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.

Exploring new frontiers: cell surface vimentin as an emerging marker for circulating tumor cells and a promising therapeutic target in advanced gastric Cancer.

BACKGROUND: Circulating tumor cells (CTCs) hold immense promise in guiding treatment strategies for advanced gastric cancer (GC). However, their clinical impact has been limited due to challenges in identifying epithelial-mesenchymal transition (EMT)-CTCs using conventional methods. METHODS: To bridge this knowledge gap, we established a detection platform for CTCs based on the distinctive biomarker cell surface vimentin (CSV). A prospective study involving 127 GC patients was conducted, comparing CTCs enumeration using both EpCAM and CSV. This approach enabled the detection of both regular and EMT-CTCs, providing a comprehensive analysis. Spiking assays and WES were employed to verify the reliability of this marker and technique. To explore the potential inducer of CSV+CTCs formation, a combination of Tandem Mass Tag (TMT) quantitative proteomics, m6A RNA immunoprecipitation-qPCR (MeRIP-qPCR), single-base elongation- and ligation-based qPCR amplification method (SELECT) and RNA sequencing (RNA-seq) were utilized to screen and confirm the potential target gene. Both in vitro and in vivo experiments were performed to explore the molecular mechanism of CSV expression regulation and its role in GC metastasis. RESULTS: Our findings revealed the potential of CSV in predicting therapeutic responses and long-term prognosis for advanced GC patients. Additionally, compared to the conventional EpCAM-based CTCs detection method, the CSV-specific positive selection CTCs assay was significantly better for evaluating the therapeutic response and prognosis in advanced GC patients and successfully predicted disease progression 14.25 months earlier than radiology evaluation. Apart from its excellent role as a detection marker, CSV emerges as a promising therapeutic target for attenuating GC metastasis. It was found that fat mass and obesity associated protein (FTO) could act as a potential catalyst for CSV+CTCs formation, and its impact on the insulin-like growth factor-I receptor (IGF-IR) mRNA decay through m6A modification. The activation of IGF-I/IGF-IR signaling enhanced the translocation of vimentin from the cytoplasm to the cell surface through phosphorylation of vimentin at serine 39 (S39). In a GC mouse model, the simultaneous inhibition of CSV and blockade of the IGF-IR pathway yielded promising outcomes. CONCLUSION: In summary, leveraging CSV as a universal CTCs marker represents a significant breakthrough in advancing personalized medicine for patients with advanced GC. This research not only paves the way for tailored therapeutic strategies but also underscores the pivotal role of CSV in enhancing GC management, opening new frontiers for precision medicine.

Insights into the selective mechanism of PDE2/9a inhibitors from silico aspects.

The selective design of competitive enzyme inhibitors is an extremely difficult task but necessary work for certain types of systems, such as the phosphodiesterase (PDE) system addressed in this article. In the PDE family, PDE2A and PDE9 respectively target the central nervous system and heart failure, and share many conserved amino acids at their binding sites. Therefore, gaining a deep understanding of the selective mechanisms of PDE2A/9A is crucial for designing highly selective drugs. In this study, various computer-aided drug design (CADD) methods, including molecular docking, molecular dynamics simulations (MD), and binding free energy calculations, are employed to explore the selective mechanisms of PDE2A/9A. Overall, our research results indicate a selective design strategy for PDE2A, which involves incorporating hydrophobic or aromatic moieties into the molecular structure to better accommodate the hydrophobic pocket of PDE2A. Additionally, it is recommended to introduce functional groups capable of forming connections with selective residues, such as Phe830 and Gln812 for PDE2A, or Ala452 and Tyr424 for PDE9A, to enhance the selectivity of inhibitors targeting PDE2A/9A. This achievement is anticipated to pave the way for the development of innovative and selective small molecules targeting PDE2A/9A.Communicated by Ramaswamy H. Sarma.

Effect of probiotics on pregnancy outcomes in gestational diabetes: systematic review and meta-analysis.

