Publications

Lipid molecules such as cholesterol interact with the surface of integral membrane proteins (IMP) in a mode different from drug-like molecules in a protein binding pocket. These differences are due to the lipid molecule’s shape, the membrane’s hydrophobic environment, and the lipid’s orientation in the membrane. We can use the recent increase in experimental structures in complex with cholesterol to understand protein-cholesterol interactions. We developed the RosettaCholesterol protocol consisting of (1) a prediction phase using an energy grid to sample and score native-like binding poses and (2) a specificity filter to calculate the likelihood that a cholesterol interaction site may be specific. We used a multi-pronged benchmark (self-dock, flip-dock, cross-dock, and global-dock) of protein-cholesterol complexes to validate our method. RosettaCholesterol improved sampling and scoring of native poses over the standard RosettaLigand baseline method in 91% of cases and performs better regardless of benchmark complexity. On the β2AR, our method found one likely-specific site, which is described in the literature. The RosettaCholesterol protocol quantifies cholesterol binding site specificity. Our approach provides a starting point for high-throughput modeling and prediction of cholesterol binding sites for further experimental validation.

Transmembrane helix folding and self-association play important roles in biological signaling and transportation pathways across biomembranes. With molecular simulations, studies to explore the structural biochemistry of this process have been limited to focusing on individual fragments of this process – either helix formation or dimerization. While at an atomistic resolution, it can be prohibitive to access long spatio-temporal scales, at the coarse grained (CG) level, current methods either employ additional constraints to prevent spontaneous unfolding or have a low resolution on sidechain beads that restricts the study of dimer disruption caused by mutations. To address these research gaps, in this work, we apply our recent, in-house developed CG model (ProMPT) to study the folding and dimerization of Glycophorin A (GpA) and its mutants in the presence of Dodecyl-phosphocholine (DPC) micelles. Our results first validate the two-stage model that folding and dimerization are independent events for transmembrane helices and found a positive correlation between helix folding and DPC-peptide contacts. The wild type (WT) GpA is observed to be a right-handed dimer with specific GxxxG contacts, which agrees with experimental findings. Specific point mutations reveal several features responsible for the structural stability of GpA. While the T87L mutant forms anti-parallel dimers due to an absence of T87 interhelical hydrogen bonds, a slight loss in helicity and a hinge-like feature at the GxxxG region develops for the G79L mutant. We note that the local changes in the hydrophobic environment, affected by the point mutation, contribute to the development of this helical bend. This work presents a holistic overview of the structural stability of GpA in a micellar environment, while taking secondary structural fluctuations into account. Moreover, it presents opportunities for applications of computationally efficient CG models to study conformational alterations of transmembrane proteins that have physiological relevance.

Many active particles, such as swimming micro-organisms or motor proteins, do work on their environment by going though a periodic sequence of shapes. Interactions between particles can lead to synchronization of their duty cycles. Here, we study the collective dynamics of a suspension of active particles coupled through hydrodynamics. We find that at high enough density the system transitions to a state of collective motion by a mechanism that is distinct from other instabilities in active matter systems. Second, we demonstrate that the emergent nonequilibrium states feature stationary chimera patterns in which synchronized and phase-isotropic regions coexist. Third, we show that in confinement, oscillatory flows and robust unidirectional pumping states exist, and can be selected by choice of alignment boundary conditions. These results point toward a new route to collective motion and pattern formation and could guide the design of new active materials.

Fluid flow networks are ubiquitous and can be found in a broad range of contexts, from human-made systems such as water supply networks to living systems like animal and plant vasculature. In many cases, the elements forming these networks exhibit a highly non-linear pressure-flow relationship. Although we understand how these elements work individually, their collective behavior remains poorly understood. In this work, we combine experiments, theory, and numerical simulations to understand the main mechanisms underlying the collective behavior of soft flow networks with elements that exhibit negative differential resistance. Strikingly, our theoretical analysis and experiments reveal that a minimal network of nonlinear resistors, which we have termed a `fluidic memristor', displays history-dependent resistance. This new class of element can be understood as a collection of hysteresis loops that allows this fluidic system to store information. Our work provides insights that may inform new applications of fluid flow networks in soft materials science, biomedical settings, and soft robotics, and may also motivate new understanding of the flow networks involved in animal and plant physiology

