SUMMARY Microtubule-targeting agents (MTAs) are widely used chemotherapy drugs capable of disrupting microtubule-dependent cellular functions, such as division and migration. We show that two clinically approved MTAs, paclitaxel and vinblastine, each suppress stiffness-sensitive migration and polarization characteristic of human glioma cells on compliant hydrogels. MTAs influence microtubule dynamics and cell traction forces by nearly opposite mechanisms, the latter of which can be explained by a combination of changes in myosin motor and adhesion clutch number. Our results support a microtubule-dependent signaling-based model for controlling traction forces through a motor-clutch mechanism, rather than microtubules directly relieving tension within F-actin and adhesions. Computational simulations of cell migration suggest that increasing protrusion number also impairs stiffness-sensitive migration, consistent with experimental MTA effects. These results provide a theoretical basis for the role of microtubules and mechanisms of MTAs in controlling cell migration.

Microtubules are multistranded polymers in eukaryotic cells that support key cellular functions such as chromosome segregation, motor-based cargo transport, and maintenance of cell polarity. Microtubules self-assemble via ''dynamic instability,'' in which the dynamic plus ends switch stochastically between alternating phases of polymerization and depolymerization. A key question in the field is what are the atomistic origins of this switching, i.e., what is different between the GTP-and GDP-tubulin states that enables microtubule growth and shortening, respectively? More generally, a major challenge in biology is how to connect theoretical frameworks across length-and timescales, from atoms to cellular behavior. In this study, we describe a multiscale model by linking atomistic molecular dynamics (MD), molecular Brownian dynamics (BD), and cellularlevel thermokinetic modeling of microtubules. Here, we investigated the underlying interaction energy when tubulin dimers associate laterally by performing all-atom MD simulations. We found that the lateral potential energy is not significantly different among three nucleotide states of tubulin, GTP, GDP, and GMPCPP and is estimated to be y À11 k B T. Furthermore, using MD potential energy in our BD simulations of tubulin dimers confirms that the lateral bond is weak on its own, with a mean lifetime of $0.1 ms, implying that the longitudinal bond is required for microtubule assembly. We conclude that nucleotide-dependent lateral-bond strength is not the key mediator microtubule dynamic instability, implying that GTP acts elsewhere to exert its stabilizing influence on microtubule polymer. Furthermore, the estimated lateral-bond strength (DG 0 lat y À5 k B T) is well-aligned with earlier estimates based on thermokinetic modeling and light microscopy measurements. Thus, we have computationally connected atomistic-level structural information, obtained by cryo-electron microscopy, to cellular-scale microtubule assembly dynamics using a combination of MD, BD, and thermokinetic models to bridge from Å ngstroms to micrometers and from femtoseconds to minutes.

Microtubule self-assembly dynamics serve to facilitate many vital cellular functions, such as chromosome segregation during mitosis and synaptic plasticity. However, the detailed atomistic basis of assembly dynamics has remained an unresolved puzzle. A key challenge is connecting together the vast range of relevant length-time scales, events happening at time scales ranging from nanoseconds, such as tubulin molecular interactions (Å-nm), to minutes-hours, such as the cellular response to microtubule dynamics during mitotic progression (μm). At the same time, microtubule interactions with associated proteins and binding agents, such as anti-cancer drugs, can strongly affect this dynamic process through atomic-level mechanisms that remain to be elucidated. New high-resolution technologies for investigating these interactions, including cryo-electron microscopy (EM) techniques and total internal reflection fluorescence (TIRF) microscopy, are yielding important new insights. Here, we focus on recent studies of microtubule dynamics, both theoretical and experimental, and how these findings shed new light on this complex phenomenon across length-time scales, from Å to μm and from nanoseconds to minutes.

Effective therapies for COVID-19 are urgently needed. Presently there are more than 800 COVID-19 clinical trials globally, many with drug combinations, resulting in an empirical process with an enormous number of possible combinations. To identify the most promising potential therapies, we developed a biophysical model for the SARS-CoV-2 viral cycle and performed a sensitivity analysis for individual model parameters and all possible pairwise parameter changes (16 2 = 256 possibilities). We found that model-predicted virion production is fairly insensitive to changes in most viral entry, assembly, and release parameters, but highly sensitive to some viral transcription and translation parameters. Furthermore, we found a cooperative benefit to pairwise targeting of transcription and translation, predicting that combined targeting of these processes will be especially effective in inhibiting viral production.

Microtubule “dynamic instability,” the abrupt switching from assembly to disassembly caused by the hydrolysis of GTP to GDP within the β subunit of the αβ-tubulin heterodimer, is necessary for vital cellular processes such as mitosis and migration. Despite existing high-resolution structural data, the key mechanochemical differences between the GTP and GDP states that mediate dynamic instability behavior remain unclear. Starting with a published atomic-level structure as an input, we used multiscale modeling to find that GTP hydrolysis results in both longitudinal bond weakening (~ 4 kBT) and an outward bending preference (~ 1.5 kBT) to both drive dynamic instability and give rise to the microtubule tip structures previously observed by light and electron microscopy. More generally, our study provides an example where atomic level structural information is used as the sole input to predict cellular level dynamics without parameter adjustment.