No abstract

A new multi-label deep neural network architecture is used to combine Infrared and mass spectra, trained on single compounds to predict functional groups, and experimentally validated on complex mixtures.

The facile abstraction of bis-allylic hydrogens from polyunsaturated fatty acids (PUFAs) is the hallmark chemistry responsible for initiation and propagation of autoxidation reactions. The products of these autoxidation reactions can form cross-links to other membrane components, damage proteins and nucleic acid. We report that PUFAs deuterated at bis-allylic sites are much more resistant to autoxidation reactions, due to the isotope effect. This is shown using coenzyme Q-deficient Saccharomyces cerevisiae coq mutants with defects in biosynthesis of coenzyme Q (Q). Q functions in respiratory energy metabolism and also functions as a lipid-soluble antioxidant. Yeast coq mutants incubated in the presence of the PUFAs α-linolenic or linoleic acid exhibit 99% loss of colony formation after four hours, demonstrating a profound loss of viability. In contrast, coq mutants treated with monounsaturated oleic acid or with one of the deuterated PUFAs:11,11-D2-Linoleic or 11,11,14,14-D4-αLinolenic retain viability similar to wild-type yeast. Deuterated PUFAs also confer protection to wild-type yeast subjected to heat stress. These results indicate that isotope-reinforced PUFAs are stabilized compared to standard PUFAs, and they protect coq mutants and wild-type yeast cells against the toxic effects of lipid autoxidation products. These findings suggest new approaches to controlling ROS-inflicted cellular damage and oxidative stress.

Figure S1: Distribution of RMSD values (Å) for all ligand poses generated by CANDOCK for docked poses in the CASF-2016 benchmark for (a) rigid-protein docking, (b) semi-flexible protein, and (c) fully-flexible protein docking.

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and they are unable to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample chemical relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.