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.

Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987–2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed “RosettaScripts” for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta’s ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.

Structure-based drug design is frequently used to accelerate the development of small-molecule therapeutics. Although substantial progress has been made in X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, the availability of high-resolution structures is limited owing to the frequent inability to crystallize or obtain sufficient NMR restraints for large or flexible proteins. Computational methods can be used to both predict unknown protein structures and model ligand interactions when experimental data are unavailable. This paper describes a comprehensive and detailed protocol using the Rosetta modeling suite to dock small-molecule ligands into comparative models. In the protocol presented here, we review the comparative modeling process, including sequence alignment, threading and loop building. Next, we cover docking a small-molecule ligand into the protein comparative model. In addition, we discuss criteria that can improve ligand docking into comparative models. Finally, and importantly, we Reprints and permissions information is available online at http://www.nature.com/reprints/index.html.Correspondence should be addressed to J.M. (jens.meiler@vanderbilt.edu). 7 These authors contributed equally to this work.Note: Supplementary information is available in the online version of the paper. AUTHOR CONTRIBUTIONSAll authors contributed equally to this work. All authors wrote substantial portions of the main text, the figures and the supplementary information. S.A.C. proposed the composition of the work for the benefit of the scientific community, tested the presented protocol and managed submission. S.L.D. wrote instructions on how to install the software, generated the comparative models, wrote dataprocessing scripts and managed references. S.H.D. wrote the supplementary glossary and was responsible for overall editing of the work. G.H.L. wrote the RosettaLigand program in its present form. D.P.N. carefully read through the manuscript for consistency and accuracy and helped in the analysis of the generated models. E.D.N. also generated comparative models, performed all of the ligand docking and performed the data analysis. J.R.W. contributed several figures, data-processing scripts, specialty movers, wrote large sections of the tutorial and managed references. J.H.S. tested the protocol, wrote the Troubleshooting section and edited the manuscript for clarity. J.M. helped define the scope of the work and guided the process of establishing the protocol. COMPETING FINANCIAL INTERESTSThe authors declare no competing financial interests. Author Manuscript present a strategy for assessing model quality. The entire protocol is presented on a single example selected solely for didactic purposes. The results are therefore not representative and do not replace benchmarks published elsewhere. We also provide an additional tutorial so that the user can gain hands-on experience in using Rosetta. The protocol should take 5-7 h, with additional time allocated for computer generation of models....

Here, we report that novel epidermal growth factor receptor (EGFR) gene fusions comprising the N-terminal of EGFR linked to various fusion partners, most commonly RAD51, are recurrent in lung cancer. We describe five patients with metastatic lung cancer whose tumors harbored EGFR fusions, four of whom were treated with EGFR tyrosine kinase inhibitors (TKIs) with documented anti-tumor responses. In vitro, EGFR-RAD51 fusions are oncogenic and can be therapeutically targeted with available EGFR TKIs and therapeutic antibodies. These results support the dependence of EGFR-rearranged tumors on EGFR-mediated signaling and suggest several therapeutic strategies for patients whose tumors harbor this novel alteration.

Oncogenic EGFR mutations are found in 10-35% of lung adenocarcinomas. Such mutations, which present most commonly as small in-frame deletions in exon 19 or point mutations in exon 21 (L858R), confer sensitivity to EGFR tyrosine kinase inhibitors (TKIs). In analyzing the tumor from a 33-year-old male never smoker, we identified a novel EGFR alteration in lung cancer: EGFR exon 18-25 kinase domain duplication (EGFR-KDD). Through analysis of a larger cohort of tumor samples, we detected additional cases of EGFR-KDD in lung, brain, and other cancers. In vitro, EGFR-KDD is constitutively active, and computational modeling provides potential mechanistic support for its auto-activation. EGFR-KDD-transformed cells are sensitive to EGFR TKIs and, consistent with these in vitro findings, the index patient had a partial response to the EGFR TKI, afatinib. The patient eventually progressed, at which time, re-sequencing revealed an EGFR-dependent mechanism of acquired resistance to afatinib, thereby validating EGFR-KDD as a driver alteration and therapeutic target.