Publications

Amphipathic helices (AHs) are secondary structures that can facilitate binding of proteins to the membrane by folding into a helix with hydrophobic and hydrophilic faces that interact with the same surfaces in the lipid membrane. Septins are cytoskeletal proteins that preferentially bind to domains of micron-scale curvature on the cell membrane. Studies have shown that AH domains in septin are essential for curvature sensing. We present the first computational study of septin AH interactions with lipid bilayers. Using all-atom simulations and metadynamics-enhanced sampling, we study the effect of charge distribution at the flanking ends of septin AH on the energy for helical folding and its consequences on the binding configuration and affinity to the membrane. This is relevant to septins, since the net positive charge on the flanking C-terminal amino acids is a conserved property across several organisms. Simulations revealed that the energy barrier for folding in the neutral-capped AH is much larger than the charge-capped AH, leading to a small fraction of AH folding and integration to the membrane compared to a significantly folded configuration in the bound charge-capped AH. These observations are consistent with the binding measurements of synthetic AH constructs with variable helicity to lipid vesicles. Additionally, we examined an extended AH sequence including eight amino acids upstream and downstream of the AH to mimic the native protein. Again, simulations and experiments show that the extended peptide, with a net positive charge at C-terminus, adopts a strong helical configuration in solution, giving rise to a higher membrane affinity. Altogether, these results identify the energy cost for folding of AHs as a regulator of AH binding configuration and affinity and provide a basic template for parameterizing AH-membrane interactions as a starting point for the future multiscale simulations for septin-membrane interactions.

Germline-soma segregation is crucial for fertility. Primordial germ cells (PGCs) arise early in development and are the very first cells to form in the Drosophila embryo. At the time of PGC formation, the embryo is a syncytium where nuclei divide within a common cytoplasm. Whereas invaginating plasma membrane furrows enclose nuclei to form somatic lineages during the 14th nuclear division cycle, PGCs emerge from the syncytium during the 9th division cycle in a mechanistically distinct process. PGC formation depends on maternally deposited germ granules localized at the embryo’s posterior pole. Germ granules trigger protrusion of membrane buds that enlarge to surround several nuclei that reach the posterior pole. Buds are remodeled to cells through mitotic division and constriction of the bud neck. Previous studies implicated F-actin,1 actin regulators,2,3 and contractile ring components4 in mitotic furrow formation, but what drives bud emergence and how germ granules provoke reshaping of the plasma membrane remain unknown. Here, we investigate the mechanism of germ-granule-induced bud formation. Treating the embryo as a pressurized elastic shell, we used mathematical modeling to examine possible mechanical mechanisms for local membrane protrusion. One mechanism, outward buckling produced by polymerization of a branched F-actin network, is supported by experimental data. Further, we show that germ granules modify membrane lipid composition, promoting local branched F-actin polymerization that initiates PGC formation. We propose that a mechanism for membrane lipid regulation of F-actin dynamics in migrating cells has been adapted for PGC formation in response to spatial cues provided by germ granules.

Despite the parallels between problems in computer vision and cryo-electron microscopy (cryo-EM), many state-of-the-art approaches from computer vision have yet to be adapted for cryo-EM. Within the computer-vision research community, implicits such as neural radiance fields (NeRFs) have enabled the detailed reconstruction of 3D objects from few images at different camera-viewing angles. While other neural implicits, specifically density fields, have been used to map conformational heterogeneity from noisy cryo-EM projection images, most approaches represent volume with an implicit function in Fourier space, which has disadvantages compared with solving the problem in real space, complicating, for instance, masking, constraining physics or geometry, and assessing local resolution. In this work, we build on a recent development in neural implicits, a multi-resolution hash-encoding framework called instant-NGP, that we use to represent the scalar volume directly in real space and apply it to the cryo-EM density-map reconstruction problem (InstaMap). We demonstrate that for both synthetic and real data, InstaMap for homogeneous reconstruction achieves higher resolution at shorter training stages than five other real-spaced representations. We propose a solution to noise overfitting, demonstrate that InstaMap is both lightweight and fast to train, implement masking from a user-provided input mask and extend it to molecular-shape heterogeneity via bending space using a per-image vector field.

Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) integrates statistical inference, physics-based simulators, and machine learning and is emerging as a powerful framework for analysing complex experimental data. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep-learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force-spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.

