Despite the importance of transmembrane proteins and growing interest in them, the vast majority of the membrane proteins remains underexplored owing to experimental difficulties. To fill this knowledge gap, several in-silico methods are developed aiming to predict the transmembrane regions, topology and structure. Although prediction for [alpha]-helical transmembrane regions can be made with considerable accuracy, it is not so in case of transmembrane [beta]-strands. The shorter and less hydrophobic transmembrane [beta]-strands are much harder to predict. The [beta]-barrel transmembrane proteins are present in the outer membrane of bacteria, whole organelles like mitochondria and chloroplasts. They function as ion transporters and play rolein passive nutrient uptake. In this work, we present a data-driven prediction model of beta-strand transmembrane region. The prediction is done based on amino acid sequence information without using any evolutionary data from multiple sequence alignments. Data on [beta]-barrel transmembrane proteins with atomic resolution structures and known transmembrane region is collected from public domain databases PDB and PDBTM. The protein sequences are separated into their transmembrane and non-transmembrane regions. The model is developed based on non-linear counter-propagation artificial neural network using mathematical descriptors defining the transmembrane protein sequences. The model shows 83% prediction accuracy when tested with external validation set. To further improve the prediction for unknown protein sequences and successfully eliminate false positives and negatives, statistical data on amino acid distribution in transmembrane [beta]-strands is incorporated in the final prediction. Finally, we did a benchmarking study comparing our developed prediction method with other algorithmic techniques and predictors available.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 4777242We present an approach towards structure elucidation of bilitranslocase (BTL) transmembrane regions. BTL is a membrane protein which transports bilirubin from blood to liver cells. The sequence and secondary structure information of transmembrane segments of proteins with known 3D structure is exploited to predict the transmembrane domains of structurally unresolved target protein. With the help of known structures the trans-membrane domains are encoded in such a way that it is possible to group and classify them with respect to their specific sub-structural characteristics and to build a model for prediction of transmembrane segments. In order to explore the bilitranslocase transport mechanism, we tested a set of non-congeneric compounds for their competitive inhibition constants in the investigated protein-substrate system. The information about chemical structure of small molecules that inhibit bilitranslocase helps us to build a hypothesis about the transport mechanism of the studied biological system.
B.04 Guest lecture
COBISS.SI-ID: 4708634The study presented here aims to compare the fatty acid composition and their characteristic profiles derived from erythrocyte phospholipids of patients with different diseases and to test the hypothesis that the changes in their profiles might be relevant to various diseases and metabolic abnormalities. The study sample consisted of 272 patients, among them 135 with inflammatory bowel disease (60 patients with Crohn disease and 75 patients with other inflammatory bowel diseases), 53 with uterine leiomyoma, 14 with verified absence of uterine leiomyoma, 52 with asthma, 18 with colon adenomas and 70 blood samples without any of mentioned diseases that were used as a control group. A robust and reliable analytical method, additionally improved and optimised, was used for the lipid extraction, separation of lipid classes and isolation of phospholipids by silica solid phase. Plasmalogens act as targets for oxidants and are major lipophilic antioxidants. The resulting fatty acids and plasmalogen linked dimethyl acetals were evaluated by the principal component analysis (PCA) by chemometrics and statistical methods. The study suggests differences in membrane fatty acid profiles in patients with different diseases which might have a potential for diagnostics and population screening for diseases, where fatty acids may play role as biomarkers for diseases.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 15470102