G protein-coupled receptors (GPCRs) represent the largest membrane protein family and a significant target class for therapeutics. Receptors from GPCRs’ largest class, class A, influence virtually every aspect of human physiology. About 45% of the members of this family endogenously bind flexible peptides or peptides segments within larger protein ligands. While many of these peptides have been structurally characterized in their solution state, the few studies of peptides in their receptor-bound state suggest that these peptides interact with a shared set of residues and undergo significant conformational changes. For the purpose of understanding binding dynamics and the development of peptidomimetic drug compounds, further studies should investigate the peptide ligands that are complexed to their cognate receptor.

There is an increased interest, in the field of cardiac modeling, for an improved coordinate system that can consistently describe local position within a heart geometry across various distinct geometries. A newly designed coordinate system, Cobiveco, meets these requirements. However, it assumes the use of biventricular models with a flat base, ignoring important cardiac structures. Therefore, we extended the scope of this state-of-the-art biventricular coordinate system to work with various heart geometries which include basal cardiac structures that were previously unaccounted for in Cobiveco. First, we implemented a semi-automated input surface assignment for increased accessibility and reproducibility of assigned coordinates. Then, we extended the coordinate system to handle more anatomically accurate biventricular models including the valve planes, which are of great interest when modeling diseases that manifest themselves in the basal area. Furthermore, we added the functionality of mapping vector data, such as myocardial fiber orientations, which are crucial for replicating the anisotropic electrical propagation in cardiac tissue.

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A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. We attached a cyclen-based paramagnetic lanthanide tag to an engineered noncanonical amino acid (ncAA) using a coppercatalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone H N pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The most accurate DsbB models generated in this case gave Cα-RMSD values over the transmembrane region of 2.11 Å (best-RMSD) and 3.23 Å (best-scoring).