Computational Biology and Chemistry最新文献

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.

Metisa plana is a widespread insect pest infesting oil palm plantations in Malaysia. Farnesyl acetate (FA), a juvenile hormone analogue, has been reported to exert in vitro and in vivo insecticidal activity against other insect pests. However, the insecticidal mechanism of FA on M. plana remains unclear. Therefore, this study aims to elucidate responsive genes in M. plana in response to FA treatment. The RNA-sequencing reads of FA-treated M. plana were de novo-assembled with existing raw reads from non-treated third instar larvae, and 55,807 transcripts were functionally annotated to multiple protein databases. Several insecticide detoxification-related genes were differentially regulated among the 321 differentially expressed transcripts. Cytochrome P450 monooxygenase, carboxylesterase, and ATP-binding cassette protein were upregulated, while peptidoglycan recognition protein was downregulated. Innate immune response genes, such as glutathione S-transferases, acetylcholinesterase, and heat shock protein, were also identified in the transcriptome. The findings signify that changes occurred in the insect’s receptor and signaling, metabolic detoxification of insecticides, and immune responses upon FA treatment on M. plana. This valuable information on FA toxicity may be used to formulate more effective biorational insecticides for better M. plana pest management strategies in oil palm plantations.

Sclerostin (SOST), a Wnt signaling pathway inhibitor, is involved in the pathogenesis of skeletal disorders. This study investigated the impact of the GKWWRPS motif on the PNAIG motif in Loop 2 of SOST, which is accountable for the interactions with the LRP6 protein that triggers the down-regulation of the Wnt signaling pathway. Single amino acid mutations on the GKWWRPS motif, hypothesized to have a probable stabilization effect towards the PNAIG motif, led to a significant reduction in the primary interactions between the SOST and LRP6 proteins. Protein-protein docking and molecular dynamic studies were conducted to investigate the role of the motif. The study found that a solitary mutation in the GKWWRPS motif significantly reduced the primary interactions between SOST and LRP6 proteins, except for probable cold-spot residues. The study's findings establish the GKWWRPS motif as a promising target for therapeutic interventions. Based on the obtained results, it can be inferred that alterations implemented within the GKWWRPS motif could lead to the destabilization of the PNAIG motif, which would directly modulate the interactions between the SOST and LRP6 proteins. The present investigation thus presents novel opportunities in the field of anti-sclerostin interventions.

Gastric cancer (GC) is a leading cause of cancer-related mortality and is characterized by significant heterogeneity, highlighting the need for further studies aimed at personalized treatment strategies. Tumor angiogenesis is critical for tumor development and metastasis, yet its role in molecular subtyping and prognosis prediction remains underexplored. This study aims to identify angiogenesis-related subtypes and develop a prognostic model for GC patients. Using data from The Cancer Genome Atlas (TCGA), we performed consensus cluster analysis on differentially expressed angiogenesis-related genes (ARGs), identifying two patient subtypes with distinct survival outcomes. Differentially expressed genes between the subtypes were analyzed via Cox and LASSO regression, leading to the establishment of a subtype-based prognostic model using a machine learning algorithm. Patients were classified into high- and low-risk groups based on the risk score. Validation was performed using independent datasets (ICGC and GSE15459). We utilized a deconvolution algorithm to investigate the tumor immune microenvironment in different risk groups and conducted analyses on genetic profiling, sensitivity and combination of anti-tumor drug. Our study identified ten prognostic signature genes, enabling the calculation of a risk score to predict prognosis and overall survival. This provides critical data for stratified diagnosis and treatment upon patient admission, monitoring disease progression throughout the entire course, evaluating immunotherapy efficacy, and selecting personalized medications for GC patients.

Fungal secondary metabolites have a long history of contributing to pharmaceuticals, notably in the development of antibiotics and immunosuppressants. Harnessing their potent bioactivities, these compounds are now being explored for cancer therapy, by targeting and disrupting the genes that induce cancer progression. The current study explores the anticancer potential of gliotoxin, a fungal secondary metabolite, which encompasses a multi-faceted approach integrating computational predictions, molecular dynamics simulations, and comprehensive experimental validations. In-silico studies have identified potential gliotoxin targets, including MAPK1, NFKB1, HIF1A, TDP1, TRIM24, and CTSD which are involved in critical pathways in cancer such as the NF-κB signaling pathway, MAPK/ERK signaling pathway, hypoxia signaling pathway, Wnt/β-catenin pathway, and other essential cellular processes. The gene expression analysis results indicated all the identified targets are overexpressed in various breast cancer subtypes. Subsequent molecular docking and dynamics simulations have revealed stable binding of gliotoxin with TDP1 and HIF1A. Cell viability assays exhibited a dose-dependent decreasing pattern with its remarkable IC₅₀ values of 0.32, 0.14, and 0.53 μM for MDA-MB-231, MDA-MB-468, and MCF-7 cells, respectively. Likewise, in 3D tumor spheroids, gliotoxin exhibited a notable decrease in viability indicating its effectiveness against solid tumors. Furthermore, gene expression studies using Real-time PCR revealed a reduction of expression of cancer-inducing genes, MAPK1, HIF1A, TDP1, and TRIM24 upon gliotoxin treatment. These findings collectively underscore the promising anticancer potential of gliotoxin through multi-targeting cancer-promoting genes, positioning it as a promising therapeutic option for breast cancer.

Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due to its role in regulating invasion and egress from host cells. While potent Pyrazolopyrimidine analogs have been identified as candidate hit molecules, they exhibit limitations in inhibiting Cryptosporidium growth in cell culture, prompting exploration of alternative scaffolds. Leveraging the most potent compound, RM-1–95, co-crystallized with CpCDPK1, an E-pharmacophore model was generated and validated alongside a deep learning model trained on known CpCDPK1 compounds. These models facilitated screening Enamine's 2 million HTS compound library for novel CpCDPK1 inhibitors. Subsequent hierarchical docking prioritized hits, with final selections subjected to Quantum polarized docking for accurate ranking. Results from docking studies and MD simulations highlighted similarities in interactions between the cocrystallized ligand RM-1–95 and identified hit molecules, indicating comparable inhibitory potential against CpCDPK1. Furthermore, assessing metabolic stability through Cytochrome 450 site of metabolism prediction offered crucial insights for drug design, optimization, and regulatory approval processes.

Classification of protein families from their sequences is an enduring task in Proteomics and related studies. Numerous deep-learning models have been moulded to tackle this challenge, but due to the black-box character, they still fall short in reliability. Here, we present a novel explainability pipeline that explains the pivotal decisions of the deep learning model on the classification of the Eukaryotic kinome. Based on a comparative and experimental analysis of the most cutting-edge deep learning algorithms, the best deep learning model CNN-BLSTM was chosen to classify the eight eukaryotic kinase sequences to their corresponding families. As a substitution for the conventional class activation map-based interpretation of CNN-based models in the domain, we have cascaded the GRAD CAM and Integrated Gradient (IG) explainability modus operandi for improved and responsible results. To ensure the trustworthiness of the classifier, we have masked the kinase domain traces, identified from the explainability pipeline and observed a class-specific drop in F1-score from 0.96 to 0.76. In compliance with the Explainable AI paradigm, our results are promising and contribute to enhancing the trustworthiness of deep learning models for biological sequence-associated studies.

Background and aims

Pancreatic ductal adenocarcinoma (PDAC) is infrequent. Currently, non-invasive biomarkers for early detection of PDAC are not accessible. Here, we intended to identify a set of urine markers able to discriminate patients with early-stage PDAC from healthy individuals.

Patients and methods

Seventy-five urine samples from PDAC patients and 50 healthy controls were assayed using quantitative real-time PCR (qPCR). The chosen biomarkers were lymphatic vessel endothelial HA receptor (LYVE-1), regenerating islet-derived 1 alpha (REG1A), and trefoil factor family (TFF1).

Results

LYVE-1, REG1A, and TFF1 expression in PDAC proved to be significantly elevated compared to healthy individuals (p < 0.05). Determination of these markers' expression might be useful for early tumor diagnosis with a sensitivity of 96 %, 100 %, and 73.33 % respectively, and a specificity of 100 %, 82 %, and 100 % respectively.

Conclusion

We have recognized three diagnostic biomarkers REG1A, TFF1, and LYVE1 that can detect patients with early-stage pancreatic cancer in non-invasive urine specimens with improved sensitivity and specificity. To the best of our knowledge, there have been no prior investigations examining the mRNA expression levels of them in urine within the Egyptian population.

Despite progress in diagnosis and treatment strategies, breast cancer remains a primary risk to female health as indicated by second most cancer-deaths globally caused by this cancer. High risk mutation is linked to prognosis of breast cancer. Due to high resistance of breast cancer against current therapies, there is necessity of novel treatment strategies. Sirtuins are signaling proteins belonging to histone deacetylase class III family, known to control several cellular processes. Therefore, targeting sirtuins could be one of the approaches to treat breast cancer. Several plants synthesize phytoestrogens which exhibit structural and physiological similarities to estrogens and have been recognized to possess anticancer activity. In our study, we investigated several phytoestrogens for sirtuin inhibition by conducting molecular docking studies, and in-vitro studies against breast cancer cell lines. In molecular docking studies, we identified coumestrol possessing high binding energy with sirtuin proteins 1–3 as compared to other phytoestrogens. The molecular dynamic studies showed stable interaction of ligand and protein with higher affinity at sirtuin proteins 1–3 binding sites. In cell proliferation assay and colony formation assay using breast cancer cell lines (MCF-7 and MDAMB-231) coumestrol caused significant reduction in cell proliferation and number of colonies formed. Further, the flow cytometric analysis showed that coumestrol induces intracellular reactive oxygen species and the western blot analysis revealed reduction in the level of SIRT-1 expression in breast cancer cell lines. In conclusion, in-silico data and in-vitro studies suggest that the phytoestrogen coumestrol has sirtuin inhibitory activity against breast cancer.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility.