Computational drug discovery provides an efficient tool for helping large-scale lead molecule screening. One of the major tasks of lead discovery is identifying molecules with promising binding affinities toward a target, a protein in general. The accuracies of current scoring functions that are used to predict the binding affinity are not satisfactory enough. Thus, machine learning or deep learning based methods have been developed recently to improve the scoring functions. In this study, a deep convolutional neural network model (called OnionNet) is introduced; its features are based on rotation-free element-pair-specific contacts between ligands and protein atoms, and the contacts are further grouped into different distance ranges to cover both the local and nonlocal interaction information between the ligand and the protein. The prediction power of the model is evaluated and compared with other scoring functions using the comparative assessment of scoring functions (CASF-2013) benchmark and the v2016 core set of the PDBbind database. The robustness of the model is further explored by predicting the binding affinities of the complexes generated from docking simulations instead of experimentally determined PDB structures.

Host defense cationic Antimicrobial Peptides (AMPs) can kill microorganisms including bacteria, viruses and fungi using various modes of action. The negatively charged bacterial membranes serve as a key target for many AMPs. Bacterial cell death by membrane permeabilization has been well perceived. A number of cationic AMPs kill bacteria by cell agglutination which is a distinctly different mode of action compared to membrane pore formation. However, mechanism of cell agglutinating AMPs is poorly understood. The outer membrane lipopolysaccharide (LPS) or the cell-wall peptidoglycans are targeted by AMPs as a key step in agglutination process. Here, we report the first atomic-resolution structure of thanatin, a cell agglutinating AMP, in complex with LPS micelle by solution NMR. The structure of thanatin in complex with LPS, revealed four stranded antiparallel β-sheet in a ‘head-tail’ dimeric topology. By contrast, thanatin in free solution assumed an antiparallel β-hairpin conformation. Dimeric structure of thanatin displayed higher hydrophobicity and cationicity with sites of LPS interactions. MD simulations and biophysical interactions analyses provided mode of LPS recognition and perturbation of LPS micelle structures. Mechanistic insights of bacterial cell agglutination obtained in this study can be utilized to develop antibiotics of alternative mode of action.

One key task in virtual screening is to accurately predict the binding affinity (△G) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict △G. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

Actin cables, composed of actin filament bundles nucleated by formins, mediate intracellular transport for cell polarity establishment and maintenance. We previously observed that metaphase cells preferentially promote actin cable assembly through cyclin-dependent kinase 1 (Cdk1) activity. However, the relevant metaphase Cdk1 targets were not known. Here we show that the highly conserved actin filament crosslinking protein fimbrin is a critical Cdk1 target for actin cable assembly regulation in budding yeast. Fimbrin is specifically phosphorylated on threonine 103 by the metaphase cyclin–Cdk1 complex, in vivo and in vitro. On the basis of conformational simulations, we suggest that this phosphorylation stabilizes fimbrin's N-terminal domain, and modulates actin filament binding to regulate actin cable assembly and stability in cells. Overall, this work identifies fimbrin as a key target for cell cycle regulation of actin cable assembly in budding yeast, and suggests an underlying mechanism.

The RAS-RAF-MEK1/2-ERK1/2 pathway remains one of the most commonly dysregulated pathways in human cancers, based on which several inhibitors have already been approved by the U.S. Food and Drug Administration for treatment of mutation-positive melanomas since 2011.Despite the success of RAF-MEK1/2-ERK1/2 inhibitors, their efficacies are compromised by the emerging drug resistance and dose-limiting side effects.

Spectroscopic properties of as-grown and gamma-irradiated undoped and Bi-doped alpha-BBO (BaB(2)O(4)) single crystals were investigated. Bi(2+) and color centers in Bi:alpha-BBO crystals were investigated to be nonluminescent in the near-infrared (NIR) region. Broadband NIR luminescence at 1139 nm with a FWHM of 108 nm and a decay time of 526 mus was realized in Bi:alpha-BBO crystal through gamma irradiation. Bi(+) was attributed to be responsible for the NIR emission, which can be bleached by thermal annealing. The involved physical processes in Bi:alpha-BBO crystal during the courses of irradiation and heat annealing were tentatively established.

No abstract