Temperature is a major environmental variable influencing the distribution and behavior of plants. Recent advances have led to the identification of a role for the circadian clock in sensing temperature in Arabidopsis thaliana. Elongation growth and flowering are accelerated at warmer temperatures, and these effects are mediated by the circadian clock gene EARLY FLOWERING 3 (ELF3). ELF3 exists with a tripartite protein complex called the Evening Complex (EC) that functions as a DNA transcription repressor targeting growth-related genes. ELF3, a large scaffold protein with disordered domains, binds to the transcription factor LUX ARRYTHMO (LUX) and ELF4 to form the EC. A crucial feature of ELF3 is that it acts as a highly sensitive thermosensor that responds directly and rapidly to small increases of temperature of about 5 ºC and is rapidly reversible. At temperatures of about 22 ºC and below, the EC is active, binding and repressing the promoters of multiple growth promoting genes, reducing their expression and cell elongation. At around 27 ºC and above ELF3 undergoes rapid and reversible phase change and protein condensate formation. This temperature-dependent activity causes EC occupancy on target genes to decrease at 27 ºC, allowing their increased expression. A C-terminal prion-like domain (PrD) is sufficient for ELF3 phase change and temperature responsiveness. The PrD region contains a polyglutamine (polyQ) repeat of variable length, the size of which has been found to modulate the thermal responsiveness as measured by hypocotyl (stem) elongation and condensate formation. How the PrD is able to respond to temperature is however poorly understood. To understand the underlying biophysical basis for ELF3 thermal responsiveness, we use a polymer chain growth approach to build large ensembles and characterize monomeric ELF3-PrD at a range of polyQ lengths and temperatures. We then explore temperature-dependent dynamics of wild-type ELF3-PrD, ELF3-PrD with the variable polyQ tract removed, and a mutant (F527A) using chain growth structures as initial conformations for replica exchange (REST2) simulations. In addition to different mechanisms of temperature sensing with and without the variable polyQ tract, we find increased solvent accessibility of expanded polyQ tracts, promotion of temperature-sensitive helices adjacent to polyQ tracts, and exposure of a cluster of aromatic residues at increased temperature, all three of which promote inter-protein interaction. These results suggest a set of potential design principles for the engineering of temperature dependent molecular interactions. This has considerable potential for biotechnological application in medicine and agriculture.

Germline mutations upregulating RAS signaling are associated with multiple developmental disorders. A hallmark of these conditions is that the same mutation may present vastly different phenotypes in different individuals, even in monozygotic twins. Here, we demonstrate how the origins of such largely unexplained phenotypic variations may be dissected using highly controlled studies in Drosophila that have been gene edited to carry activating variants of MEK, a core enzyme in the RAS pathway. This allowed us to measure the small but consistent increase in signaling output of such alleles in vivo. The fraction of mutation carriers reaching adulthood was strongly reduced, but most surviving animals had normal RAS-dependent structures. We rationalize these results using a stochastic signaling model and support it by quantifying cell fate specification errors in bilaterally symmetric larval trachea, a RAS-dependent structure that allows us to isolate the effects of mutations from potential contributions of genetic modifiers and environmental differences. We propose that the small increase in signaling output shifts the distribution of phenotypes into a regime, where stochastic variation causes defects in some individuals, but not in others. Our findings shed light on phenotypic heterogeneity of developmental disorders caused by deregulated RAS signaling and offer a framework for investigating causal effects of other pathogenic alleles and mild mutations in general.

Positional information in developing tissues often takes the form of stripes of gene expression that mark the boundaries of a particular cell type or morphogenetic process. How stripes form is still in many cases poorly understood. Here we use optogenetics and live-cell biosensors to investigate one such pattern: the posterior stripe of brachyenteron (byn) expression in the early Drosophila embryo. This byn stripe depends on interpretation of an upstream signal – a gradient of ERK kinase activity – and the expression of two target genes tailless (tll) and huckebein (hkb) that exert antagonistic control over byn. We find that high or low doses of ERK signaling produce either transient or sustained byn expression, respectively. These ERK stimuli also regulate tll and hkb expression with distinct dynamics: tll transcription is rapidly induced under both low and high stimuli, whereas hkb transcription converts graded ERK inputs into an output switch with a variable time delay. Antagonistic regulatory paths acting on different timescales are hallmarks of an incoherent feedforward loop architecture, which is sufficient to explain transient or sustained byn dynamics and adds temporal complexity to the steady-state model of byn stripe formation. We further show that an all-or-none stimulus can be ‘blurred’ through intracellular diffusion to non-locally produce a stripe of byn gene expression. Overall, our study provides a blueprint for using optogenetic inputs to dissect developmental signal interpretation in space and time.