OBJECTIVE: Gestational diabetes mellitus (GDM) is a prevalent complication during pregnancy associated with numerous adverse outcomes. There is emerging evidence suggesting the potential of probiotics as a therapeutic measure for GDM; however, existing studies have yielded contradictory results. This meta-analysis assessed the efficacy of probiotics on blood glucose management and pregnancy outcomes in patients with GDM. METHODS: A comprehensive search of the PubMed, Embase, and Cochrane databases was conducted up to August 22, 2023, to identify relevant studies. The primary outcomes focused on fasting blood glucose (FBG), fasting serum insulin (FSI), homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI). The secondary outcomes included various maternal and neonatal outcomes. RESULTS: This meta-analysis included 15 randomized controlled trials (RCTs), encompassing 1006 patients with GDM. The results showed that, compared to a placebo, probiotics demonstrated a significant reduction in FBG (MD - 2.58, 95% CI - 4.38 to - 0.79, p < 0.01), FSI (MD - 2.29, 95% CI - 3.40 to - 1.18, p < 0.01), HOMA-IR (MD - 0.56, 95% CI - 0.81 to - 0.32, p < 0.01), and birth weight (MD - 101.20, 95% CI - 184.62 to - 17.77, p = 0.02). Furthermore, it resulted in fewer neonatal intensive care unit (NICU) admissions (RR 0.60, 95% CI 0.40-0.89, p = 0.01), instances of hyperbilirubinemia (RR 0.31, 95% CI 0.16-0.61, p < 0.01), and elevated QUICKI (MD 0.01, 95% CI 0.00-0.01, p < 0.01). No significant impact was observed in the other analyzed outcomes. CONCLUSIONS: In conclusion, probiotics improve FBG, FSI, and HOMA-IR, and reduce the occurrence of neonatal hyperbilirubinemia, NICU admissions, and birth weight in the offspring of patients with GDM. However, the quality of the evidence, as per the GRADE approach, varies from high to low, necessitating further studies to consolidate these findings.

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.

Insights into ß3-adrenoceptor agonism through comprehensive in silico investigation.

Research onß3-AR, the new member of the adrenoceptor family, is in its infancy and few ß3-AR agonists have been approved for marketing to date. Meanwhile, ß3-AR exhibited obvious species differences in pharmacological properties, such as between human and animals, however, the 3D structure of human ß3-AR has not been published, which makes it difficult to understand the interaction between human ß3-AR and its agonists. Herein, binding patterns of ß3-AR agonists are explored starting from the Alphafold predicted structural model, and the obtained model was optimized by using molecular dynamics simulations. Moreover, the human ß3-AR and its agonists were subjected to molecular docking, dynamics simulations, binding free energy calculations and pharmacophore modeling to elucidate the characteristics of human ß3-AR activity pockets and agonist conformational relationships, including a hydrophobic group, a positively charged group as well as two hydrogen-bonded donors, which provide comprehensive insights into the interactions between human ß3-AR and its agonists.

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 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.

Selectivity mechanism of BCL-XL/2 inhibition through in silico investigation.

The BCL-XL protein is among the most important members of the anti-apoptotic subfamily of the BCL-2 protein family, and is currently a promising new target for anti-tumor drug research. However, the BCL-XL/2 proteins have similar structures and functions, which could lead to undesirable side effects because of inhibitors that can bind to both BCL-XL and BCL-2. Therefore, it is crucial to expound on the structural basis of the selective mechanism towards BCL-XL/2 inhibition. In the current study, we employed hybrid computational methods including molecular docking and dynamics simulation, MM/GBSA energy calculation, alanine scanning mutagenesis and Hirshfeld surface analysis to comprehensively reveal the selectivity mechanism towards BCL-XL/2 from multiple perspectives, revealing the significant effects of the BCL-XL residues SER106 and LEU108 as well as the BCL-2 residue ASP103 on the inhibitory selectivity. Overall, our findings provide useful references for the rational design of BCL-XL/2 selective inhibitors with better affinity.