Estimation of airway smooth muscle stiffness changes due to length oscillation using artificial neural network

ISDTool 2.0: a computational model for predicting immunosuppressive domain of retroviruses

Immunosuppressive domain (ISD) is a conserved region of transmembrane proteins (TM) in envelope gene (env) of retroviruses. in vitro and vivo, a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. Evidence has shown that ISD suppresses lymphocyte proliferation and allows escape from immune effectors of the innate and adaptive arms in mouse immune system. Previously, we have developed a tool ISDTool 1.0 to identify ISD of human endogenous retrovirus (HERV). However, several other important retroviruses exist and no method is devoted to ISD prediction of them so far. In the paper, a computational model is proposed to identify ISD of six typical retroviruses from three species. The model combines the minimum Redundancy Maximum Relevance (mRMR) feature selection criterion with weighted extreme learning machine (WELM) to achieve high identification accuracies of 98.95%, 96.34% and 96.87% using self-consistency, 5-fold and 10-fold cross-validation, respectively. A software tool named ISDTool 2.0 has been developed to facilitate the application of the model and a large number of new putative ISDs of the six retroviruses were predicted. In addition, motifs of ISD in these retroviruses were analyzed and the evolutionary relationship was discussed. Datasets and the software involved in the paper are available at http://sourceforge.net/projects/isdtool/files/ISDTool-2.0/.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.