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Background: Tau assembly produces soluble oligomers and insoluble neurofibrillary tangles, which are neurotoxic to the brain and associated with Alzheimer’s and Parkinson’s diseases. Therefore, preventing tau aggregation is a promising therapy for those neurodegenerative disorders. Objective: The aim of this study was to develop a joint computational/cell-based oligomerization protocol for screening inhibitors of tau assembly. Methods: Virtual oligomerization inhibition (VOI) experiment using molecular dynamics simulation was performed to screen potential oligomerization inhibitors of PHF6 hexapeptide. Tau seeding assay, which is directly related to the outcome of therapeutic intervention, was carried out to confirm a ligand’s ability in inhibiting tau assembly formation. Results: Our protocol was tested on two known compounds, EGCG and Blarcamesine. EGCG inhibited both the aggregation of PHF6 peptide in VOI and tau assembly in tau seeding assay, while Blarcamesine was not a good inhibitor at the two tasks. We also pointed out that good binding affinity to tau aggregates is needed, but not sufficient for a ligand to become a good inhibitor of tau oligomerization. Conclusion: VOI goes beyond traditional computational inhibitor screening of amyloid aggregation by directly examining the inhibitory ability of a ligand to tau oligomerization. Comparing with the traditional biochemical assays, tau seeding activities in cells is a better indicator for the outcome of a therapeutic intervention. Our hybrid protocol has been successfully validated. It can effectively and efficiently identify the inhibitors of amyloid oligomerization/aggregation processes, thus, facilitate to the drug development of tau-related neurodegenerative diseases. 

Resistance to antibiotics is a serious and worsening threat to human health worldwide, and there is an urgent need to develop new antibiotics that can avert it. One possible solution is the development of compounds that possess multiple modes of action, requiring at least two mutations to acquire resistance. Compound SCH-79797 both avoids resistance and has two mechanisms of action: one inhibiting the folate pathway, and a second described as “membrane permeabilization”; however, the mechanism by which membranes from bacterial cells, but not the host, are disrupted has remained mysterious. The opening of the bacterial mechanosensitive channel of large conductance, MscL, which ordinarily serves the physiological role of osmotic emergency release valves gated by hypoosmotic shock, has been previously demonstrated to affect bacterial membrane permeabilization. MscL allows the rapid permeabilization of both ions and solutes through the opening of the largest known gated pore, which has a diameter of 30 Å. We found that SCH-79797 and IRS-16, a more potent derivative, directly bind to the MscL channel and produce membrane permeabilization as a result of its activation. These findings suggest that possessing or adding an MscL-activating component to an antibiotic compound could help to lower toxicity and evade antibiotic resistance. 

Metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which are involved in neuropathy and periphery homeostasis. mGluR4, which belongs to Group III mGluRs, is most widely distributed in the periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate the mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue–ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSEs) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGluRs. Thus, machine learning-based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery projects. 

MscL is a highly conserved mechanosensitive channel found in the majority of bacterial species, including pathogens. It functions as a biological emergency release valve, jettisoning solutes from the cytoplasm upon acute hypoosmotic stress. It opens the largest known gated pore and has been heralded as an antibacterial target. Although there are no known endogenous ligands, small compounds have recently been shown to specifically bind to and open the channel, leading to decreased cell growth and viability. Their binding site is at the cytoplasmic/membrane and subunit interfaces of the protein, which has been recently been proposed to play an essential role in channel gating. Here, we have targeted this pocket using in silico screening, resulting in the discovery of a new family of compounds, distinct from other known MscL-specific agonists. Our findings extended the study of this functional region, the progression of MscL as a viable drug target, and demonstrated the power of in silico screening for identifying and improving the design of MscL agonists. 

The Arf-family GTPases can switch on a central actin regulator named the WAVE Complex to promote actin polymerization.