Biology subjects

Sparks, S.

Publications and source records attributed to Sparks, S..

4 recordsLinked to original sources

bioRxiv2024

Bayesian Modelling Approaches for Breath-Hold Induced Cerebrovascular Reactivity

Cerebrovascular reactivity (CVR) reflects the ability of blood vessels to dilate and constrict in response to a vasoactive stimulus and is an important indicator of cerebrovascular health. CVR can be mapped non-invasively with functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) contrast in combination with a breath-hold (BH) task. There are several ways to analyse this type of data and retrieve individual CVR amplitude and timing information. The most common approach involves employing a time-shifted general linear model with the measured end-tidal carbon dioxide signal as a regressor of interest. In this work, we introduce a novel method for CVR mapping based on a variational Bayesian approach. We analysed BOLD fMRI data from six participants that performed a BH task in ten different sessions each, and computed the corresponding CVR amplitude and delay maps for each session/subject. No statistically significant differences were observed between the modelling approaches in the CVR delay and amplitude maps in grey matter. Notably, the largest difference between methods was apparent in the case of low CVR amplitude, attributed to how each method addressed noisy voxels, particularly in white matter and cerebral spinal fluid. Both approaches showed highly reproducible CVR amplitude maps where between-subject variability was significantly larger than between-session variability. Furthermore, our results illustrated that the Bayesian approach is more computationally efficient, and future implementations could incorporate more complex noise models, non-linear fitting, and physiologically meaningful information into the model in the form of priors. This work demonstrates the utility of variational Bayesian modelling for CVR mapping and highlights its potential for characterising BOLD fMRI dynamics in the study of cerebrovascular health and its application to clinical settings.

neuroscience↗

bioRxiv2024

Integrative spatiotemporal map of nucleocytoplasmic transport

Nuclear Pore Complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargos, including pre-ribosomes and viral capsids. It quantifies how flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by "fuzzy" multivalent interactions, redundant FG repeats, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

cell biology↗

bioRxiv2022

Design of a systemic small molecule clinical STING agonist using physics-based simulations and artificial intelligence

The protein STING (stimulator of interferon genes) is a central regulator of the innate immune system and plays an important role in antitumor immunity by inducing the production of cytokines such as type I interferon (IFN). Activation of STING stems from the selective recognition of endogenous cyclic dinucleotides (CDNs) by the large, polar, and flexible binding site, thus posing challenges to the design of small molecule agonists with drug-like physicochemical properties. In this work we present the design of SNX281, a small molecule STING agonist that functions through a unique self-dimerizing mechanism in the STING binding site, where the ligand dimer approximates the size and shape of a cyclic dinucleotide while maintaining drug-like small molecule properties. SNX281 exhibits systemic exposure, STING-mediated cytokine release, strong induction of type I IFN, potent in vivo antitumor activity, durable immune memory, and single-dose tumor elimination in mouse models via a Cmax-driven pharmacologic response. Bespoke computational methods - a combination of quantum mechanics, molecular dynamics, binding free energy simulations, and artificial intelligence - were developed during the course of the project to design SNX281 by explicitly accounting for the unique self-dimerization mechanism and the large-scale conformational change of the STING protein upon activation. Over the course of the project, we explored millions of virtual molecules while synthesizing and testing only 208 molecules in the lab. This work highlights the value of a multifaceted computationally-driven approach anchored by methods tailored to address target-specific problems encountered along the project progression from initial hit to the clinic.

cancer biology↗

bioRxiv2021

Atomic-Resolution Prediction of Degrader-mediated Ternary Complex Structures by Combining Molecular Simulations with Hydrogen Deuterium Exchange

Targeted protein degradation (TPD) has emerged as a powerful approach in drug discovery for removing (rather than inhibiting) proteins implicated in diseases. A key step in this approach is the formation of an induced proximity complex, where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) assessment of ubiquitination propensity via the full Cullin Ring Ligase (CRL) macromolecular assembly. The novel approach presented here combines experimental biophysical data--in this case hydrogen-deuterium exchange mass spectrometry (HDX-MS, which measures the solvent exposure of protein residues)--with all-atom explicit solvent molecular dynamics (MD) simulations aided by enhanced sampling techniques to predict structural ensembles of ternary complexes at atomic resolution. We present results demonstrating the efficiency, accuracy, and reliability of our approach to predict ternary structure ensembles using the bromodomain of SMARCA2 (SMARCA2BD) with the E3 ligase VHL as the system of interest. The simulations reproduce X-ray crystal structures - including prospective simulations validated on a new structure that we determined in this work (PDB ID: 7S4E) - with root mean square deviations (RMSD) of 1.1 to 1.6 [A]. The simulations also reveal a structural ensemble of low-energy conformations of the ternary complex within a broad energy basin. To further characterize the structural ensemble, we used snapshots from the aforementioned simulations as seeds for Hamiltonian replica exchange molecular dynamics (HREMD) simulations, and then perform 7.1 milliseconds of aggregate simulation time using Folding@home. The resulting free energy surface identifies the crystal structure conformation within a broad low-energy basin and the dynamic ensemble is consistent with solution-phase biophysical experimental data (HDX-MS and small-angle x-ray scattering, SAXS). Finally, we graft structures from the ternary complexes onto the full CRL and perform enhanced sampling simulations, where we find that differences in degradation efficiency can be explained by the proximity distribution of lysine residues on the POI relative to the E2-loaded ubiquitin. Several of the top predicted ubiquitinated lysine residues are validated prospectively through a ubiquitin mapping proteomics experiment.

biophysics↗