Bulletin of the American Physical Society
2020 Annual Meeting of the APS Four Corners Section (Virtual)
Volume 65, Number 16
Friday–Saturday, October 23–24, 2020; Albuquerque, NM (Virtual)
Session B04: Biophysics and Soft Condensed Matter ILive
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Chair: Kathrin Spendier, UCCS |
Friday, October 23, 2020 10:30AM - 10:54AM Live |
B04.00001: Developing a Drug Delivery System Using Magnetic Particles -- Tools and Strategies Invited Speaker: Kathrin Spendier Magnetic nanoparticles have been proposed for a variety of applications in biological systems including, killing tumors through hyperthermia and magnetic resonance image (MRI) contrast enhancements. Our work is devoted to investigating the feasibility of using magnetic particles as drug delivery vehicles through high viscosity fluids. To do this we developed an imaging system that can control and visualize magnetic particle motion in a variety of oscillating fields. Depending on the magnetic material used as well as the oscillating/rotating frequency and amplitude of the applied magnetic field, we observe a particle not only to rotate and oscillate but also to wiggle, spin, and flip. These different modes of particle motion are described by a simple mathematically model that we use to further study the behavior of magnetic particle motion in high viscosity fluids. [Preview Abstract] |
Friday, October 23, 2020 10:54AM - 11:06AM Live |
B04.00002: Spatio-Temporal Super-Resolution Imaging Improves Localization of Emitters in High Background Samples Megan K. Dunlap, Ducan P. Ryan, Peter M. Goodwin, James H. Werner, Jennifer A. Hollingsworth, Martin P. Gelfand, Alan Van Orden A super-resolution microscope equipped with time-correlated single photon counting detectors records photon arrival times that can be incorporated into the localization algorithm. This additional information improves the localization precision of single emitters in the presence of high fluorescent background. In this talk, we report on details of the localization algorithm that incorporates the single photon arrival times, and we demonstrate its advantages over a traditional analysis with simulated and experimental data. LA-UR-20-27550. [Preview Abstract] |
Friday, October 23, 2020 11:06AM - 11:18AM Live |
B04.00003: Conformational change and substrate binding to the bile acid transporter ASBT$_{\mathrm{NM}}$ Fiona Naughton, Oliver Beckstein The apical sodium-dependant bile acid transporter (ASBT) utilises the sodium gradient to drive the reabsorption of bile acids from the intestine, operating through an alternating-access mechanism. It is of interest as a potential target for the treatment of hypercholesterolemia as well as for drug delivery. Structures of bacterial homologues, including that from Neisseria meningitidis (ASBT$_{\mathrm{NM}})$ in an inward-facing conformation with sodium and the bile acid taurocholate bound, are available. However, less is known about the more dynamic details, including those involving substrate binding and the nature of the conformational transition. We have used molecular dynamics simulations to investigate the behaviour of ASBT$_{\mathrm{NM}}$ on an atomistic scale. Metadynamics was used to explore the inward-outward transition and the binding landscape of taurocholate and sodium to inward facing ASBT$_{\mathrm{NM}}$, with refinement using bias-exchange umbrella sampling to quantify the latter, characterising both the main and alternate binding sites. Together, these results represent an important step towards understanding the complete transport cycle of ASBT. [Preview Abstract] |
Friday, October 23, 2020 11:18AM - 11:30AM Live |
B04.00004: Improved Protein Contact Predictions using Diverse Neural Networks Wendy Billings, Dennis Della Corte Substantial progress has been made toward the accurate prediction of protein tertiary structure from primary sequence, aided by machine learning. Current success is based on two-stage protocols: first, prediction of macromolecular structure restraints by deep convolutional neural networks; second, application of these restraints to construct a folded three-dimensional structure of the target protein. Accuracy of the final atomic model depends heavily on the individual quality of both stages. Here we evaluate inter-residue distance predictions made by neural networks in the first stage by way of the related contact prediction task. Using published protein structures from CASP13, we calculate the contact prediction performance of several networks (including AlphaFold, trRosetta, and our own ProSPr) both individually and combined in ensembles. Our results demonstrate that combining the predictions of diverse neural networks can improve contact prediction accuracy and outperform the best individual networks. We call for increased availability of distance prediction networks for a community-based ensemble approach to superior protein contact prediction. [Preview Abstract] |
Friday, October 23, 2020 11:30AM - 11:42AM |
B04.00005: Combining Consensus and Ensemble Docking Methods to Improve Molecular Docking Connor Morris, Dennis Della Corte Molecular docking programs are computational tools used to predict protein-ligand binding poses and energy. They are widely used in drug discovery to filter binding ligands from nonbinding ones in the search for potential drug candidates. However, they suffer from two main weaknesses: inaccurate scoring functions and rigid protein receptors. Two distinct methods, consensus docking and ensemble docking, are used to account for these problems separately. Consensus docking uses multiple docking scoring functions to evaluate docking poses, mitigating the weaknesses of each individual scoring function. Ensemble docking uses molecular dynamics (MD) to incorporate protein flexibility into docking simulations. Combining consensus docking and ensemble docking methods into a single docking protocol leads to improved docking results. [Preview Abstract] |
Friday, October 23, 2020 11:42AM - 11:54AM |
B04.00006: Protein Structure Prediction Using 3-D ResNet Bryce Hedelius Predicting a protein's structure from its amino acid sequence is a longstanding problem. Deep learning methods based currently produce the best predictions. Sequence alignments are used to generate features, such as amino acid identity and propensity. A network then predicts characteristics of the structure such as inter-residual distances or torsion angles which are used to generate a prediction of the structure. Here I propose a method based on a 3D residual neural network. The features are derived from a generalized Potts model that considers three-wise interactions and the labels include planar and dihedral angles involving three residues. The predictions will then be used as constraints in a molecular dynamics simulation. Higher order constraints are expected to aid structure generation while improving predictions of other constraints. [Preview Abstract] |
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