Knowledge of what happens on the molecular scale within is critical for our understanding the function of the human body and other living organisms. Here we highlight three recently published papers that provide critical insights into three key aspects of molecular biology: RNA structure, points mutations, and protein-protein interactions.
The X-ray Structures of Six Octameric RNA Duplexes in the Presence of Different Di- and Trivalent Cations
Michelle F. Schaffer, Guanya Peng, Bernhard Spingler, Joachim Schnabl, Meitian Wang, Vincent Olieric, and Roland K. O. Sigel
Due to the polyanionic nature of RNA, the principles of charge neutralization and electrostatic condensation require that cations help to overcome the repulsive forces in order for RNA to adopt a three-dimensional structure. A precise structural knowledge of RNA-metal ion interactions is crucial to understand the mechanism of metal ions in the catalytic or regulatory activity of RNA. We solved the crystal structure of an octameric RNA duplex in the presence of the di- and trivalent metal ions Ca2+, Mn2+, Co2+, Cu2+, Sr2+, and Tb3+. The detailed investigation reveals a unique innersphere interaction to uracil and extends the knowledge of the influence of metal ions for conformational changes in RNA structure. Furthermore, we could demonstrate that an accurate localization of the metal ions in the X-ray structures require the consideration of several crystallographic and geometrical parameters as well as the anomalous difference map.
Michael B. Doud, and Jesse D. Bloom
Influenza genes evolve mostly via point mutations, and so knowing the effect of every amino-acid mutation provides information about evolutionary paths available to the virus. We and others have combined high-throughput mutagenesis with deep sequencing to estimate the effects of large numbers of mutations to influenza genes. However, these measurements have suffered from substantial experimental noise due to a variety of technical problems, the most prominent of which is bottlenecking during the generation of mutant viruses from plasmids. Here we describe advances that ameliorate these problems, enabling us to measure with greatly improved accuracy and reproducibility the effects of all amino-acid mutations to an H1 influenza hemagglutinin on viral replication in cell culture. The largest improvements come from using a helper virus to reduce bottlenecks when generating viruses from plasmids. Our measurements confirm at much higher resolution the results of previous studies suggesting that antigenic sites on the globular head of hemagglutinin are highly tolerant of mutations. We also show that other regions of hemagglutinin—including the stalk epitopes targeted by broadly neutralizing antibodies—have a much lower inherent capacity to tolerate point mutations. The ability to accurately measure the effects of all influenza mutations should enhance efforts to understand and predict viral evolution.
RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences
Ji-Yong An, Zhu-Hong You, Fan-Rong Meng, Shu-Juan Xu and Yin Wang
Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely available web server called RVMAB-PPI in Hypertext Preprocessor (PHP) for predicting PPIs. The web server including source code and the datasets are available at https://18.104.22.168:8888/ppi_ab/.