Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Design, Synthesis, and Characterization of Novel Styryl Dyes as Fluorescent Probes for Tau Aggregate Detection in Vitro and in Cells

A series of novel styryl dye derivatives incorporating indolium and quinolinium core structures were successfully synthesized to explore their interacting and binding capabilities with tau aggregates in vitro and in cells. The synthesized dyes exhibited enhanced fluorescence emission in viscous environments due to the rotatable bond confinement in the core structure. Dye 4, containing a quinolinium moeity and featuring two cationic sites, demonstrated a 28-fold increase in fluorescence emission upon binding to tau aggregates. This dye could also stain tau aggregates in living cells, confirmed by cell imaging using confocal fluorescence microscopy. A molecular docking study was conducted to provide additional visualization and support for binding interactions. This work offers novel and non-cytotoxic fluorescent probes with desirable photophysical properties, which could potentially be used for studying tau aggregates in living cells, prompting further development of new fluorescent probes for early Alzheimer's disease detection.

FMO-guided design of darunavir analogs as HIV-1 protease inhibitors

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

HLA-B*46:01:01:01 and HLA-DRB1*09:01:02:01 are associated with anti-rHuEPO-induced pure red cell aplasia

Treatment of anemia in patients with chronic kidney disease (CKD) with recombinant human erythropoietin (rHuEPO) can be disrupted by a severe complication, anti-rHuEPO-induced pure red cell aplasia (PRCA). Specific HLA genotypes may have played a role in the high incidence of PRCA in Thai patients (1.7/1,000 patient years vs. 0.03/10,000 patient years in Caucasians). We conducted a case-control study in 157 CKD patients with anti-rHuEPO-induced PRCA and 56 controls. The HLA typing was determined by sequencing using a highly accurate multiplex single-molecule, real-time, long-read sequencing platform. Four analytical models were deployed: Model 1 (additive: accounts for the number of alleles), Model 2 (dominant: accounts for only the presence or absence of alleles), Model 3 (adjusted additive with rHuEPO types) and Model 4 (adjusted dominant with rHuEPO types). HLA-B*46:01:01:01 and DRB1*09:01:02:01 were found to be independent risk markers for anti-rHuEPO-induced PRCA in all models [OR (95%CI), p-values for B*46:01:01:01: 4.58 (1.55-13.51), 0.006; 4.63 (1.56-13.75), 0.006; 5.72 (1.67-19.67), 0.006; and 5.81 (1.68-20.09), 0.005; for DRB1*09:01:02:01: 3.99 (1.28-12.49), 0.017, 4.50 (1.32-15.40), 0.016, 3.42 (1.09-10.74), 0.035, and 3.75 (1.08-13.07), 0.038, in Models 1-4, respectively. HLA-B*46:01:01:01 and DRB1*09:01:02:01 are susceptible alleles for anti-rHuEPO-induced PRCA. These findings support the role of HLA genotyping in helping to monitor patients receiving rHuEPO treatment.

Multi-Level Biological Network Analysis and Drug Repurposing Based on Leukocyte Transcriptomics in Severe COVID-19: In Silico Systems Biology to Precision Medicine

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein–protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug–gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug–gene and drug–protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

Chondroprotective effects of purple corn anthocyanins on advanced glycation end products induction through suppression of NF-κB and MAPK signaling

Formation of advanced glycation end products (AGEs), which are associated with diabetes mellitus, contributes to prominent features of osteoarthritis, i.e., inflammation-mediated destruction of articular cartilage. Among the phytochemicals which play a role in anti-inflammatory effects, anthocyanins have also been demonstrated to have anti-diabetic properties. Purple corn is a source of three major anthocyanins: cyanidin-3-O-glucoside, pelargonidin-3-O-glucoside and peonidin-3-O-glucoside. Purple corn anthocyanins have been demonstrated to be involved in the reduction of diabetes-associated inflammation, suggesting that they may have a beneficial effect on diabetes-mediated inflammation of cartilage. This investigation of the chondroprotective effects of purple corn extract on cartilage degradation found a reduction in glycosaminoglycans released from AGEs induced cartilage explants, corresponding with diminishing of uronic acid loss of the cartilage matrix. Investigation of the molecular mechanisms in human articular chondrocytes showed the anti-inflammatory effect of purple corn anthocyanins and the metabolite, protocatechuic acid (PCA) on AGEs induced human articular chondrocytes via inactivation of the NFκb and MAPK signaling pathways. This finding suggests that purple corn anthocyanins and PCA may help ameliorate AGEs mediated inflammation and diabetes-mediated cartilage degradation.