The transport activity of a membrane protein, bilitranslocase (T.C. # 2.A.65.1.1), which acts as a transporter of bilirubin from blood to liver cells, was experimentally determined for a large set of various endogenous compounds, drugs, purine and pyrimidine derivatives. On these grounds, the structure-activity models were developed following the OECD principles of QSAR models and their predictive ability for new chemicals was evaluated. The applicability domain of the models was estimated by Euclidean distances criteria according to the applied modeling method. The selection of the most influential structural variables was an important stage in the adopted modeling methodology. The interpretation of selected variables was performed in order to get an insight into the mechanism of transport through the cell membrane via bilitranslocase. Validation of the optimized models was performed by a previously determined validation set. The classification model was build to separate active from inactive compounds. The resulting accuracy, sensitivity, and specificity were 0.73, 0.89, and 0.64, respectively. Only active compounds were used to develop a predictive model for bilitranslocase inhibition constants. The model showed good predictive ability; Root Mean Squared error of the validation set, RMSV = 0.29 log units.
COBISS.SI-ID: 4723994
We present an approach towards structure elucidation of bilitranslocase, the 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 transmembrane 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. We have shown that the model for prediction of transmembrane segments proposed four transmembrane alpha helices, each containing around 20 amino acids. This result is partially confirmed with experimental studies using particular antibodies corresponding to parts of amino acid sequences of bilitranslocase. In order to shed light on the bilitranslocase transport mechanism, we also 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 either pass or block the transmembrane path enabled by bilitranslocase helps us to build a hypothesis about the transport mechanism of the studied biological system.
COBISS.SI-ID: 4798234
In the study of membrane proteins we found the counter-propagation neural network as a promising method for the construction of predictive models. In this paper we interpreted the mechanism of functioning of the applied neural networks. The goal of the study was to contribute to a better mechanistic understanding of so-called 'general' QSAR models for non-congeneric chemicals based on the counter propagation artificial neural network (CP ANN). Possible mechanisms of action was proofed using the Toxtree expert system based on structural alerts (SAs) for carcinogenicity. We have illustrated how statistically selected MDL descriptors, which refer to topological characteristics as well as to polarizability and charge distribution related to reactivity, are correlated with particular chemical classes (containing carcinogenic SA) with the recognized mechanistic link to the carcinogenic activity and consequently with the carcinogenic potency. Mechanistic insight in CP ANN models was demonstrated using an inherent mapping technique (i.e. Kohonen maps).
COBISS.SI-ID: 4861722