Abstract: BACKGROUND: Rehmanniae Radix Preparata (RRP) can effectively improve the symptoms of osteoporosis, but its molecular mechanism for treating osteoporosis is still unclear. The objective of this study is to investigate the anti-osteoporosis mechanisms of RRP through network pharmacology. METHODS: The overlapping targets of RRP and osteoporosis were screened out using online platforms. A visual network diagram of PPI was constructed and analyzed by Cytoscape 3.7.2 software. Molecular docking was used to evaluate the binding activity of ligands and receptors, and some key genes were verified through pharmacological experiments. RESULTS: According to topological analysis results, AKT1, MAPK1, ESR1, and SRC are critical genes for RRP to treat osteoporosis, and they have high binding activity with stigmasterol and sitosterol. The main signal pathways of RRP in the treatment of osteoporosis, including the estrogen signaling pathway, HIF-1 signal pathway, MAPK signal pathway, PI3K-Akt signal pathway. Results of animal experiments showed that RRP could significantly increase the expression levels of Akt1, MAPK1, ESR1, and SRC1 mRNA in bone tissue to increase bone density. CONCLUSION: This study explained the coordination between multiple components and multiple targets of RRP in the treatment of osteoporosis and provided new ideas for its clinical application and experimental research.

Abstract: Rubber composed of highly unsaturated hydrocarbons, modified through addition of chemicals and vulcanization are widely used to date. However, the usage of rubber, faces many obstacles. These elastomeric materials are difficult to be re-used and recovered, leading to high post-consumer waste and vast environmental problems. Tyres, the major rubber waste source can take up to 80 years to naturally degrade. Experiments show that the latex clearing proteins (Lcp) found in Actinobacteria were reportedly critical for the initial oxidative cleavage of poly(cis-1,4-isoprene), the major polymeric unit of rubber. Although, more than 100 rubber degrading strains have been reported, only 8 Lcp proteins isolated from Nocardia (3), Gordonia (2), Streptomyces (1), Rhodococcus (1), and Solimonas (1) have been purified and biochemically characterized. Previous studies on rubber degrading strains and Lcp enzymes, implied that they are distinct. Following this, we aim to discover additional rubber degrading strains by randomly screening 940 Actinobacterial strains isolated from various locations in Sarawak on natural rubber (NR) latex agar. A total of 18 strains from 5 genera produced clearing zones on NR latex agar, and genes encoding Lcp were identified. We report here lcp genes from Microtetraspora sp. AC03309 (lcp1 and lcp2) and Dactylosporangium sp. AC04546 (lcp1, lcp2, lcp3), together with the predicted genes related to rubber degradation. In silico analysis suggested that Microtetraspora sp. AC03309 is a distinct species closely related to Microtetraspora glauca while Dactylosporangium sp. AC04546 is a species closely related to Dactylosporangium sucinum. Genome-based characterization allowed the establishment of the strains taxonomic position and provided insights into their metabolic potential especially in biodegradation of rubber. Morphological changes and the spectrophotometric detection of aldehyde and keto groups indicated the degradation of the original material in rubber samples incubated with the strains. This confirms the strains' ability to utilize different rubber materials (fresh latex, NR product and vulcanized rubber) as the sole carbon source. Both strains exhibited different levels of biodegradation ability. Findings on tyre utilization capability by Dactylosporangium sp. AC04546 is of interest. The final aim is to find sustainable rubber treatment methods to treat rubber wastes.

Abstract: MOTIVATION: The acid dissociation constant (pKa) is a critical parameter to reflect the ionization ability of chemical compounds and is widely applied in a variety of industries. However, the experimental determination of pKa is intricate and time-consuming, especially for the exact determination of micro pKa information at the atomic level. Hence, a fast and accurate prediction of pKa values of chemical compounds is of broad interest. RESULTS: Here, we compiled a large scale pKa dataset containing 16595 compounds with 17489 pKa values. Based on this dataset, a novel pK a prediction model, named Graph-pKa, was established using graph neural networks. Graph-pKa performed well on the prediction of macro pK a values, with a mean absolute error around 0.55 and a coefficient of determination around 0.92 on the test dataset. Furthermore, combining multi-instance learning, Graph-pKa was also able to automatically deconvolute the predicted macro pKa into discrete micro pK a values. AVAILABILITY: The Graph-pK a model is now freely accessible via a web-based interface (https://pka.simm.ac.cn/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Trastuzumab and pertuzumab are monoclonal antibodies used in the treatment of human epidermal growth factor receptor-2 (HER2)-positive breast cancer. Therapeutic proteins may undergo chemical modifications that may affect the results of bioanalytical assays, as well as their therapeutic efficacy. Modifications may arise during production and storage, as well as after administration to patients. Studying in vivo biotransformation of monoclonal, therapeutic antibodies requires their enrichment from plasma to discriminate them from endogenous antibodies, as well as from other plasma proteins. To this end, we screened Affimer reagents for selectivity toward trastuzumab or pertuzumab. Affimer reagents are alternative binding proteins possessing two variable binding loops that are based on the human protease inhibitor stefin A or phytocystatin protein scaffolds. Affimer reagents were selected from an extensive library by phage display. The four best-performing binders for each therapeutic antibody were prioritized using a microtiter plate-based approach combined with liquid chromatography-mass spectrometry (LC-MS) in the selected reaction monitoring (SRM) mode. These Affimer reagents were immobilized via engineered 6-His or Cys tags to Ni(2+)- or maleimide beads, respectively. Recovery values of 70% and higher were obtained for both trastuzumab and pertuzumab when spiked at 100, 150, and 200 mug/mL concentrations in human plasma followed by trypsin digestion in the presence of 0.5% sodium deoxycholate and 10 mM dithiothreitol (DTT). Notably, the maleimide beads showed undetectable unspecific binding to endogenous immunoglobulin G (IgGs) or other plasma proteins when analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). The enrichment method was applied to samples from stress tests of the antibodies at 37 degrees C to mimic in vivo conditions.

