Francis Crick\'s global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations required from ideal beta barrel geometry to maintain extensive inter-strand hydrogen bonding without introducing considerable backbone strain. Instead, beta barrels and other protein folds have been designed guided by 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires considerable expert knowledge and provides only indirect control over the global barrel shape. Here we show that the simplicity and control over shape and structure provided by global parametric representations can be generalized beyond coiled coils by taking advantage of the rich sequence-structure relationships implicit in RoseTTAFold based inpainting and diffusion design methods. Starting from parametrically generated idealized barrel backbones, both RFjoint inpainting and RFdiffusion readily incorporate the backbone irregularities necessary for proper folding with minimal deviation from the idealized barrel geometries. We show that for beta barrels across a broad range of global beta sheet parameterizations, these methods achieve high in silico and experimental success rates, with atomic accuracy confirmed by an X-ray crystal structure of a novel beta barrel topology, and de novo designed 12, 14, and 16 stranded transmembrane nanopores with conductances ranging from 200 to 500 pS. By combining the simplicity and control of parametric generation with the high success rates of deep learning based protein design methods, our approach makes the design of proteins where global shape confers function, such as beta barrel nanopores, more precisely specifiable and accessible.

Biomolecular condensates arising from liquid-liquid phase separation contribute to diverse cellular processes, such as gene expression. Partitioning of client molecules into condensates is critical to regulating the composition and function of condensates. Previous studies suggest that client size limits partitioning, with dextrans >5 nm excluded from condensates. Here, we asked whether larger particles, such as macromolecular complexes, can partition into condensates based on particle-condensate interactions. We sought to discover the biophysical principles that govern particle inclusion in or exclusion from condensates using polymer nanoparticles with tailored surface chemistries as models of macromolecular complexes. Particles coated with polyethylene glycol (PEG) did not partition into condensates. We next leveraged the PEGylated particles as an inert platform to which we conjugated specific adhesive moieties. Particles functionalized with biotin partitioned into condensates containing streptavidin, driven by high-affinity biotin-streptavidin binding. Oligonucleotide-decorated particles exhibited varying degrees of partitioning into condensates, depending on condensate composition. Partitioning of oligonucleotide-coated particles was tuned by altering salt concentration, oligonucleotide length, and oligonucleotide surface density. Remarkably, beads with distinct surface chemistries partitioned orthogonally into immiscible condensates. Based on our experiments, we conclude that arbitrarily large particles can controllably partition into biomolecular condensates given sufficiently strong condensate-particle interactions, a conclusion also supported by our coarse-grained molecular dynamics simulations and theory. These findings may provide insights into how various cellular processes are achieved based on partitioning of large clients into biomolecular condensates, as well as offer design principles for the development of drug delivery systems that selectively target disease-related biomolecular condensates.

Despite recent experimental progress in characterizing cell migration mechanics, our understanding of the mechanisms governing rapid cell movement remains limited. To effectively limit tumor growth, antitumoral T cells need to rapidly migrate to find and kill cancer cells. To investigate the upper limits of cell speed, we developed a new hybrid stochastic-mean field model of bleb-based cell motility. We first examined the potential for adhesion-free bleb-based migration and show that cells migrate inefficiently in the absence of adhesion-based forces, i.e., cell swimming. While no cortical contractility oscillations are needed for cells to swim in viscoelastic media, high-to-low cortical contractility oscillations are necessary for cell swimming in viscous media. This involves a high cortical contractility phase with multiple bleb nucleation events, followed by an intracellular pressure buildup recovery phase at low cortical tensions, resulting in modest net cell motion. However, our model suggests that cells can employ a hybrid bleb- and adhesion-based migration mechanism for rapid cell motility and identifies conditions for optimality. The model provides a momentum-conserving mechanism underlying rapid single-cell migration and identifies factors as design criteria for engineering T cell therapies to improve movement in mechanically complex environments.

Familial dilated cardiomyopathy (DCM) is frequently caused by autosomal dominant point mutations in genes involved in diverse cellular processes, including sarcomeric contraction. While patient studies have defined the genetic landscape of DCM, genetics are not currently used in patient care, and patients receive similar treatments regardless of the underlying mutation. It has been suggested that a precision medicine approach based on the molecular mechanism of the underlying mutation could improve outcomes; however, realizing this approach has been challenging due to difficulties linking genotype and phenotype and then leveraging this information to identify therapeutic approaches. Here, we used multiscale experimental and computational approaches to test whether knowledge of molecular mechanism could be harnessed to connect genotype, phenotype, and drug response for a DCM mutation in troponin T, deletion of K210. Previously, we showed that at the molecular scale, the mutation reduces thin filament activation. Here, we used computational modeling of this molecular defect to predict that the mutant will reduce cellular and tissue contractility, and we validated this prediction in human cardiomyocytes and engineered heart tissues. We then used our knowledge of molecular mechanism to computationally model the effects of a small molecule that can activate the thin filament. We demonstrate experimentally that the modeling correctly predicts that the small molecule can partially rescue systolic dysfunction at the expense of diastolic function. Taken together, our results demonstrate how molecular mechanism can be harnessed to connect genotype and phenotype and inspire strategies to optimize mechanism-based therapeutics for DCM.

