subunit interface.

Simulation-Guided Rational Design of DNA Walker-Based Theranostic Platform.

Biomolecule-functionalized nanoparticles represent a type of promising biomaterials in biomedical applications owing to their excellent biocompatibility and versatility. DNA-based reactions on nanoparticles have enabled emerging applications including intelligent biosensors, drug delivery, and biomimetic devices. Among the reactions, strand hybridization is the critical step to control the sensitivity and specificity of biosensing, and the efficiency of drug delivery. However, a comprehensive understanding of DNA hybridization on nanoparticles is still lacking, which may differ from the process in homogeneous solutions. To address this limitation, coarse-grained model-based molecular dynamic simulation is harnessed to disclose the critical factors involved in intermolecular hybridization. Based on simulation guidance, DNA walker-based smart theranostic platform (DWTP) based on "on-particle" hybridization is developed, showing excellent consistency with simulation. DWTP is successfully applied for highly sensitive miRNA 21 detection and tumor-specific miRNA 21 imaging, driven by tumor-endogenous APE 1 enzyme. It enables the precise release of antisense oligonucleotide triggered by tumor-endogenous dual-switch miRNA 21 and APE 1, facilitating effective gene silencing therapy with high biosafety. The simulation of "on-particle" DNA hybridization has improved the corresponding biosensing performance and the release efficiency of therapeutic agents, representing a conceptually new approach for DNA-based device design.

Ultrathin Flexible Silica Nanosheets with Surface Chemistry-Modulated Affinity to Mammalian Cells.

Flexibility of nanomaterials is challenging but worthy to tune for biomedical applications. Biocompatible silica nanomaterials are under extensive exploration but are rarely observed to exhibit flexibility despite the polymeric nature. Herein, a facile one-step route is reported to ultrathin flexible silica nanosheets (NSs), whose low thickness and high diameter-to-thickness ratio enables folding. Thickness and diameter can be readily tuned to enable controlled flexibility. Mechanism study reveals that beyond the commonly used surfactant, the "uncommon" one bearing two hydrophobic tails play a guiding role in producing sheeted/layered/shelled structures, while addition of ethanol appropriately relieved the strong interfacial tension of the assembled surfactants, which will otherwise produce large curled sheeted structures. With these ultrathin NSs, it is further shown that the cellular preference for particle shape and rigidity is highly dependent on surface chemistry of nanoparticles: under high particle-cell affinity, NSs, and especially the flexible ones will be preferred by mammalian cells for internalization or attachment, while this preference is basically invalid when the affinity is low. Therefore, properties of the ultrathin silica NSs can be effectively expanded and empowered by surface chemistry to realize improved bio-sensing or drug delivery.

Assignment of a Physical Energy Scale for the Dimensionless Interaction Energies within the PRIME20 Peptide Model.

We present a calibration scheme to determine the conversion factors from a coarse-grained stochastic approximation Monte Carlo approach using the PRIME20 peptide interaction model into atomistic force-field interaction energies at full explicit aqueous solvation. The conversion from coarse-grained to atomistic structures was done according to our previously established inverse coarse-graining protocol. We provide a physical energy scale for both the backbone hydrogen bonding interactions and the sidechain interactions by correlating the dimensionless energy descriptors of the PRIME20 model with the energies averaged over molecular dynamics simulations. The conversion factor for these interactions turns out to be around 2kJ/mol for the backbone interactions, and zero for the sidechain interactions. We discuss these surprisingly small values in terms of their molecular interpretation.

Reverse Mapping of Coarse Grained Polyglutamine Conformations from PRIME20 Sampling.

An inverse coarse-graining protocol is presented for generating and validating atomistic structures of large (bio-) molecules from conformations obtained via a coarse-grained sampling method. Specifically, the protocol is implemented and tested based on the (coarse-grained) PRIME20 protein model (P20/SAMC), and the resulting all-atom conformations are simulated using conventional biomolecular force fields. The phase space sampling at the coarse-grained level is performed with a stochastical approximation Monte Carlo approach. The method is applied to a series of polypeptides, specifically dimers of polyglutamine with varying chain length in aqueous solution. The majority (>70 %) of the conformations obtained from the coarse-grained peptide model can successfully be mapped back to atomistic structures that remain conformationally stable during 10 ns of molecular dynamics simulations. This work can be seen as the first step towards the overarching goal of improving our understanding of protein aggregation phenomena through simulation methods.

Combining Scattering Experiments and Colloid Theory to Characterize Charge Effects in Concentrated Antibody Solutions.

Charges and their contribution to protein-protein interactions are essential for the key structural and dynamic properties of monoclonal antibody (mAb) solutions. In fact, they influence the apparent molecular weight, the static structure factor, the collective diffusion coefficient, or the relative viscosity, and their concentration dependence. Further, charges play an important role in the colloidal stability of mAbs. There exist standard experimental tools to characterize mAb net charges, such as the measurement of the electrophoretic mobility, the second virial coefficient, or the diffusion interaction parameter. However, the resulting values are difficult to directly relate to the actual overall net charge of the antibody and to theoretical predictions based on its known molecular structure. Here, we report the results of a systematic investigation of the solution properties of a charged IgG1 mAb as a function of concentration and ionic strength using a combination of electrophoretic measurements, static and dynamic light scattering, small-angle X-ray scattering, and tracer particle-based microrheology. We analyze and interpret the experimental results using established colloid theory and coarse-grained computer simulations. We discuss the potential and limits of colloidal models for the description of the interaction effects of charged mAbs, in particular pointing out the importance of incorporating shape and charge anisotropy when attempting to predict structural and dynamic solution properties at high concentrations.

