RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures.

Protein–RNA and protein–DNA interactions play fundamental roles in many biological processes. A detailed understanding of these interactions requires knowledge about protein–nucleic acid complex structures. Because the experimental determination of these complexes is time-consuming and perhaps futile in some instances, we have focused on computational docking methods starting from the separate structures. Docking methods are widely employed to study protein–protein interactions; however, only a few methods have been made available to model protein–nucleic acid complexes. Here, we describe NPDock (Nucleic acid–Protein Docking); a novel web server for predicting complexes of protein–nucleic acid structures which implements a computational workflow that includes docking, scoring of poses, clustering of the best-scored models and refinement of the most promising solutions. The NPDock server provides a user-friendly interface and 3D visualization of the results. The smallest set of input data consists of a protein structure and a DNA or RNA structure in PDB format. Advanced options are available to control specific details of the docking process and obtain intermediate results. The web server is available at http://genesilico.pl/NPDock.

Cancer-associated fibroblast (CAF) heterogeneity is increasingly appreciated, but the origins and functions of distinct CAF subtypes remain poorly understood. The abundant and transcriptionally diverse CAF population in pancreatic ductal adenocarcinoma (PDAC) is thought to arise from a common cell of origin, pancreatic stellate cells (PSC), with diversification resulting from cytokine and growth factor gradients within the tumor microenvironment. Here we analyzed the differentiation and function of PSCs during tumor progression in vivo. Contrary to expectations, we found that PSCs give rise to a numerically minor subset of PDAC CAFs. Targeted ablation of PSC-derived CAFs within their host tissue revealed nonredundant functions for this defined CAF population in shaping the PDAC microenvironment, including production of specific extracellular matrix components and tissue stiffness regulation. Together, these findings link stromal evolution from distinct cells of origin to transcriptional heterogeneity among PDAC CAFs and demonstrate unique functions for CAFs of a defined cellular origin. Significance: By tracking and ablating a specific CAF population, we find that a numerically minor CAF subtype from a defined cell of origin plays unique roles in establishing the pancreatic tumor microenvironment. Together with prior studies, this work suggests that mesenchymal lineage heterogeneity and signaling gradients diversify PDAC CAFs. See related commentary by Cukierman, p. 296. This article is highlighted in the In This Issue feature, p. 275

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5′-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson–Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Purpose: To carry out an integrative profile of human pancreatic ductal adenocarcinoma (PDAC) to identify prognosis-significant genes and their related pathways. Experimental Design: A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA and miRNA expression in 25 early-stage PDAC was carried out. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n = 148 and 42) were then used as in vitro validation of the array findings. Results: The composite score identified 171 genes in which expressions were able to define two prognosis subgroups (P = 3.8e-5). Eighty-eight percent (151 of 171) of the genes were regulated by prognosis-significant miRNAs. The phosphoinositide 3-kinase/AKT pathway and SRC signaling were densely populated by prognosis-significant genes and driven by genomic amplification of SRC and miRNA regulation of p85α and CBL. On tissue microarray validation (n = 148), p85α protein expression was associated with improved survival for all patients (P = 0.02), and activated P-SRC (Y418) was associated shorter survival for patients with low-grade histology tumors (P = 0.04). Interacting P-SRC and p85α revealed that they define two distinct PDAC patient subgroups (P = 0.0066). Furthering the importance of these pathways, CBL protein expression was associated with improved survival (P = 0.03) on a separate cohort (n = 42). Conclusions: These pathways and related genes may represent putative clinical biomarkers and possible targets of individualized therapy in the distinct patient subgroups they define. Clin Cancer Res; 18(5); 1352–63. ©2012 AACR.