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Cyanobacteria are responsible for up to 80% of aquatic carbon dioxide fixation and have evolved a specialized carbon concentrating mechanism to increase photosynthetic yield. As such, cyanobacteria are attractive targets for synthetic biology and engineering approaches to address the demands of global energy security, food production, and climate change for an increasing world’s population. The bicarbonate transporter BicA is a sodium-dependent, low-affinity, high-flux bicarbonate symporter expressed in the plasma membrane of cyanobacteria. Despite extensive biochemical characterization of BicA, including the resolution of the BicA crystal structure, the dynamic understanding of the bicarbonate transport mechanism remains elusive. To this end, we have collected over 1 ms of all-atom molecular dynamics simulation data of the BicA dimer to elucidate the structural rearrangements involved in the substrate transport process. We further characterized the energetics of the transition of BicA protomers and investigated potential mutations that are shown to decrease the free energy barrier of conformational transitions. In all, our study illuminates a detailed mechanistic understanding of the conformational dynamics of bicarbonate transporters and provides atomistic insights to engineering these transporters for enhanced photosynthetic production. 

Estimating and disentangling epistemic uncertainty, uncertainty that is reducible with more training data, and aleatoric uncertainty, uncertainty that is inherent to the task at hand, is critically important when applying machine learning to highstakes applications such as medical imaging and weather forecasting. Conditional diffusion models’ breakthrough ability to accurately and efficiently sample from the posterior distribution of a dataset now makes uncertainty estimation conceptually straightforward: One need only train and sample from a large ensemble of diffusion models. Unfortunately, training such an ensemble becomes computationally intractable as the complexity of the model architecture grows. In this work we introduce a new approach to ensembling, hyper-diffusion models (HyperDM), which allows one to accurately estimate both epistemic and aleatoric uncertainty with a single model. Unlike existing single-model uncertainty methods like Monte-Carlo dropout and Bayesian neural networks, HyperDM offers prediction accuracy on par with, and in some cases superior to, multi-model ensembles. Furthermore, our proposed approach scales to modern network architectures such as Attention U-Net and yields more accurate uncertainty estimates compared to existing methods. We validate our method on two distinct real-world tasks: x-ray computed tomography reconstruction and weather temperature forecasting. Source code is publicly available at https://github.com/matthewachan/hyperdm. 

Membrane transporters of the solute carrier 6 (SLC6) family mediate various physiological processes by facilitating the translocation of amino acids, neurotransmitters, and other metabolites. In the body, the activity of these transporters is tightly controlled through various post-translational modifications with implications on protein expression, stability, membrane trafficking, and dynamics. While N-linked glycosylation is a universal regulatory mechanism among eukaryotes, a consistent mechanism of how glycosylation affects the SLC6 transporter family remains elusive. It is generally believed that glycans influence transporter stability and membrane trafficking; however, the role of glycosylation on transporter dynamics remains disputable, with differing conclusions among individual transporters across the SLC6 family. In this study, we collected over 1 ms of aggregated all-atom molecular dynamics (MD) simulation data to systematically identify the impact of N-glycans on SLC6 transporter dynamics. We modeled four human SLC6 transporters, the serotonin, dopamine, glycine, and B0AT1 transporters, by first simulating all possible combinations of a glycan attached to each glycosylation site followed by investigating the effect of larger, oligo-N-linked glycans to each transporter. The simulations reveal that glycosylation does not significantly affect the transporter structure but alters the dynamics of the glycosylated extracellular loop and surrounding regions. The structural consequences of glycosylation on the loop dynamics are further emphasized with larger glycan molecules attached. However, no apparent differences in ligand stability or movement of the gating helices were observed, and as such, the simulations suggest that glycosylation does not have a profound effect on conformational dynamics associated with substrate transport. 

Swirl combustion is encountered in many engineering applications since it provides efficient fuel burning. Experimental studies of turbulent swirl combustion poses challenges due to unsteady nature of the combustion phenomenon. Therefore, computational approaches are a promising alternative for the numerical studies of supersonic combustion. The present studies concerns the computational studies of swirl combustion, particularly the effect of the injection scheme on the combustion efficiency and flame stability. Therefore, the effect of the air-fuel ratio on the combustion efficiency and flame stability is subject of investigation. The combustion efficiency is assessed based on the temperature developed inside the swirl combustor. The computations are carried out using the large-eddy simulation (LES) approach along with the flamelet combustion model. The analysis reveals the unsteady nature of the flame and thus, its departure from the core of the combustor. The analysis also reveals the presence of a region of high level of temperature, NO and2CO , inside the combustor. 

Cyanobacteria are responsible for up to 80% of aquatic carbon dioxide fixation and have evolved specialized carbon concentrating mechanism to increase photosynthetic yield. As such, cyanobacteria are attractive targets for synethic biology and engineering approaches to address the demands of global energy security, food production, and climate change for an increasing world’s population. The bicarbonate transporter BicA is a sodium-dependent, low-affinity, high-flux bicarbonate symporter expressed in the plasma membrane of cyanobacteria. Despite extensive biochemical characterization of BicA, including the resolution of the BicA crystal structure, the dynamic understanding of the bicarbonate mechanism remains elusive. To this end, we have collected over 1 ms of all-atom molecular dynamics simulation data of the BicA dimer to elucidate the structural rearrangements involved in the substrate transport process. We further characterized the energetics of the cooperativity between BicA promoters and investigated potential mutations that are shown to decrease the free energy barrier of conformational transitions. In all, our study illuminates a detailed mechanistic understanding of the conformational dynamics of bicarbonate transporters and provide atomistic insights to engineering these transporters for enhanced photosynthetic production.