Abstract: Abnormal activation of epidermal growth factor receptor (EGFR) drives non-small cell lung cancer (NSCLC) development. EGFR mutations-mediated resistance to tyrosine-kinase inhibitors (TKIs) is a major hurdle for NSCLC treatment. Here, we show that F-box protein FBXL2 targets EGFR and EGFR TKI-resistant mutants for proteasome-mediated degradation, resulting in suppression of EGFR-driven NSCLC growth. Reduced FBXL2 expression is associated with poor clinical outcomes of NSCLC patients. Furthermore, we show that glucose-regulated protein 94 (Grp94) protects EGFR from degradation via blockage of FBXL2 binding to EGFR. Moreover, we have identified nebivolol, a clinically used small molecule inhibitor, that can upregulate FBXL2 expression to inhibit EGFR-driven NSCLC growth. Nebivolol in combination with osimertinib or Grp94-inhibitor-1 exhibits strong inhibitory effects on osimertinib-resistant NSCLC. Together, this study demonstrates that the FBXL2-Grp94-EGFR axis plays a critical role in NSCLC development and suggests that targeting FBXL2-Grp94 to destabilize EGFR may represent a putative therapeutic strategy for TKI-resistant NSCLC.

Abstract: SCovid (http://bio-annotation.cn/scovid) aims at providing a comprehensive resource of single-cell data for exposing molecular characteristics of coronavirus disease 2019 (COVID-19) across 10 human tissues. COVID-19, an epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been found to be accompanied with multiple-organ failure since its first report in Dec 2019. To reveal tissue-specific molecular characteristics, researches regarding to COVID-19 have been carried out widely, especially at single-cell resolution. However, these researches are still relatively independent and scattered, limiting the comprehensive understanding of the impact of virus on diverse tissues. To this end, we developed a single-cell atlas of COVID-19. Firstly we collected 21 single-cell datasets of COVID-19 across 10 human tissues paired with control datasets. Then we constructed a pipeline for the analysis of these datasets to reveal molecular characteristics of COVID-19 based on manually annotated cell types. The current version of SCovid documents 1 042 227 single cells of 21 single-cell datasets across 10 human tissues, 11 713 stably expressed genes and 3778 significant differentially expressed genes (DEGs). SCovid provides a user-friendly interface for browsing, searching, visualizing and downloading all detailed information.

Abstract: Recent fragment-based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS-CoV-2 M(pro) and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to M(pro) has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo- or small molecule fragment-bound structures of M(pro) has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS-CoV-2 M(pro) . It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC-AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

Abstract: Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the "Rule-of-Four" (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

Abstract: Naphthyridines, also known as diazanaphthalenes, are a group of heterocyclic compounds that include six isomeric bicyclic systems containing two pyridine rings. 1,6-Naphthyridines are one of the members of such a family capable of providing ligands for several receptors in the body. Among such structures, 1,6-naphthyridin-2(1H)-ones (7) are a subfamily that includes more than 17,000 compounds (with a single or double bond between C3 and C4) included in more than 1000 references (most of them patents). This review will cover the analysis of the diversity of the substituents present at positions N1, C3, C4, C5, C7, and C8 of 1,6-naphthyridin-2(1H)-ones, the synthetic methods used for their synthesis (both starting from a preformed pyridine or pyridone ring), and the biomedical applications of such compounds.

Abstract: Solubility enhancement of poorly soluble active pharmaceutical ingredients is of crucial importance for drug development and processing. Extensive experimental screening is limited due to the vast number of potential solvent combinations. Hence, theoretical models can offer valuable hints for guiding experiments aimed at providing solubility data. In this paper, we explore the possibility of applying quantum-chemistry-derived molecular descriptors, adequate for development of an ensemble of neural networks model (ENNM), for solubility computations of sulfamethizole (SMT) in neat and aqueous binary solvent mixtures. The machine learning procedure utilized information encoded in sigma-potential profiles computed using the COSMO-RS approach. The resulting nonlinear model is accurate in backcomputing SMT solubility and allowed for extensive screening of green solvents. Since the experimental characteristics of SMT solubility are limited, the data pool was extended by new solubility measurements in water, five neat organic solvents (acetonitrile, N,N-dimethylformamide, dimethyl sulfoxide, 1,4-dioxane, and methanol), and their aqueous binary mixtures at 298.15, 303.15, 308.15, and 313.15 K. Experimentally determined order of decreasing SMT solubility in neat solvents is the following: N,N-dimethylformamide > dimethyl sulfoxide > methanol > acetonitrile > 1,4dioxane >> water, in all studied temperatures. Similar trends are observed for aqueous binary mixtures. Since N,N-dimethylformamide is not considered as a green solvent, the more acceptable replacers were searched for using the developed model. This step led to the conclusion that 4-formylmorpholine is a real alternative to N,N-dimethylformamide, fulfilling all requirements of both high dissolution potential and environmental friendliness.