Human Ferrochelatase: Insights for the Mechanism of Ferrous Iron Approaching Protoporphyrin IX by QM/MM and QTCP Free Energy Studies.

Ferrochelatase catalyzes the insertion of ferrous iron into protoporphyrin IX, the terminal step in heme biosynthesis. Some disputes in its mechanism remain unsolved, especially for human ferrochelatase. In this paper, high-level quantum mechanical/molecular mechanics (QM/MM) and free-energy studies were performed to address these controversial issues including the iron-binding site, the optimal reaction path, the substrate porphyrin distortion, and the presence of the sitting-atop (SAT) complex. Our results reveal that the ferrous iron is probably at the binding site coordinating with Met76, and His263 plays the role of proton acceptor. The rate-determining step is either the first proton removed by His263 or the proton transition within the porphyrin with an energy barrier of 14.99 or 14.87 kcal/mol by the quantum mechanical thermodynamic cycle perturbation (QTCP) calculations, respectively. The fast deprotonation step with the conservative residues rather than porphyrin deformation found in solution provides the driving force for biochelation. The SAT complex is not a necessity for the catalysis though it induces a modest distortion on the porphyrin ring.

A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.