The beta-lactamase enzyme provides effective resistance to beta-lactam antibiotics due to substrate recognition controlled by point mutations. Recently, extended-spectrum and inhibitor-resistant mutants have become a global health problem. Here, the functional dynamics that control substrate recognition in TEM beta-lactamase are investigated using all-atom molecular dynamics simulations. Comparisons are made between wild-type TEM-1 and TEM-2 and the extended-spectrum mutants TEM-10 and TEM-52, both in apo form and in complex with four different antibiotics (ampicillin, amoxicillin, cefotaxime and ceftazidime). Dynamic allostery is predicted based on a quasi-harmonic normal mode analysis using a perturbation scan. An allosteric mechanism known to inhibit enzymatic function in TEM beta-lactamase is identified, along with other allosteric binding targets. Mechanisms for substrate recognition are elucidated using multivariate comparative analysis of molecular dynamics trajectories to identify changes in dynamics resulting from point mutations and ligand binding, and the conserved dynamics, which are functionally important, are extracted as well. The results suggest that the H10-H11 loop (residues 214-221) is a secondary anchor for larger extended spectrum ligands, while the H9-H10 loop (residues 194-202) is distal from the active site and stabilizes the protein against structural changes. These secondary non-catalytically-active loops offer attractive targets for novel noncompetitive inhibitors of TEM beta-lactamase.

The final stage of endocytosis involves the recruitment of the protein dynamin to the neck of the vesicle to cut the membrane and release the vesicle to the interior of the cell. Dynamin forms a helical protein coat around the vesicle neck that ultimately disrupts the lipid membrane and the vesicle is released. Dynamin is able to causes membrane scission by undergoing a large conformational change after catalyzing the hydrolysis of guanosine triphosphate (GTP) to guanosine diphosphate (GDP), which results in an allosteric change in the protein structure. Understanding how the reaction energy released by the hydrolysis reaction is used by the protein to undergo conformational changes is key to determining the molecular mechanism of membrane fission. To this end, we have used molecular dynamics of dynamin monomers, dimers, and tetramers in solution to understand the free energy changes associated with allosteric conformation changes undergone by the protein. These studies will allow us to begin developing a new molecular based model of how dynamin is able to channel the energy released by GTP hydrolysis to remodel lipid membranes during dynamin induced membrane fission.

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Motivation Ab initio gene prediction in non-model organisms is a difficult task. While many ab initio methods have been developed, their average accuracy over long segments of a genome, and especially when assessed over a wide range of species, generally yields results with sensitivity and specificity levels in the low 60% range. A common weakness of most methods is the tendency to learn patterns that are species-specific to varying degrees. The need exists for methods to extract genetic features that can distinguish coding and non-coding regions that are not sensitive to specific organism characteristics. Results A new method based on a neural network (NN) that uses a collection of sensors to create input features is presented. It is shown that accurate predictions are achieved even when trained on organisms that are significantly different phylogenetically than test organisms. A consensus prediction algorithm for a CoDing Sequence (CDS) is subsequently applied to the first nucleotide level of NN predictions that boosts accuracy through a data driven procedure that optimizes a CDS/nonCDS threshold. An aggregate accuracy benchmark at the nucleotide level shows that this new approach performs better than existing ab initio methods, while requiring significantly less training data. Availability https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning Supplementary information Supplementary data are available at Bioinformatics online.