Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

Toward a more complete view of tRNA biology

International audienceTransfer RNAs are ancient molecules present in all domains of life. In addition to translating the genetic code into protein and defining the second genetic code together with aminoacyl-tRNA synthetases, tRNAs act in many other cellular functions. Robust phenomenological observations on the role of tRNAs in translation, together with massive sequence and crystallographic data, have led to a deeper physicochemical understanding of tRNA architecture, dynamics and identity. In vitro studies complemented by cell biology data already indicate how tRNA behaves in cellular environments, in particular in higher Eukarya. From an opposite approach, reverse evolution considerations suggest how tRNAs emerged as simplified structures from the RNA world. This perspective discusses what basic questions remain unanswered, how these answers can be obtained and how a more rational understanding of the function and dysfunction of tRNA can have applications in medicine and biotechnology

Emergence and Evolution

The aminoacyl-tRNA synthetases (aaRSs) are essential components of the protein synthesis machinery responsible for defining the genetic code by pairing the correct amino acids to their cognate tRNAs. The aaRSs are an ancient enzyme family believed to have origins that may predate the last common ancestor and as such they provide insights into the evolution and development of the extant genetic code. Although the aaRSs have long been viewed as a highly conserved group of enzymes, findings within the last couple of decades have started to demonstrate how diverse and versatile these enzymes really are. Beyond their central role in translation, aaRSs and their numerous homologs have evolved a wide array of alternative functions both inside and outside translation. Current understanding of the emergence of the aaRSs, and their subsequent evolution into a functionally diverse enzyme family, are discussed in this chapter