A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding.

In machine learning and data analysis, dimensionality reduction and high-dimensional data visualization can be accomplished by manifold learning using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this manifold learning scheme by introducing a preprocessing strategy for the t-SNE algorithm. In our preprocessing, we exploit Laplacian eigenmaps to reduce the high-dimensional data first, which can aggregate each data cluster and reduce the Kullback-Leibler divergence (KLD) remarkably. Moreover, the k-nearest-neighbor (KNN) algorithm is also involved in our preprocessing to enhance the visualization performance and reduce the computation and space complexity. We compare the performance of our strategy with that of the standard t-SNE on the MNIST dataset. The experiment results show that our strategy exhibits a stronger ability to separate different clusters as well as keep data of the same kind much closer to each other. Moreover, the KLD can be reduced by about 30% at the cost of increasing the complexity in terms of runtime by only 1-2%.

Study on the binding mechanism of thiamethoxam with three model proteins:spectroscopic studies and theoretical simulations.

As a top-selling neonicotinoid insecticide widely used in the field, thiamethoxam is an environmental pollutant because of the accumulation in ecosystem and has also been reported that it has potential risks to the health of mammals even humans. In order to understand the binding mechanism of thiamethoxam with biological receptors, spectroscopic techniques and theoretical simulations was used to explore the specific interactions between thiamethoxam and proteins. Interestingly, the results indicated that hydrophobic interaction as the main driving force, thiamethoxam formed a single binding site complex with proteins spontaneously, resulting in a decrease in the esterase-like activity of human serum albumin. The results of computer simulation showed that there were hydrophobic, electrostatic and hydrogen bonding interactions between thiamethoxam and receptors. The results of experiment and computer simulation were mutually confirmed, so a model was established for the interaction between the two which uncovered the structural characteristics of the binding site. This research provided new insights for the structure optimization of thiamethoxam, as well as gave an effective reference for evaluating the risk of thiamethoxam systemically in the future.

Identification of novel insect ß-N-acetylhexosaminidase OfHex1 inhibitors based on virtual screening, biological evaluation, and molecular dynamics simulation.

Chitin can be widely found in the fungal cell wall, nematode eggshells, and the exoskeleton of arthropods; however, it is completely absent from higher plants and mammals. The process of chitin degradation is essential for both growth and maturation of insects. Thus, inhibiting chitin degradation is a promising strategy for the control and management of pests. The chitinolytic ß-N-acetyl-D-hexosaminidase OfHex1 of Ostrinia furnacalis (one of the most destructive pests) has been suggested as a potential target for the design of eco-friendly pesticides. This study presents the sequential virtual screening of the ZINC library with 8 million compounds, targeting OfHex1. After confirmation via enzyme inhibition experiments, compound 5 exhibited potential inhibitory activity against OfHex1 with a Ki of 28.9 ± 0.5 µM and significant selectivity (IC50 > 100 µM against HsHexB and hOGA). Molecular dynamics simulations combined with binding free energy and free energy decomposition calculations were conducted to investigate the molecular basis underlying the potency of these inhibitors toward OfHex1. The present work provides useful information for the future rational design of novel and potent OfHex1 inhibitorsCommunicated by Ramaswamy H. Sarma.

APAn, a Class of ABA Receptor Agonism/Antagonism Switching Probes.

In plants, biosynthesized ABA undergoes two important physiological processes of signal transduction and metabolism simultaneously. In this study, we described a class of ABA receptor agonist/antagonist switching probes APAn, which can regulate the agonistic activity or antagonistic activity according to the length of a 6'-alkoxyl chain. From APA1 to APA6, with the extension of the alkoxyl chain, it showed a gradually increased receptor-binding potential and decreased HAB1 inhibition activity. Theoretical analysis based on molecular docking and molecular dynamics simulation revealed that some factors outside the ligand-binding pocket in receptors could also affect the binding of the ligand to the receptor, for example, the van der Waals interaction between the alkyl chain in APAn and the 3'-tunnel of ABA receptors made it bind more tightly than iso-PhABA. This enhanced binding made it an antagonist rather than a weakened agonist.

Computational and experimental investigations on the interactions of aryloxy-phenoxy-propionate herbicides to estrogen receptor alpha in zebrafish.

When the amount of pesticide exceeds the self-purification ability of the environment, it will be enriched in the human body through the atmosphere, soil, water circulation, etc., threatening human health. Aryloxy-phenoxy-propionate (APP) herbicides are a class of acetyl-CoA carboxylase (ACCase) inhibitor herbicides, widely used in field-weeding of soybean, cabbage, peanut and other crops. However, due to the water circulation, surface runoff and the agronomic practices such as watering irrigation, APP herbicides have the risk of polluting water and destroying the living environment of aquatic organisms. In this paper, a multistep framework combining homology modeling, molecular docking and molecular dynamic simulations were adopted to explore the interactions between APP herbicides and zebrafish estrogen receptor α (ERα) to investigate the estrogenic activities of the herbicides. The structure of zebrafish ERα was modeled by homology modeling, using the human's estrogen receptor α (PDB ID:2YJA) as the template. Then, eight typical APP herbicides were selected to dock with the zebrafish ERα, and it was determined that there were clear interactions between the herbicides and the receptor. The binding patterns of Quizalofop-P-ethyl (QPE), Clodinafop-propargyl (CP) and Haloxyfop-P (HP) with ERα were further investigated by molecular dynamics and binding free energy calculation. The results showed the van der Waals force and electrostatic force were the main driving forces for maintaining the stability of the complex system. In order to verify the theoretical prediction, an exposed experiment was conducted to study the effects of different concentrations of herbicides on VTG level of zebrafish in vivo and the results were consistent with the computational method. The results of this study revealed the mechanism of the action between APP herbicides and zebrafish estrogen receptors, and also provided ideas for optimizing the herbicides.