Mutations in signal transduction pathways lead to various diseases including cancers. MEK1 kinase, encoded by the human MAP2K1 gene, is one of the central components of the MAPK pathway and more than a hundred somatic mutations in MAP2K1 gene were identified in various tumors. Germline mutations deregulating MEK1 also lead to congenital abnormalities, such as the Cardiofaciocutaneous Syndrome and Arteriovenous Malformation. Evaluating variants associated with a disease is a challenge and computational genomic approaches aid in this process. Establishing evolutionary history of a gene improves computational prediction of disease-causing mutations; however, the evolutionary history of MEK1 is not well understood. Here, by revealing a precise evolutionary history of MEK1 we construct a well-defined dataset of MEK1 metazoan orthologs, which provides sufficient depth to distinguish between conserved and variable amino acid positions. We used this dataset to match known and predicted disease-causing and benign mutations to evolutionary changes observed in corresponding amino acid positions. We found that all known and the vast majority of suspected disease-causing mutations are evolutionarily intolerable. We selected several MEK1 mutations that cannot be unambiguously assessed by automated variant prediction tools, but that are confidently identified as evolutionary intolerant and thus “damaging” by our approach, for experimental validation in Drosophila. In all cases, evolutionary intolerant variants caused increased mortality and severe defects in fruit fly embryos confirming their damaging nature predicted by out computational strategy. We anticipate that our analysis will serve as a blueprint to help evaluate known and novel missense variants in MEK1 and that our approach will contribute to improving automated tools for disease-associated variant interpretation.

During cell division, the mitotic spindle forms inside cells and segregates chromosomes. The spindle's position sets the division plane, which is essential for proper growth and development. Force mechanisms regulating the position of the spindle are not yet understood. Here, we develop a coarse-grained model of spindles in cells, which accounts for microtubule dynamics, pulling forces from cortically bounded motor proteins, and fluid drag. We show that the spindle's resistance to rotation is largely driven by pulling forces from the motor proteins rather than the drag imposed by the cytoplasm. We also show that the arrangement of motor proteins affects the spindle's resistance to rotation for configurations where multiple motors stack at the same region, the spindle's resistance to rotation significantly reduces. Our findings are consistent with measurements in human tissue culture cells, where the spindle resistance to the rotation has been quantified.

The mechanical properties of a thin, planar material, perfused by an embedded flow network, have been suggested to be potentially changeable locally and globally by fluid transport and storage, which can result in both small- and large-scale deformations such as out-of-plane buckling. In these processes, fluid absorption and storage eventually cause the material to locally swell. Different parts can hydrate and swell unevenly, prompting a differential expansion of the surface. In order to computationally study the hydraulically induced differential swelling and buckling of such a membrane, we develop a network model that describes both the membrane shape and fluid movement, coupling mechanics with hydrodynamics. We simulate the time-dependent fluid distribution in the flow network based on a spatially explicit resistor network model with local fluid-storage capacitance. The shape of the surface is modeled by a spring network produced by a tethered mesh discretization, in which local bond rest lengths are adjusted instantaneously according to associated local fluid content in the capacitors in a quasistatic way. We investigate the effects of various designs of the flow network, including overall hydraulic traits (resistance and capacitance) and hierarchical architecture (arrangement of major and minor veins), on the specific dynamics of membrane shape transformation. To quantify these effects, we explore the correlation between local Gaussian curvature and relative stored fluid content in each hierarchy by using linear regression, which reveals that stronger correlations could be induced by less densely connected major veins. This flow-controlled mechanism of shape transformation was inspired by the blooming of flowers through the unfolding of petals. It can potentially offer insights for other reversible motions observed in plants induced by differential turgor and water transport through the xylem vessels, as well as engineering applications.