PUMC-MB1 is a novel group 3 medulloblastoma preclinical model, sensitive to PI3K/mTOR dual inhibitor.

PURPOSE: Medulloblastoma (MB), a common and heterogeneous posterior fossa tumor in pediatric patients, presents diverse prognostic outcomes. To advance our understanding of MB's intricate biology, the development of novel patient tumor-derived culture MB models with necessary data is still an essential requirement. METHODS: We continuously passaged PUMC-MB1 in vitro in order to establish a continuous cell line. We examined the in vitro growth using Cell Counting Kit-8 (CCK-8) and in vivo growth with subcutaneous and intracranial xenograft models. The xenografts were investigated histopathologically with Hematoxylin and Eosin (HE) staining and immunohistochemistry (IHC). Concurrently, we explored its molecular features using Whole Genome Sequencing (WGS), targeted sequencing, and RNA sequecing. Guided by bioinformatics analysis, we validated PUMC-MB1's drug sensitivity in vitro and in vivo. RESULTS: PUMC-MB1, derived from a high-risk MB patient, displayed a population doubling time (PDT) of 48.18 h and achieved 100% tumor growth in SCID mice within 20 days. HE and Immunohistochemical examination of the original tumor and xenografts confirmed the classification of PUMC-MB1 as a classic MB. Genomic analysis via WGS revealed concurrent MYC and OTX2 amplifications. The RNA-seq data classified it within the Group 3 MB subgroup, while according to the WHO classification, it fell under the Non-WNT/Non-SHH MB. Comparative analysis with D283 and D341med identified 4065 differentially expressed genes, with notable enrichment in the PI3K-AKT pathway. Cisplatin, 4-hydroperoxy cyclophosphamide/cyclophosphamide, vincristine, and dactolisib (a selective PI3K/mTOR dual inhibitor) significantly inhibited PUMC-MB1 proliferation in vitro and in vivo. CONCLUSIONS: PUMC-MB1, a novel Group 3 (Non-WNT/Non-SHH) MB cell line, is comprehensively characterized for its growth, pathology, and molecular characteristics. Notably, dactolisib demonstrated potent anti-proliferative effects with minimal toxicity, promising a potential therapeutic avenue. PUMC-MB1 could serve as a valuable tool for unraveling MB mechanisms and innovative treatment strategies.

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.

Discovery of non-sulfonamide carbonic anhydrase IX inhibitors through structure-based virtual screening.

Carbonic anhydrase IX (CA IX) is a subtype of the human carbonic anhydrase (hCA) family and exhibits high expression in various solid tumors, rendering it a promising target for tumor therapy. Currently, marketed carbonic anhydrase inhibitors (CAIs) are primarily composed of sulfonamides derivatives, which may have impeded their potential for further expansion. Therefore, we have developed a structure-based virtual screening approach to explore novel CAIs exhibiting distinctive structures and anti-tumor potential in the FDA database. In vitro experiments demonstrated that 3-pyridinemethanol (0.42 µM), procodazole (8.35 µM) and pamidronic acid (8.51 µM) exhibited inhibitory effects on CA IX activity. The binding stability and interaction mode between the CA IX and the hit compounds are further investigated through molecular dynamics simulations and binding free energy calculations. Furthermore, the ADME/Tox prediction results indicated that these compounds exhibited favorable pharmacological properties and minimal toxic side effects. Our study successfully applied computational strategies to discover three non-sulfonamide inhibitors of carbonic anhydrase IX (CA IX) that demonstrate inhibitory activity in vitro. These findings have significant implications for the development of CA IX inhibitors and anti-tumor drugs, contributing to their progress in the field.

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Considering the safety and druggability of FDA-approved drugs, virtual screening of the database was performed to discover Mcl1 inhibitors, and the hit was subsequently validated via TR-FRET, cytotoxicity, and flow cytometry assays. To reveal the binding characteristics shared by the hit and a typical Mcl1 selective inhibitor, we employed quantum mechanics and molecular mechanics (QM/MM) calculations, umbrella sampling, and metadynamics in this work. The combined studies suggested that fluvastatin had promising cell inhibitory potency and was suitable for further investigation. We believe that this research will shed light on the discovery of novel Mcl1 inhibitors that can be used as a supplemental treatment against leukemia and provide a possible method to improve the accuracy of drug repurposing with limited computational resources while balancing the costs of experimentation well.

