Role of electrostatic interactions for ligand recognition and specificity of peptide transporters.

BACKGROUND Peptide transporters are membrane proteins that mediate the cellular uptake of di- and tripeptides, and of peptidomimetic drugs such as β-lactam antibiotics, antiviral drugs and antineoplastic agents. In spite of their high physiological and pharmaceutical importance, the molecular recognition by these transporters of the amino acid side chains of short peptides and thus the mechanisms for substrate binding and specificity are far from being understood. RESULTS The X-ray crystal structure of the peptide transporter YePEPT from the bacterium Yersinia enterocolitica together with functional studies have unveiled the molecular bases for recognition, binding and specificity of dipeptides with a charged amino acid residue at the N-terminal position. In wild-type YePEPT, the significant specificity for the dipeptides Asp-Ala and Glu-Ala is defined by electrostatic interaction between the in the structure identified positively charged Lys314 and the negatively charged amino acid side chain of these dipeptides. Mutagenesis of Lys314 into the negatively charged residue Glu allowed tuning of the substrate specificity of YePEPT for the positively charged dipeptide Lys-Ala. Importantly, molecular insights acquired from the prokaryotic peptide transporter YePEPT combined with mutagenesis and functional uptake studies with human PEPT1 expressed in Xenopus oocytes also allowed tuning of human PEPT1's substrate specificity, thus improving our understanding of substrate recognition and specificity of this physiologically and pharmaceutically important peptide transporter. CONCLUSION This study provides the molecular bases for recognition, binding and specificity of peptide transporters for dipeptides with a charged amino acid residue at the N-terminal position.

RNAs and ribonucleoproteins in recognition and catalysis.

CONTENTS. 1. Did life begin with catalytic RNA?–2. Self-splicing and self-cleaving RNAs–2.1 Self-splicing of group I introns – 2.2 Self-splicing of group II introns – 2.3 Self-cleaving RNAs–3. Splicing mediated by trans-acting factors–3.1 Group III introns – 3.2 Splicing of nuclear pre-mRNAs – 3.3 Trans-splicing – 3.4 Is nuclear pre-mRNA splicing evolutionarily related to group I and group II self-splicing?– 3.5 Non-RNA mediated splicing of tRNAs–4. Processing of ribosomal precursor RNAs–5. Processing of pre-mRNA 3′ ends–5.1 Polyadenylation – 5.2 Histone pre-mRNA 3′ processing–6. Other RNPs involved in metabolic mechanisms–6.1 5′ end processing of pre-tRNAs by RNase P – 6.2 The signal recognition particle – 6.3 Telomerase – 6.4 RNA editing in trypanosomatid mitochondria–7. Why RNA?

Food Recognition for Dietary Assessment Using Deep Convolutional Neural Networks

Diet management is a key factor for the prevention and treatment of diet-related chronic diseases. Computer vision systems aim to provide automated food intake assessment using meal images. We propose a method for the recognition of already segmented food items in meal images. The method uses a 6-layer deep convolutional neural network to classify food image patches. For each food item, overlapping patches are extracted and classified and the class with the majority of votes is assigned to it. Experiments on a manually annotated dataset with 573 food items justified the choice of the involved components and proved the effectiveness of the proposed system yielding an overall accuracy of 84.9%.