The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. In addition to de novo structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein−protein complexes, design altered specificity protein−protein and protein−DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem.

The G-protein activated, inward-rectifying potassium (K + ) channels, "GIRKs", are a family of ion channels (K ir 3.1-K ir 3.4) that has been the focus of intense research interest for nearly two decades. GIRKs are comprised of various homo-and heterotetrameric combinations of four different subunits. These subunits are expressed in different combinations in a variety of regions throughout the central nervous system and in the periphery. The body of GIRK research implicates GIRK in processes as diverse as controlling heart rhythm, to effects on reward/addiction, to modulation of response to analgesics. Despite years of GIRK research, very few tools exist to selectively modulate GIRK channels' activity and until now no tools existed that potently and selectively activated GIRKs. Here we report the development and characterization of the first truly potent, effective, and selective GIRK activator, ML297 (VU0456810). We further demonstrate that ML297 is active in two in vivo models of epilepsy, a disease where up to 40% of patients remain with symptoms refractory to present treatments. The development of ML297 represents a truly significant advancement in our ability to selectively probe GIRK's role in physiology as well as providing the first tool for beginning to understand GIRK's potential as a target for a diversity of therapeutic indications.

To identify potential determinants of substrate selectivity in serotonin (5-HT) transporters (SERT), models of human and Drosophila serotonin transporters (hSERT, dSERT) were built based on the leucine transporter (LeuT Aa ) structure reported by Yamashita et al. (Nature 2005;437:215-223), PBDID 2A65. Although the overall amino acid identity between SERTs and the LeuT Aa is only 17%, it increases to above 50% in the first shell of the putative 5-HT binding site, allowing de novo computational docking of tryptamine derivatives in atomic detail. Comparison of hSERT and dSERT complexed with substrates pinpoints likely structural determinants for substrate binding. Forgoing the use of experimental transport and binding data of tryptamine derivatives for construction of these models enables us to cHitically assess and validate their predictive power: A single 5-HT binding mode was identified that retains the amine placement observed in the LeuT Aa structure, matches sitedirected mutagenesis and substituted cysteine accessibility method (SCAM) data, complies with support vector machine derived relations activity relations, and predicts computational binding energies for 5-HT analogs with a significant correlation coefficient (R = 0.72). This binding mode places 5-HT deep in the binding pocket of the SERT with the 5-position near residue hSERT A169/ dSERT D164 in transmembrane helix 3, the indole nitrogen next to residue Y176/Y171, and the ethylamine tail under residues F335/F327 and S336/S328 within 4 Å of residue D98. Our studies identify a number of potential contacts whose contribution to substrate binding and transport was previously unsuspected.★Correspondence to: Jens Meiler,

Computational small molecule docking into comparative models of proteins is widely used to query protein function and in the development of small molecule therapeutics. We benchmark RosettaLigand docking into comparative models for nine proteins built during CASP8 that contain ligands. We supplement the study with 21 additional protein/ligand complexes to cover a wider space of chemotypes. During a full docking run in 21 of the 30 cases, RosettaLigand successfully found a native-like binding mode among the top ten scoring binding modes. From the benchmark cases we find that careful template selection based on ligand occupancy provides the best chance of success while overall sequence identity between template and target do not appear to improve results. We also find that binding energy normalized by atom number is often less than −0.4 in native-like binding modes.