A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.
Janus kinases (JAKs) are enzymes involved in signaling pathways that affect hematopoiesis and immune cell functions. JAK1, JAK2, and JAK3 play different roles in numerous diseases of the immune system and have also been considered as potential targets for cancer therapy. In the present study, the susceptibility of the oral JAK inhibitor tofacitinib against these three JAKs was elucidated using the 500-ns molecular dynamics (MD) simulations and free energy calculations based on MM-PB(GB)SA, QM/MM-GBSA (PM3 and SCC-DFTB), and SIE methods. The obtained results revealed that tofacitinib could interact with all JAKs at the ATP-binding site via electrostatic attraction, hydrogen bond formation, and in particular van der Waals interaction. The conserved glutamate and leucine residues (E957 and L959 of JAK1, E930 and L932 of JAK2, and E903 and L905 of JAK3) located in the hinge region stabilized tofacitinib binding through strongly formed hydrogen bonds. Complexation with the incoming tofacitinib led to a closed conformation of the ATP-binding site and a decreased protein fluctuation at the glycine loop of the JAK protein. The binding affinities of tofacitinib/JAKs were ranked in the order of JAK3 > JAK2 ∼ JAK1, which are in line with the reported experimental data.
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