Accumulated evidence suggests that the in vivo biological potency of a ligand is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of the protein-ligand interaction (PLI). However, the existing experimental and computational techniques are largely insufficient and limited in large-scale measurements or accurate predictions of the kinetic properties of PLI. In this work, elaborate efforts have been made to develop interconsistent, reasonable, and predictive models of the association rate constant (k), dissociation rate constant (k), and equilibrium dissociation constant (K) of a series of HIV protease inhibitors with different structural skeletons. The results showed that nine Volsurf descriptors derived from water (OH2) and hydrophobic (DRY) probes are key molecular determinants for the kinetic and thermodynamic properties of HIV-1 protease inhibitors. To the best of our knowledge, this is the first time that interconsistent and reasonable models with strong prediction power have been established for both the kinetic and thermodynamic properties of HIV protease inhibitors.

Visible light-promoted dearomative [2 + 2] cycloaddition of indole derivatives tethered with olefins at the N1 position has been considered thermodynamically unfeasible due to the high triplet excited-state energies. We describe visible light-promoted [2 + 2] cycloaddition with concomitant dearomatization of indole derivatives tethered with olefins at the N1 position via the energy transfer process, providing cyclobutane-fused polycyclic indoline derivatives that are potentially useful in drug design and discovery. These cyclobutane-fused indoline-based polycycles are obtained in high yields and with good diastereoselectivities (>99:1). The key to the success of the reaction is the formation of H-bond(s) between N-alkenoylindole and solvent, enabling the reduction of the triplet energy of the indole derivatives, which greatly improved the efficiency of the protocol. The applicability of the method is demonstrated by late-stage skeletal diversification of indole-containing bioactive molecules, which provides a powerful strategy for the rapid skeleton remodeling. DFT calculations were used to give a deep understanding of the reaction pathways.

Flexible peptides binding to human leukocyte antigen (HLA) play a key role in mediating human immune responses and are also involved in idiosyncratic adverse drug reactions according to recent research. However, the structural determinations of pHLA complexes remain challenging under the present conditions. In this paper, the performance of a new peptide docking method, namely FlexPepDock, was systematically investigated by a benchmark of 30 crystallized structures of peptide-HLA class I complexes. The docking results showed that the near-native pHLA-I models with peptide bb-RMSD less than 2 Å were ranked in the top 1 model for 100% (70/70) docking cases, and the subangstrom models with peptide bb-RMSD less than 1 Å were ranked in the top 5 lowest-energy models for 65.7% (46/70) docking cases. Furthermore, 10 out of 70 docking cases ranked the subangstrom all-atom models in the top 5 lowest-energy models. The results showed that the FlexPepDock can generate high-quality models of pHLA-I complexes and can be widely applied to pHLA-I modeling and mechanism research of peptide-mediated immune responses.

Accumulated evidence suggests that binding kinetic properties—especially dissociation rate constant or drug-target residence time—are crucial factors affecting drug potency. However, quantitative prediction of kinetic properties has always been a challenging task in drug discovery. In this study, the VolSurf method was successfully applied to quantitatively predict the koff values of the small ligands of heat shock protein 90α (HSP90α), adenosine receptor (AR) and p38 mitogen-activated protein kinase (p38 MAPK). The results showed that few VolSurf descriptors can efficiently capture the key ligand surface properties related to dissociation rate; the resulting models demonstrated to be extremely simple, robust and predictive in comparison with available prediction methods. Therefore, it can be concluded that the VolSurf-based prediction method can be widely applied in the ligand-receptor binding kinetics and de novo drug design researches.

Previous studies have demonstrated that tannin could inhibit the proliferation and angiogenesis of cancer cells. However, the mechanism(s) associated with its antitumor effect remains unclear. Here, we investigated the effects of 3,3',4'-trimethylellagic acid (TMEA), a tannin compound isolated from Sanguisorba officinalis L., on the proliferation, angiogenesis, and apoptosis in cancer cells, as well as the underlying mechanism(s) related to its antitumor activity. TMEA was isolated from Sanguisorba officinalis L. by silica gel column chromatography. Molecular docking was carried out to assess active pocket binding between TMEA and vascular endothelial growth factor receptor 2 (VEGFR2). The antiangiogenic effect of TMEA on the migration and tube formation was detected in HUVECs by wound healing and tube formation assays, respectively. The antitumor effects of TMEA on the cell proliferation were determined in HepG2, A549, and SW620 cells by MTS assay in vitro and on the tumor growth of SW620 xenografts bearing in nude mice in vivo. The mRNA expression of Bcl-2, Bax, caspase-3, VEGF, PI3K, and mTOR were measured by qRT-PCR and protein expression of Bcl-2, Bax, caspase-3, VEGF, PI3K, and mTOR by Western blotting, and the protein expression of Bcl-2, Bax, caspase-3 and CD31 were detected by immunohistochemical analysis in vivo, respectively. The results showed that TMEA combined with VEGFR2 in the functional pockets of Asn223A, Gly922A, and Leu840A and inhibited the proliferation, migration, tube formation, and expression of VEGF and its downstream signaling mediators in HUVECs. TMEA also significantly inhibited the proliferation of HepG2, A549, and SW620 cancer cells in vitro, and suppressed the growth of SW620 tumors in vivo. Moreover, TMEA upregulated the expression of proapoptotic factors Bax and caspase-3 and downregulated the expression of antiapoptotic factors CD31 and Bcl-2 in cancer cells and/or tumor tissues. The data indicate that TMEA executes its anticancer activity by inducing

P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

Recent research has increasingly suggested that the crucial factors affecting drug potencies are related not only to the thermodynamic properties but also to the kinetic properties. Therefore, in silico prediction of ligand-binding kinetic properties, especially the dissociation rate constant (k off ), has aroused more and more attention. However, there are still a lot of challenges that need to be addressed. In this paper, steered molecular dynamics (SMD) combined with residue-based energy decomposition was employed to predict the dissociation rate constants of 37 HIV-1 protease inhibitors (HIV-1 PIs). For the first time, a predictive model of the dissociation rate constant was established by using the interaction-energy fingerprints sampled along the ligand dissociation pathway. On the basis of the key fingerprints extracted it can be inferred that the dissociation rates of 37 HIV-1 PIs are basically determined in the first half of the dissociation processes and that the H-bond interactions with active-site Asp25 and van der Waals interactions with flap-region Ile47 and Ile50 have important influences on the dissociation processes. In general, the strategy established in this paper can provide an efficient way for the prediction of dissociation rate constants as well as the unbinding mechanism research.

β-arylated ketones widely exist in many biologically active molecules and natural products. Herein, we disrcibled a photocatalytic redox-neutral arylation of cyclopropanols with cyanoarenes via radical-mediated C–C and C–CN bond cleavage...