Macromolecular modeling and design are increasingly useful in basic research, biotechnology, and teaching. However, the absence of a user-friendly modeling framework that provides access to a wide range of modeling capabilities is hampering the wider adoption of computational methods by non-experts. RosettaScripts is an XML-like language for specifying modeling tasks in the Rosetta framework. RosettaScripts provides access to protocol-level functionalities, such as rigid-body docking and sequence redesign, and allows fast testing and deployment of complex protocols without need for modifying or recompiling the underlying C++ code. We illustrate these capabilities with RosettaScripts protocols for the stabilization of proteins, the generation of computationally constrained libraries for experimental selection of higher-affinity binding proteins, loop remodeling, small-molecule ligand docking, design of ligand-binding proteins, and specificity redesign in DNA-binding proteins.

Ewing sarcoma is a primary bone tumor initiated by EWSR1–ETS gene fusions. To identify secondary genetic lesions that contribute to tumor progression, we performed whole-genome sequencing of 112 Ewing sarcoma samples and matched germline DNA. Overall, Ewing sarcoma tumors had relatively few single-nucleotide variants, indels, structural variants and copy-number alterations. Apart from whole chromosome arm copy-number changes, the most common somatic mutations were detected in STAG2 (17%), CDKN2A (12%), TP53 (7%), EZH2, BCOR, and ZMYM3 (2.7% each). Strikingly, STAG2 mutations and CDKN2A deletions were mutually exclusive, as confirmed in Ewing sarcoma cell lines. In an expanded cohort of 299 patients with clinical data, we discovered that STAG2 and TP53 mutations are often concurrent and are associated with poor outcome. Finally, we detected subclonal STAG2 mutations in diagnostic tumors and expansion of STAG2 immuno-negative cells in relapsed tumors as compared with matched diagnostic samples.

The emergence of human infection with a novel H7N9 influenza virus in China raises a pandemic concern. Chicken H9N2 viruses provided all six of the novel reassortant’s internal genes. However, it is not fully understood how the prevalence and evolution of these H9N2 chicken viruses facilitated the genesis of the novel H7N9 viruses. Here we show that over more than 10 y of cocirculation of multiple H9N2 genotypes, a genotype (G57) emerged that had changed antigenicity and improved adaptability in chickens. It became predominant in vaccinated farm chickens in China, caused widespread outbreaks in 2010–2013 before the H7N9 viruses emerged in humans, and finally provided all of their internal genes to the novel H7N9 viruses. The prevalence and variation of H9N2 influenza virus in farmed poultry could provide an important early warning of the emergence of novel reassortants with pandemic potential.

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.

Here we sequence 633 genes, encoding the majority of known epigenetic regulatory proteins, in over 1000 pediatric tumors to define the landscape of somatic mutations in epigenetic regulators in pediatric cancer. Our results demonstrate a marked variation in the frequency of gene mutations across 21 different pediatric cancer subtypes, with the highest frequency of mutations detected in high-grade gliomas, T-lineage acute lymphoblastic leukemia, medulloblastoma, and a paucity of mutations in low-grade glioma, and retinoblastoma. The most frequently mutated genes are H3F3A, PHF6, ATRX, KDM6A, SMARCA4, ASXL2, CREBBP, EZH2, MLL2, USP7, ASXL1, NSD2, SETD2, SMC1A, and ZMYM3. Importantly, we identify novel loss-of-function mutations in the ubiquitin-specific-processing protease 7 (USP7) in pediatric leukemia, which result in a decrease in deubiquitination activity. Collectively, our results help to define the landscape of mutations in epigenetic regulatory genes in pediatric cancer and yield a valuable new database for investigating the role of epigenetic dysregulations in cancer.