Coarse-Grained Molecular Dynamics Simulation of Heterogeneous Polysorbate 80 Surfactants and their Interactions with Small Molecules and Proteins.

Polysorbate 80 (PS80) is widely used in pharmaceutical formulations, and its commercial grades exhibit certain levels of structural heterogeneity. The objective of this study was to apply coarse-grained molecular dynamics simulations to better understand the effect of PS80 heterogeneity on micelle self-assembly, the loading of hydrophobic small molecules into the micelle core, and the interactions between PS80 and a protein, bovine serum albumin (BSA). Four representative PS80 variants with different head and tail structures were studied. Our simulations found that PS80 structural heterogeneity could affect blank micelle properties such as solvent-accessible surface area, aggregation number, and micelle aspect ratio. It was also found that hydrophobic small molecules such as ethinyl estradiol preferentially partitioned into the PS80 micelle core and PS80 dioleates formed a more hydrophobic core compared to PS80 monooleates. Furthermore, multiple PS80 molecules could bind to BSA, and PS80 heterogeneity profoundly changed the binding ratio as well as the surfactant-protein contact area.

On the Structure, Stability, and Cell Uptake of Nanostructured Lipid Carriers for Drug Delivery.

The efficacy of nanostructured lipid carriers (NLC) for drug delivery strongly depends on their stability and cell uptake. Both properties are governed by their compositions and internal structure. To test the effect of the lipid composition of NLC on cell uptake and stability, three kinds of liquid lipids with different degrees of unsaturation are employed. After ensuring homogeneous size distributions, the thermodynamic characteristics, stability, and mixing properties of NLC are characterized. Then the rates and predominant pathways of cell uptake are determined. Although the same surfactant is used in all cases, different uptake rates are observed. This finding contradicts the view that the surface properties of NLC are dominated by the surfactant. Instead, the uptake rates are explained by the structure of the nanocarrier. Depending on the mixing properties, some liquid lipids remain inside the nanocarrier, while other liquid lipids are present on the surface. Nanocarriers with liquid lipids on the surface are taken up more readily by the cells. This shows that the engineering of efficient lipid nanocarriers requires a delicate balance of interactions between all components of the nanocarrier on the molecular level.

Geometrical multiscale tortuosity of desert ant walking trajectories.

Desert ants stand out as some of the most intriguing insect navigators, having captured the attention of scientists for decades. This includes the structure of walking trajectories during goal approach and search behaviour for the nest and familiar feeding sites. In the present study, we analysed such trajectories with regard to changes in walking direction. The directional change of the ants was quantified, i.e. an angle θ between trajectory increments of a given arclength λ was computed. This was done for different length scales λ, according to our goal of analysing desert ant path characteristics with respect to length scale. First, varying λ through more than two orders of magnitude demonstrated Brownian motion characteristics typical of the random walk component of search behaviour. Unexpectedly, this random walk component was also present in - supposedly rather linear - approach trajectories. Second, there were small but notable deviations from a uniform angle distribution that is characteristic of random walks. This was true for specific search situations, mostly close to the (virtual) goal position. And third, experience with a feeder position resulted in straighter approaches and more focused searches, which was also true for nest searches, albeit to a lesser extent. Taken together, these results both verify and extend previous studies on desert ant path characteristics. Of particular interest are the ubiquitous Brownian motion signatures and specific deviations thereof close to the goal position, indicative of unexpectedly structured search behaviour.

Geometrical multiscale tortuosity of desert ant walking trajectories.

Desert ants stand out as some of the most intriguing insect navigators, having captured the attention of scientists for decades. This includes the structure of walking trajectories during goal approach and search behaviour for the nest and familiar feeding sites. In the present study, we analysed such trajectories with regard to changes in walking direction. The directional change of the ants was quantified, i.e. an angle θ between trajectory increments of a given arclength λ was computed. This was done for different length scales λ, according to our goal of analysing desert ant path characteristics with respect to length scale. First, varying λ through more than two orders of magnitude demonstrated Brownian motion characteristics typical of the random walk component of search behaviour. Unexpectedly, this random walk component was also present in - supposedly rather linear - approach trajectories. Second, there were small but notable deviations from a uniform angle distribution that is characteristic of random walks. This was true for specific search situations, mostly close to the (virtual) goal position. And third, experience with a feeder position resulted in straighter approaches and more focused searches, which was also true for nest searches, albeit to a lesser extent. Taken together, these results both verify and extend previous studies on desert ant path characteristics. Of particular interest are the ubiquitous Brownian motion signatures and specific deviations thereof close to the goal position, indicative of unexpectedly structured search behaviour.