The nucleoside analog 4'-fluorouridine suppresses the replication of multiple enteroviruses by targeting 3D polymerase.

Human enteroviruses are the major pathogens causing hand-foot-and-mouth disease in infants and young children throughout the world, and infection with enterovirus is also associated with severe complications, such as aseptic meningitis and myocarditis. However, there are no antiviral drugs available to treat enteroviruses infection at present. In this study, we found that 4'-fluorouridine (4'-FlU), a nucleoside analog with low cytotoxicity, exhibited broad-spectrum activity against infections of multiple enteroviruses with EC50 values at low micromolar levels, including coxsackievirus A10 (CV-A10), CV-A16, CV-A6, CV-A7, CV-B3, enterovirus A71 (EV-A71), EV-A89, EV-D68, and echovirus 6. With further investigation, the results indicated that 4'-FlU directly interacted with the RNA-dependent RNA polymerase of enterovirus, the 3D pol, and impaired the polymerase activity of 3D pol, hence inhibiting viral RNA synthesis and significantly suppressing viral replication. Our findings suggest that 4'-FlU could be promisingly developed as a broad-spectrum direct-acting antiviral agent for anti-enteroviruses therapy.

The new direction of drug development: Degradation of undruggable targets through targeting chimera technology.

Imbalances in protein and noncoding RNA levels in vivo lead to the occurrence of many diseases. In addition to the use of small molecule inhibitors and agonists to restore these imbalances, recently emerged targeted degradation technologies provide a new direction for disease treatment. Targeted degradation technology directly degrades target proteins or RNA by utilizing the inherent degradation pathways, thereby eliminating the functions of pathogenic proteins (or RNA) to treat diseases. Compared with traditional therapies, targeted degradation technology which avoids the principle of traditional inhibitor occupation drive, has higher efficiency and selectivity, and widely expands the range of drug targets. It is one of the most promising and hottest areas for future drug development. Herein, we systematically introduced the in vivo degradation systems applied to degrader design: ubiquitin-proteasome system, lysosomal degradation system, and RNA degradation system. We summarized the development progress, structural characteristics, and limitations of novel chimeric design technologies based on different degradation systems. In addition, due to the lack of clear ligand-binding pockets, about 80% of disease-associated proteins cannot be effectively intervened with through traditional therapies. We deeply elucidated how to use targeted degradation technology to discover and design molecules for representative undruggable targets including transcription factors, small GTPases, and phosphatases. Overall, this review provides a comprehensive and systematic overview of targeted degradation technology-related research advances and a new guidance for the chimeric design of undruggable targets.

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling.

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2.

In tauopathies such as Alzheimer's disease (AD), aberrant phosphorylation causes the dissociation of tau proteins from microtubules. The dissociated tau then aggregates into sequent forms from soluble oligomers to paired helical filaments and insoluble neurofibrillary tangles (NFTs). NFTs is a hallmark of AD, while oligomers are found to be the most toxic form of the tau aggregates. Therefore, understanding tau oligomerization with regard to abnormal phosphorylation is important for the therapeutic development of AD. In this study, we investigated the impact of phosphorylated Ser289, one of the 40 aberrant phosphorylation sites of full-length tau proteins, on monomeric and dimeric structures of tau repeat R2 peptides. We carried out intensive replica exchange molecular dynamics simulation with a total simulation time of up to 0.1 ms. Our result showed that the phosphorylation significantly affected the structures of both the monomer and the dimer. For the monomer, the phosphorylation enhanced ordered-disordered structural transition and intramolecular interaction, leading to more compactness of the phosphorylated R2 compared to the wild-type one. As to the dimer, the phosphorylation increased intermolecular interaction and ß-sheet formation, which can accelerate the oligomerization of R2 peptides. This result suggests that the phosphorylation at Ser289 is likely to promote tau aggregation. We also observed a phosphorylated Ser289-Na+-phosphorylated Ser289 bridge in the phosphorylated R2 dimer, suggesting an important role of cation ions in tau aggregation. Our findings suggest that phosphorylation at Ser289 should be taken into account in the inhibitor screening of tau oligomerization.

CMOS-compatible, broadband, and polarization-independent edge coupler for efficient chip coupling with standard single-mode fiber.

A CMOS-compatible, broadband, and polarization-independent edge coupler for efficient chip coupling with standard single-mode fiber is proposed. Three layers of a silicon nitride waveguide array with the same structures are used in the top oxide cladding of the chip to achieve high coupling efficiency and to simplify the mode transformation structure. Optimal total coupling loss at the wavelength of 1550 nm, -0.49dB for TE mode polarization and -0.92dB for TM mode polarization is obtained. The -1dB bandwidth is beyond 160 nm for TE mode polarization and ∼130nm for TM mode polarization, respectively. A significant reduction in the packaging cost of silicon photonic chips is anticipated. Meanwhile, the structure holds vast potential for on-chip three-dimensional photonic integrations or fiber-to-chip, chip-to-chip optical interconnections.