Uncovering the selectivity mechanism of phosphodiesterase 7A/8A inhibitors through computational studies.

The expression of phosphodiesterase 7A (PDE7A) and phosphodiesterase 8A (PDE8) genes is integral to human signaling pathways, and the inhibition of PDE7A has been associated with the onset of various diseases, including effects on the immune system and nervous system. The development of PDE7 selective inhibitors can promote research on immune and nervous system diseases, such as multiple sclerosis, chronic inflammation, and autoimmune responses. PDE8A is expressed alongside PDE8B, and its inhibitory mechanism is still unclear. Studying the mechanisms of selective inhibitors against different PDE subtypes is crucial to prevent potential side effects, such as nausea and cardiac toxicity, and the sequence similarity of the two protein subtypes was 55.9%. Therefore, it is necessary to investigate the differences of both subtypes' ligand binding sites. Selective inhibitors of two proteins were chosen to summarize the reason for their selectivity through molecular docking, molecular dynamics simulation, alanine scanning mutagenesis, and MM-GBSA calculation. We found that Phe384PDE7A, Leu401PDE7A, Gln413PDE7A, Tyr419PDE7A, and Phe416PDE7A in the active site positively contribute to the selectivity towards PDE7A. Additionally, Asn729PDE8A, Phe767PDE8A, Gln778PDE8A, and Phe781PDE8A positively contribute to the selectivity towards PDE8A.

Selectivity mechanism of muscarinic acetylcholine receptor antagonism through in silico investigation.

Structures of muscarinic acetylcholine receptors illustrate the strikingly high degree of homology of the residues among isoforms, thus leading to difficulty in achieving subtype selectivity when targeting these receptors and causing undesired side effects when treating the corresponding diseases. Considering the urgent need for more selective and potency therapies, this study is aimed at revealing the selectivity mechanism against M4/5 via in silico strategies, revealing crucial molecular interactions such as hydrogen bonds and pi-cation interaction formed between the key residues TYR416, ASN417, and TRP435 of M4, respectively, hydrophobic pocket formed by the key residues, especially CYS484 of M5. Besides, the water around TYR416M4 and ASN459M5, which can be replaced by substituent groups which can form the hydrogen bond interaction network by simulated bridging water and the water around ASP112M4, whose replacement maybe not contribute to the increase in binding affinity of the compound, may affect the inhibitory selectivity among M4/5 in the aspect of the solvent. Moreover, from the point of inhibitors, compounds with a positively ionizable group could selectively bind to M4 receptors, while hydrophobic molecules may bind preferably to M5. We believe that the current study would provide a basis for the design of subsequent M4/5 selective antagonists.

cPKCγ-mediated down-regulation of UCHL1 alleviates ischaemic neuronal injuries by decreasing autophagy via ERK-mTOR pathway.

Stroke is one of the leading causes of death in the world, but its underlying mechanisms remain unclear. Both conventional protein kinase C (cPKC)γ and ubiquitin C-terminal hydrolase L1 (UCHL1) are neuron-specific proteins. In the models of 1-hr middle cerebral artery occlusion (MCAO)/24-hr reperfusion in mice and 1-hr oxygen-glucose deprivation (OGD)/24-hr reoxygenation in cortical neurons, we found that cPKCγ gene knockout remarkably aggravated ischaemic injuries and simultaneously increased the levels of cleaved (Cl)-caspase-3 and LC3-I proteolysis product LC3-II, and the ratio of TUNEL-positive cells to total neurons. Moreover, cPKCγ gene knockout could increase UCHL1 protein expression via elevating its mRNA level regulated by the nuclear factor κB inhibitor alpha (IκB-α)/nuclear factor κB (NF-κB) pathway in cortical neurons. Both inhibitor and shRNA of UCHL1 significantly reduced the ratio of LC3-II/total LC3, which contributed to neuronal survival after ischaemic stroke, but did not alter the level of Cl-caspase-3. In addition, UCHL1 shRNA reversed the effect of cPKCγ on the phosphorylation levels of mTOR and ERK rather than that of AMPK and GSK-3ß. In conclusion, our results suggest that cPKCγ activation alleviates ischaemic injuries of mice and cortical neurons through inhibiting UCHL1 expression, which may negatively regulate autophagy through ERK-mTOR pathway.