Skeletal muscle actin (ACTA1) mutations are a prevalent cause of skeletal myopathies consistent with ACTA1\'s high expression in skeletal muscle. Rare de novo mutations in ACTA1 associated with combined cardiac and skeletal myopathies have been reported, but ACTA1 represents only ~20% of the total actin pool in cardiomyocytes, making its role in cardiomyopathy controversial. Here we demonstrate how a mutation in an actin isoform expressed at low levels in cardiomyocytes can cause cardiomyopathy by focusing on a unique ACTA1 mutation, R256H. We previously identified this mutation in multiple family members with dilated cardiomyopathy (DCM), who had reduced systolic function without clinical skeletal myopathy. Using a battery of multiscale biophysical tools, we show that R256H has potent functional effects on ACTA1 function at the molecular scale and in human cardiomyocytes. Importantly, we demonstrate that R256H acts in a dominant manner, where the incorporation of small amounts of mutant protein into thin filaments is sufficient to disrupt molecular contractility, and that this effect is dependent on the presence of troponin and tropomyosin. To understand the structural basis of this change in regulation, we resolved a structure of R256H filaments using Cryo-EM, and we see alterations in actin\'s structure that have the potential to disrupt interactions with tropomyosin. Finally, we show that ACTA1R256H/+ human induced pluripotent stem cell cardiomyocytes demonstrate reduced contractility and sarcomeric disorganization. Taken together, we demonstrate that R256H has multiple effects on ACTA1 function that are sufficient to cause reduced contractility and establish a likely causative relationship between ACTA1 R256H and clinical cardiomyopathy.

Understanding the mechanisms of cellular aging processes is crucial for attempting to extend organismal lifespan and for studying age-related degenerative diseases. Yeast cells divide through budding, providing a classical biological model for studying cellular aging. With their powerful genetics, relatively short lifespan and well-established signaling pathways also found in animals, yeast cells offer valuable insights into the aging process. Recent experiments suggested the existence of two aging modes in yeast characterized by nucleolar and mitochondrial declines, respectively. In this study, by analyzing experimental data it was shown that cells evolving into those two aging modes behave differently when they are young. While buds grow linearly in both modes, cells that consistently generate spherical buds throughout their lifespan demonstrate greater efficacy in controlling bud size and growth rate at young ages. A three-dimensional chemical-mechanical model was developed and used to suggest and test hypothesized mechanisms of bud morphogenesis during aging. Experimentally calibrated simulations showed that tubular bud shape in one aging mode could be generated by locally inserting new materials at the bud tip guided by the polarized Cdc42 signal during the early stage of budding. Furthermore, the aspect ratio of the tubular bud could be stabilized during the late stage, as observed in experiments, through a reduction on the new cell surface material insertion or an expansion of the polarization site. Thus model simulations suggest the maintenance of new cell surface material insertion or chemical signal polarization could be weakened due to cellular aging in yeast and other cell types.

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is KV11.1 (hERG), comprising the primary cardiac repolarizing current, IKr. hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with myriad available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions aligning well with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as a novel method to predict discrete protein conformations and novel linkages from structure to function.

In mammalian cells, the cohesin protein complex is believed to translocate along chromatin during interphase to form dynamic loops through a process called active loop extrusion. Chromosome conformation capture and imaging experiments have suggested that chromatin adopts a compact structure with limited interpenetration between chromosomes and between chromosomal sections. We developed a theory demonstrating that active loop extrusion causes the apparent fractal dimension of chromatin to cross over between two and four at contour lengths on the order of 30 kilo-base pairs (kbp). The anomalously high fractal dimension D=4 is due to the inability of extruded loops to fully relax during active extrusion. Compaction on longer contour length scales extends within topologically associated domains (TADs), facilitating gene regulation by distal elements. Extrusion-induced compaction segregates TADs such that overlaps between TADs are reduced to less than 35% and increases the entanglement strand of chromatin by up to a factor of 50 to several Mega-base pairs. Furthermore, active loop extrusion couples cohesin motion to chromatin conformations formed by previously extruding cohesins and causes the mean square displacement of chromatin loci during lag times (Δt) longer than tens of minutes to be proportional to Δt1/3. We validate our results with hybrid molecular dynamics - Monte Carlo simulations and show that our theory is consistent with experimental data. This work provides a theoretical basis for the compact organization of interphase chromatin, explaining the physical reason for TAD segregation and suppression of chromatin entanglements which contribute to efficient gene regulation.

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5\' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically-determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus SARS-CoV-2, resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T\'s \"arms.\" Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4-6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across the studied human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9-8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4-9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities, with implications for potential protein-binding modes and therapeutic targets.

The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtubule track. Recent advances in optical tweezers provide binding times for several lengths of kinesin motors trapped at varying distances from a microtubule, empowering the investigation of competing models. We first explore a diffusion-limited model of binding. Through Brownian dynamics simulations and simulation-based inference, we find this simple diffusion model fails to explain the experimental binding times, but an extended model that accounts for the ADP state of the molecular motor agrees closely with the data, even under the scrutiny of penalizing for additional model complexity. We provide quantification of both kinetic rates and biophysical parameters underlying the proposed binding process. Our model suggests that most but not every motor binding event is limited by their ADP state. Lastly, we predict how these association rates can be modulated in distinct ways through variation of environmental concentrations and spatial distances.