Development of sodium glucose co-transporter 2 (SGLT2) inhibitors with novel structure by molecular docking and dynamics simulation.

In this study, molecular docking studies were carried out to explore the binding interactions of sodium glucose co-transporter 2 (SGLT2) with its inhibitors. A correlation between the docking scores and the experimental bioactivity was observed (R2 = 0.8368, N = 24). The new inhibitors were designed using the 3D quantitative structure activity relationship (3D-QSAR) method, and the activities were predicted by the docking method. In order to understand the structure-activity correlation of compound 1 m (the highest score of docking) and compound 1 t (the lowest score), we carried out a combined molecular dynamics simulation and MM-GBSA method. It was found that, in the system of SGLT2-1 m, the interaction between Gln271 and Val272 exhibited significant effects, which were absent in the SGLT2-1 t system. This study is expected to shed light on the mechanism of action of compound 1 m, leading to development of active drug candidates targeting SGLT2.

Computational Studies on the Potency and Selectivity of PUGNAc Derivatives Against GH3, GH20, and GH84 ß-N-acetyl-D-hexosaminidases.

ß-N-acetyl-D-hexosaminidases have attracted significant attention due to their crucial role in diverse physiological functions including antibacterial synergists, pathogen defense, virus infection, lysosomal storage, and protein glycosylation. In particular, the GH3 ß-N-acetyl-D-hexosaminidase of V. cholerae (VcNagZ), human GH20 ß-N-acetyl-D-hexosaminidase B (HsHexB), and human GH84 ß-N-acetyl-D-hexosaminidase (hOGA) are three important representative glycosidases. These have been found to be implicated in ß-lactam resistance (VcNagZ), lysosomal storage disorders (HsHexB) and Alzheimer's disease (hOGA). Considering the profound effects of these three enzymes, many small molecule inhibitors with good potency and selectivity have been reported to regulate the corresponding physiological functions. In this paper, the best-known inhibitors PUGNAc and two of its derivatives (N-valeryl-PUGNAc and EtBuPUG) were selected as model compounds and docked into the active pockets of VcNagZ, HsHexB, and hOGA, respectively. Subsequently, molecular dynamics simulations of the nine systems were performed to systematically compare their binding modes from active pocket architecture and individual interactions. Furthermore, the binding free energy and free energy decomposition are calculated using the MM/GBSA methods to predict the binding affinities of enzyme-inhibitor systems and to quantitatively analyze the contribution of each residue. The results show that PUGNAc is deeply-buried in the active pockets of all three enzymes, which indicates its potency (but not selectivity) against VcNagZ, HsHexB, and hOGA. However, EtBuPUG, bearing branched 2-isobutamido, adopted strained conformations and was only located in the active pocket of VcNagZ. It has completely moved out of the pocket of HsHexB and lacks interactions with HsHexB. This indicates why the selectivity of EtBuPUG to VcNagZ/HsHexB is the largest, reaching 968-fold. In addition, the contributions of the catalytic residue Asp253 (VcNagZ), Asp254 (VcNagZ), Asp175 (hOGA), and Asp354 (HsHexB) are important to distinguish the activity and selectivity of these inhibitors. The results of this study provide a helpful structural guideline to promote the development of novel and selective inhibitors against specific ß-N-acetyl-D-hexosaminidases.

miR-125b Contributes to Ovarian Granulosa Cell Apoptosis Through Targeting BMPR1B, a Major Gene for Sheep Prolificacy.

Bone morphogenetic protein receptor 1B (BMPR1B) is a major gene for ovine ovulation rate and litter size, which plays a crucial role in follicle development. However, its role and regulation in the ovine granulosa cells (GCs) are unclear. Here, we showed that silencing of BMPR1B enhanced cell apoptosis, whereas overexpression of BMPR1B inhibited cell apoptosis in the ovine GCs. Luciferase reporter assays revealed that BMPR1B is a target of miR-125b, and miR-125b reduced BMPR1B messenger RNA and protein levels in the ovine GCs by directly binding to the 3' untranslated region of the ovine BMPR1B gene. Furthermore, miR-125b enhanced cell apoptosis by attenuating BMPR1B in the ovine GCs. Our data demonstrated that BMPR1B is an important factor in the ovine GC function in vitro and targeted by a specific regulator miR-125b.