The Procapsid Binding Domain of φ29 Packaging RNA Has a Modular Architecture and Requires 2‘-Hydroxyl Groups in Packaging RNA Interaction^†

The φ29 packaging RNA (pRNA) is an essential component in the φ29 bacteriophage DNA packaging motor, the strongest biomolecular motor known today. Utilizing Mg2+-dependent intermolecular base pairing interactions between two 4-nucleotide loops within the pRNA procapsid binding domain, multiple copies of pRNA form a ring-shaped complex that is indispensable for packaging motor function. To understand pRNA structural organization and pRNA/pRNA interaction, studies were carried out on pRNA closed dimers, the simplest functional pRNA complex believed to be the building blocks for assembling the oligomeric ring. Tertiary folding and interactions in various pRNA mutants were evaluated based on measured closed dimer affinity that is directly linked to the proper positioning of the interacting loops. The data revealed that the procapsid binding domain contains two autonomous modules that are capable of interacting noncovalently to form a fully active species in pRNA/pRNA interaction. Deleting the 2‘-hydroxyl groups in one of the interacting loops weakens the dimer affinity by 125-fold, suggesting potential tertiary interactions involving these 2‘-hydroxyl groups. The results provide evidence that nonbase functional groups are involved in pRNA folding and interaction and lead to a simple model that describes the pRNA monomer configuration in terms of three arms spanning a hinge. The functional constructs developed here will aid biophysical and biochemical investigations of pRNA structure and function, as well as developments of pRNA-based technology for nanoscience and gene therapy

Workflow of the three-step quantile regression forest method.

All features were screened by their Pearson correlations with drug response. Then a random forest was trained to rank selected features by their importance. The variables with the importance of twice standard deviation greater than the mean of importance were selected for the final quantile regression forest.</p

Information of the 95% and 80% prediction intervals of drug responses for 24 drugs.

Information of the 95% and 80% prediction intervals of drug responses for 24 drugs.</p

The Pearson correlation coefficients of observed and predicted drug responses (activity area) by QRFs.

The Pearson correlation coefficients of observed and predicted drug responses (activity area) by QRFs.</p

Variable importance and word clouds of functional annotations for the genes used by QRFs.

Panels (A) and (B) are the bar charts of variable importance for drugs 17-AAG and AZD6244. Word clouds of functional annotations of the genes for 24 drugs are in panel (C) (all genes) and panel (D) (ensemble of top 30 genes of each drug), where font size of each annotation indicates its enrichment score.</p

Boxplots and normal Q-Q plots of the activity areas in the CCLE dataset.

Panel (A) shows the boxplots of activity areas for 24 drugs. Panel (B) shows the normal Q-Q plots of activity area for two example drugs Lapatinib and Paclitaxel.</p

A quantile regression forest based method to predict drug response and assess prediction reliability

Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results.</div

The 95% prediction intervals and mean predictions by quantile regression forests.

Red triangular indicates the point (or mean) prediction of drug response, two red dots indicates the upper and lower boundaries of 95% prediction interval. (A) and (B) show the comparisons of 24 drugs for cell lines “CAPAN2” and “C2BBE1”, respectively. (C) and (D) are the comparisons of four different cell lines to drugs “Irinotecan” and “Topotecan”, respectively.</p

The correlations between territorial behaviors of male Chinese grouse (territorial flights, flutter-jumps) and assorted variables (a, testosterone; b, <i>T. tenuis</i> abundance; c, condition score; d, comb redness; e, comb size).

<p>Mated males (closed squares), Unmated males (open squares).</p

Prediction performance of quantile regression forests for CCLE data set.

(A) Bar chart of Pearson correlation coefficients of drug responses and predicted values by QRFs, ENR, ISIS, and CRF-20000. QRFs (mean): (conditional) mean prediction of drug response given genomic features using QRFs; QRFs (median): median prediction of drug response using QRFs. (B) Scatter plots of observed and predicted drug responses (activity area) for four drugs in CCLE using QRFs.</p