Cryo-electron microscopy (cryo-EM) experiments take 2D snapshots of individual proteins. In principle, these snapshots contain not only the main biomolecular conformations but also scarcely populated states and rare transitions between intermediates. This makes cryo-EM a powerful tool, not only for investigating the structure of biomolecules at high resolution but also for inferring the entire conformational ensemble distribution. Some recent works have reported conformational state populations by counting particle-images from cryo-EM. We wish to caution the community that these measurements are highly susceptible to noise and should not be relied upon as a precise estimate of the thermodynamic landscape of a biomolecule for understanding its biological function. Here, we demonstrate that the extremely noisy nature of cryo-EM images and uncertainty in the viewing orientations of biomolecules lead to ambiguities when assigning images to structures. If ignored, this ambiguity can introduce inherent bias when determining the populations of conformational states through individual particle assignment. We further show that modeling the conformational probability distribution using the entire image dataset mitigates these biases

The genome contains genetic information essential for cell's life. The genome's spatial organization inside the cell nucleus is critical for its proper function including gene regulation. The two major genomic compartments -- euchromatin and heterochromatin -- contain largely transcriptionally active and silenced genes, respectively, and exhibit distinct dynamics. In this work, we present a hydrodynamic framework that describes the large-scale behavior of euchromatin and heterochromatin, and accounts for the interplay of mechanical forces, active processes, and nuclear confinement. Our model shows contractile stresses from cross-linking proteins lead to the formation of heterochromatin droplets via mechanically driven phase separation. These droplets grow, coalesce, and in nuclear confinement, wet the boundary. Active processes, such as gene transcription in euchromatin, introduce non-equilibrium fluctuations that drive long-range, coherent motions of chromatin as well as the nucleoplasm, and thus alter the genome's spatial organization. These fluctuations also indirectly deform heterochromatin droplets, by continuously changing their shape. Taken together, our findings reveal how active forces, mechanical stresses and hydrodynamic flows contribute to the genome's organization at large scales and provide a physical framework for understanding chromatin organization and dynamics in live cells.

Bacterial motility in spatially structured environments impacts a variety of natural and engineering processes. Constructing models to predict, control, and design bacterial motility for these processes remains challenging because bacteria and active swimmers have complex interactions with surfaces and because the precise environment geometry is unknown. Here, we present a method for deriving bacterial diffusion coefficients in disordered media in terms of cell and environmental parameters. The approach abstracts the dynamics in the full geometry to “surface states,” which encode how cells interact with surfaces in the environment. Then, a long-time diffusion equation can be derived analytically from the state model. Applying this method to a run-and-tumble particle in a 2D Lorentz gas environment provides analytical predictions that show good agreement with particle simulations. Like past studies, we observe that the diffusivity depends nonmonotonically on the cell’s run length. Using the analytical expressions, we derive the optimal run length, revealing an intuitive dependence on environmental length scales. Furthermore, we find that rescaling length and time by the average distance and time between trap events collapses all of the diffusivities onto a single curve, which we derive analytically. Thus, our approach extracts interpretable, macroscopic diffusive behavior from complex microscopic dynamics, and provides tools and intuitions for understanding bacterial diffusion in disordered media.

Simulating membrane proteins accurately combines two challenges into one: properly capturing the structure and dynamics of proteins as well as correctly representing the membrane environment in which they are usually embedded. Beginning with pioneering efforts in the 1980s and 1990s,1−7 both challenges have been met with increasing success over the years. Simulations of membrane proteins in realistic cellular contexts over many microseconds are now common.Concomitant advances in the determination of membrane protein structures, with over 50 unique structures determined 8 annually have further expanded the reach of simulations in this area. This Special Issue highlights a number of recent molecular dynamics (MD) simulations of membrane proteins and covers a wide range of applications and specialized techniques.

The flagellum is the most complex macromolecular structure known in bacteria and is composed of around two dozen distinct proteins. The main building block of the long, external flagellar filament, flagellin, is secreted through the flagellar type-III secretion system at a remarkable rate of several tens of thousands of amino acids per second, significantly surpassing the rates achieved by other pore-based protein secretion systems. The evolutionary implications and potential benefits of this high secretion rate for flagellum assembly and function, however, have remained elusive. In this study, we provide both experimental and theoretical evidence that the flagellar secretion rate has been evolutionarily optimized to facilitate rapid and efficient construction of a functional flagellum. By synchronizing flagellar assembly, we found that a minimal filament length of 2.5 μm was required for swimming motility. Biophysical modeling revealed that this minimal filament length threshold resulted from an elasto-hydrodynamic instability of the whole swimming cell, dependent on the filament length. Furthermore, we developed a stepwise filament labeling method combined with electron microscopy visualization to validate predicted flagellin secretion rates of up to 10,000 amino acids per second. A biophysical model of flagellum growth demonstrates that the observed high flagellin secretion rate efficiently balances filament elongation and energy consumption, thereby enabling motility in the shortest amount of time. Taken together, these insights underscore the evolutionary pressures that have shaped the development and optimization of the flagellum and type-III secretion system, illuminating the intricate interplay and cost-benefit tradeoff between functionality and efficiency in assembly of large macromolecular structures.

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce “PLUMED Tutorials,” a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.