New methods based on surfaces or beads have allowed measurement of properties of single DNA molecules in very accurate ways. Theoretical coarse grained models have been developed to understand the behavior of single stranded and double stranded DNA. These models have been shown to be accurate and relatively simple for very short systems of 6-8 base pairs near surfaces. Comparatively less is known about the influence of a surface on the secondary structures of longer molecules important to many technologies. Surface fields due to either applied potentials and/or dielectric boundaries are not in current surface mounted coarse grained models. To gain insight into longer and surface mounted sequences we parameterized a discretized worm-like chain model. Each link is considered a sphere of 6 base pairs in length for dsDNA, and 1.5 bases for ssDNA (requiring an always even number of spheres). For this demonstration of the model, the chain is tethered to a surface by a fixed length, non-interacting 0.536 nm linker. Configurational sampling was achieved via Monte-Carlo simulation. Our model successfully reproduces end to end distance averages from experimental results, in agreement with polymer theory and all atom simulations. Our average tilt results are also in agreement with all atom simulations for the case of dense systems.

Accurate modeling and design of protein-ligand interactions have broad applications in cell, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein homology modeling-ligand docking-protein design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced fit modes of ligand binding. In structure modeling tests, we blindly predicted near-atomic accuracy ligand conformations bound to G protein-coupled receptors (GPCRs) that were rarely identified by traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally-uncharacterized GPCRs. We then applied the technique to design functional human dopamine receptors with novel ligand binding selectivity. Most blindly predicted ligand binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand binding profile of membrane receptors that remain difficult to crystallize.

Summary Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug binding sites in structurally-uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.

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