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening.

Virtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. Notably, FaissMolLib employs the highly efficient Faiss search algorithm, outperforming the Tanimoto algorithm in identifying similar molecules with its tighter clustering in scatter plots and lower mean, standard deviation, and variance in key molecular properties. This feature enables FaissMolLib to screen 2.8 million compounds in just 0.05 seconds, offering researchers an efficient, easily deployable solution for virtual screening on laptops and building unique compound databases. This significant advancement holds great potential for accelerating drug discovery efforts and enhancing chemical data analysis. FaissMolLib is freely available at http://liuhaihan.gnway.cc:80. The code and dataset of FaissMolLib are freely available at https://github.com/Superhaihan/FiassMolLib.

Computational characteristics of the structure-activity relationship of inhibitors targeting Pks13-TE domain.

Multiple studies have established the Pks13-TE domain as a promising target for anti-tuberculosis drug development. However, recent findings have revealed that the lead compound currently in the pipeline for Pks13-TE has significant cardiotoxicity issues. Given the pressing need for new chemical structures for Pks13-TE inhibitors, this study aims to provide a detailed understanding of the Pks13-TE domain binding site through the application of computational chemical biology techniques. Our results highlight the size and shape of the Pks13-TE domain binding pocket, key residues including Asp1644, Asn1640, Phe1670, and Tyr1674 within the pocket, and inhibitor pharmacophore characteristics such as aromatic ring sites, positively charged sites, and hydrogen bond donors. To our knowledge, these simulation results are novel and contribute to the discovery of next-generation Pks13-TE inhibitors without similar prior studies.

Structural requirement of RARγ agonism through computational aspects.

CONTEXT: RARγ is a therapeutic target for many skin diseases and has potential in cancer treatment. In the current study, we put forward a comprehensive structure-activity relationship study of third and fourth generations of RARγ agonists, addressing multiple crystal structures of RARγ complexes and approved drugs. Adapalene and Trifarotene, through hybrid strategies including protein contacts Atlas analysis, molecular docking, dynamics simulations, MM-GBSA, ASM, and pharmacophore modeling. Our result revealed crucial amino acids Arg267, Ser278, Phe288, Phe230, Met272, Leu271, and Leu268 within the RARγ pocket, as well as pharmacophore features such as two hydrophobic groups, two aromatic rings, and negative ionic features, which are essential for the binding of RARγ agonists. Based on this study, the binding mechanism of RARγ agonists was elucidated, which will be helpful for the rational design of new RARγ agonists for skin diseases and cancer treatment. METHODS: In this study, Schrödinger suite 2021-2 with OPLS_4 force field, Discovery Studio program 3.0, LigandScout 4.3, and PyMOL are utilized in the investigation.

Selectivity mechanism of GRK2/5 inhibition through in silico investigation.

As two representative isoforms of G protein-coupled receptor kinases family, the largest known membrane receptor family, GRK2 and GRK5 are ubiquitously distributed in human heart, brain, lung, kidney, skeletal muscle and other tissues. GRK2 and GRK5 have common functions implicated in the regulation of heart failure, though GRK5 has also been involved in diseases like hypertension, cancer, diabetes and Alzheimer's disease. Therefore, to clarify the selectivity mechanism towards GRK2 and GRK5 will be of great significance for the discovery of effective and selective inhibitors. To this end, the structures and chemical properties of key residues were analyzed among GRK2 and GRK5 derived from their respective protein crystal structures. Furthermore, a combination of multiple computational strategies, including sequence superposition, receptor-ligand docking, molecular dynamics, MM-GBSA calculation, QM/MM approach and pharmacological modeling, were integrated to validated and elucidate their unique binding modes towards highly selective inhibitors. In addition, the specific amino acid distribution within the GRK2/5 target site is also analyzed in this paper, which can guide future research and development of selective inhibitors in a more targeted manner. Overall, our study comprehensively clarifies the selectivity mechanism of GRK2/5 inhibition, thereby providing guidance for further rational design of selective inhibitors targeting GRK2/5.