Biology subjects

Gross, S. P.

Publications and source records attributed to Gross, S. P..

4 recordsLinked to original sources

bioRxiv2026

A Phosphorylation Switch Governs KIF11's Mechanical Output During Mitosis

The kinesin-5 motor protein KIF11 is crucial for mitotic spindle assembly, driving the separation of spindle poles through microtubule sliding. Src-family kinases phosphorylate KIF11 at multiple tyrosine residues within its motor domain, but the mechanistic consequences of these modifications remain unclear. Here, we dissect the role of phosphorylation at Y211 using phospho-mimetic (Y211E) and non-phosphorylatable mutants (Y211F) in biochemical, biophysical, and cellular assays. Optical trapping and Forster resonance energy transfer (FRET) analyses reveal that Y211 phosphorylation slows neck-linker docking, reducing motor velocity and force generation under load. In human cells, Y211E expression impairs bipolar spindle formation and decreases spindle pole separation velocity, while Y211F shortens steady-state spindle length. Fluorescence recovery after photobleaching (FRAP) analyses show that Y211E accelerates motor turnover on spindle microtubules, consistent with the mutant motors heightened load sensitivity. Together, these findings support a model in which Src-mediated phosphorylation at Y211 acts as a rheostat to tune KIF11 mechanochemistry and spindle assembly dynamics, linking cancer-relevant kinase signaling to mitotic force generation.

cell biology↗

bioRxiv2023

ADP release can explain spatially-dependent kinesin binding times

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. Significance StatementCytoskeletal-motor assemblies self-organize to achieve cellular functions ranging from delivering intracellular cargoes to generating forces in mitosis. Advancements in single-molecule experiments have revealed immense detail about motor detachment and stepping, but relatively little regarding the attachment process. With newly available spatially parameterized motor binding times from an optical trap, the evaluation of mechanistic models for binding becomes possible. We find that a model limited by both diffusive search and ADP-release best explains the data. The coupled chemo-mechanical nature of this interaction is more malleable than either separately, possibly explaining the rich diversity and regulation observed in cells. More broadly, our study provides a timely vignette on leveraging computations with experiments to understand how geometry and other complexities shape protein-protein interactions.

biophysics↗

bioRxiv2021

Deep Learning Assisted Mechanotyping of Individual Cells Through Repeated Deformations and Relaxations in Undulating Channels

Mechanical properties of cells are important features that are tightly regulated, and are dictated by various pathologies. Deformability cytometry allows for the characterization of mechanical properties of hundreds of cells per second, opening the way to differentiating cells via mechanotyping. A remaining challenge for detecting and classifying rare sub-populations is the creation of a combined experimental and analysis protocol that would assure classification accuracy approaching 100%. In order to maximize the accuracy, we designed a microfluidic channel that subjects each cell to repeated deformations and relaxations. We also track the shape dynamics of individual cells with high time resolution, and apply sequence-based deep learning models for feature extraction. HL60 cells with and without treatment with cytochalasin D (cytoD), a reagent previously shown to perturb the actin network, were used as a model system to understand the classification potential of our approach. Multiple recurrent and convolutional neural network architectures were trained using time sequences of cell shapes, and shown to achieve high classification accuracy based on cytoskeletal properties alone. The best model classified the two sub-populations of HL60 cells with an accuracy of 95%. This work establishes the application of sequence-based deep learning models to dynamic deformability cytometry.

biophysics↗

bioRxiv2020

Roles of motor on-rate and cargo mobility in intracellular transport

Molecular motors like kinesin are critical for cellular organization and biological function including in neurons. There is detailed understanding of how they move and how factors such as applied force and the presence of microtubule-associated proteins can alter this single-motor travel. In order to walk, the cargo-motor complex must first attach to a microtubule. This attachment process is less studied. Here, we use a combination of single-molecule bead experiments, modeling, and simulation to examine how cargos with kinesin-1 bind to microtubules. In experiment, we find that increasing cargo size and environment viscosity both signficantly slow cargo binding time. We use modeling and simulation to examine how the single motor on rate translates to the on rate of the cargo. Combining experiment and modeling allows us to estimate the single motor on rate as 100 s-1. This is a much higher value than previous estimates. We attribute the difference between our measurements and previous estimates to two factors: first, we are directly measuring initial motor attachment (as opposed to re-binding of a second motor) and second, the theoretical framework allows us to account for missed events (i.e. binding events not detected by the experiments due to their short duration). This indicates that the mobility of the cargo itself, determined by its size and interaction with the cytoplasmic environment, play a previously underestimated role in determining intracellular transport kinetics.

cell biology↗