Journal of Theoretical and Computational Chemistry

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.

The clinical outcomes in patients with non-small cell lung cancer have improved, as a result of anaplastic lymphoma kinase (ALK) inhibition. Therefore in the current study, substantial effort has been made to identify ALK inhibitors through systematic virtual screening experiment consisting of e-pharmacophore and pharmacophore perception techniques. Initially, a pharmacophore model (AAAHP.193) and an e-pharmacophore model (DDRRR) encompassing the whole dataset of 12 known ALK inhibitors were developed. The hypotheses could retrieve effective compounds from DrugBank database (8621 molecules), which were then subjected to molecular docking and ADME prediction. These approaches resulted in the identification of five hits, namely, nebivolol, HDY, D42, 796, and LZE having higher Glide docking scores and promising ADME properties with augmented CNS involvement. Moreover, molecular dynamics simulations were performed to validate the inhibitory activity of the hit compounds, and density functional theory calculations were carried out to scrutinize the chemical reactivity of the hits. Subsequent interaction and scaffold analysis identified prominent interactions of the hits with ALK kinase domain and scaffolds with anti-tumor activity against lung cancer cell lines. We strongly believe that the study provides an outlook for the sighting of novel and potent ALK inhibitors in the near future.

Helix formation is of great significance in protein folding. The helix-forming tendencies of amino acids are accumulated along the sequence to determine the helix-forming tendency of peptides. Computer simulation can be used to model this process in atomic details and give structural insights. In the current work, we employ equilibrate-state free energy simulation to systematically study the folding/unfolding thermodynamics of a series of mutated peptides. Two AMBER force fields including AMBER99SB and AMBER14SB are compared. The new 14SB force field uses refitted torsion parameters compared with 99SB and they share the same atomic charge scheme. We find that in vacuo the helix formation is mutation dependent, which reflects the different helix propensities of different amino acids. In general, there are helix formers, helix indifferent groups and helix breakers. The helical structure becomes more favored when the length of the sequence becomes longer, which arises from the formation of additional backbone hydrogen bonds in the lengthened sequence. Therefore, the helix indifferent groups and helix breakers will become helix formers in long sequences. Also, protonation-dependent helix formation is observed for ionizable groups. In 14SB, the helical structures are more stable than in 99SB and differences can be observed in their grouping schemes, especially in the helix indifferent group. In solvents, all mutations are helix indifferent due to protein–solvent interactions. The decrease in the number of backbone hydrogen bonds is the same with the increase in the number of protein–water hydrogen bonds. The 14SB in explicit solvent is able to capture the free energy minima in the helical state while 14SB in implicit solvent, 99SB in explicit solvent and 99SB in implicit solvent cannot. The helix propensities calculated under 14SB agree with the corresponding experimental values, while the 99SB results obviously deviate from the references. Hence, implicit solvent models are unable to correctly describe the thermodynamics even for the simple helix formation in isolated peptides. Well-developed force fields and explicit solvents are needed to correctly describe the protein dynamics. Aside from the free energy, differences in conformational ensemble under different force fields in different solvent models are observed. The numbers of hydrogen bonds formed under different force fields agree and they are mostly determined by the solvent model.

In this work, the calculated electron density difference, Bader charge analysis and the density of states (DOS) of SnO₂–TiO₂-nanotubes (NTs) indicate that the electrons are transferred from the Ti atoms of TiO₂ into the O atoms of (SnO${}_2{)}_6$ in SnO₂–TiO₂-NTs and the supported (SnO${}_2{)}_6$ cluster acts as the role of storage for photogenerated electrons excited from TiO₂-NTs, which is in good agreement with experimental results that the SnO₂–TiO₂-NTs composite films have higher photocurrent density for photocathodic protection of 304 stainless steel (304SS). The theoretical investigations provide a plausible explanation for the photoelectrochemical anticorrosion mechanism of SnO₂–TiO₂-NTs using periodic density functional theory (DFT).

A theoretical study on the reaction of phloroglucinol with ^•OH has been performed with the aim of elucidating the geometric, energetic and kinetic properties of the reaction as well as identifying the preferred reaction pathway. Three reaction mechanisms have been considered, namely, direct hydrogen atom abstraction, addition–elimination mechanism in the absence and in the presence of a base catalyst and oxidation mechanism in the absence and in the presence of O₂. The study has been performed using the DFT/M06$-$2X, DFT/BHHLYP and DFT/MPW1K methods in conjunction with either the 6-31++G(d,p) or the 6-311++G(3df,2p) basis set. The energetic parameters are influenced by the type of function utilized and the media in which the calculation is done. The direct hydrogen abstraction mechanism provides the smallest branching ratio with respect to the ^•OH addition mechanisms. The PG + ^•OH reaction under atmospheric conditions saturated with O₂ would predominantly form tetrahydroxybenzene; the predominant product within the biological system would largely depend on physiological conditions; under pH $\sim$ 7 and with oxygen dissolved within the biological system, the preferred product would be tetrahydroxybenzene; however, if the reaction takes place in some part of the biological system where the pH $>$ 7, the preferred product would be the phenoxyl radical.