In silico development of adenosine A2B receptor antagonists for sickle cell disease.

Sickle cell disease (SCD) is a disease resulting from mutation in the globin portion of hemoglobin caused by the replacement of adenine for thymine in the codon of the ß globin gene. In Brazil, SCD affects about 0.3% of the black and Caucasian population. Until now, there is no specific treatment and the available drugs have several serious adverse effects which makes the search for new drugs an emergently need. The use of computational techniques can accelerate the drug development process by prioritization of molecules with affinity against essential targets. Adenosine A2b receptor (rA2b) has been studied in SCD due to its relationship with red blood cells concentration of 2,3-diphosphoglycerate which reduces the hemoglobin affinity for oxygen (O2), facilitating its availability for the tissues. Then, development of rA2b antagonists could be helpful for the treatment of SCD. However, there is still no 3D structure of rA2b and to overcome this limitation, homology modeling should be applied. In this scenario, this study aims to build a suitable 3D model of rA2b by SWISS MODEL and to evaluate the structural aspects of rA2b with known antagonists that may be useful for the identification of new potential antagonists by molecular dynamics on a lipid bilayer environment using GROMACS 5.1.4. The complexes with antagonists ZINC223070016 and ZINC17974526 interacted with key residues by hydrophobic contacts and hydrogen bonds which stabilized them at the rA2b binding site. This intermolecular profile can contribute to the development of more potent rA2b antagonists. Communicated by Ramaswamy H. Sarma.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists.

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.