Computational identification of potential inhibitors targeting cdk1 in colorectal cancer

Efficient and easy gene expression and genetic variation data analysis and visualization using exvar

RNA sequencing data manipulation workflows are complex and require various skills and tools. This creates the need for user-friendly and integrated genomic data analysis and visualization tools. We developed a novel R package using multiple Cran and Bioconductor packages to perform gene expression analysis and genetic variant calling from RNA sequencing data. Multiple public datasets were analyzed using the developed package to validate the pipeline for all the supported species. The developed R package, named "exvar", includes multiple data analysis functions and three data visualization shiny apps integrated as functions. Also, it could be used to analyze several species' data. The exvar package is available in the project's GitHub repository ( https://github.com/omicscodeathon/exvar ).

Targeting aldose reductase using natural African compounds as promising agents for managing diabetic complications

Background

Diabetes remains a leading cause of morbidity and mortality due to various complications induced by hyperglycemia. Inhibiting Aldose Reductase (AR), an enzyme that converts glucose to sorbitol, has been studied to prevent long-term diabetic consequences. Unfortunately, drugs targeting AR have demonstrated toxicity, adverse reactions, and a lack of specificity. This study aims to explore African indigenous compounds with high specificity as potential AR inhibitors for pharmacological intervention.

Methodology

A total of 7,344 compounds from the AfroDB, EANPDB, and NANPDB databases were obtained and pre-filtered using the Lipinski rule of five to generate a compound library for virtual screening against the Aldose Reductase. The top 20 compounds with the highest binding affinity were selected. Subsequently, in silico analyses such as protein-ligand interaction, physicochemical and pharmacokinetic profiling (ADMET), and molecular dynamics simulation coupled with binding free energy calculations were performed to identify lead compounds with high binding affinity and low toxicity.

Results

Five natural compounds, namely, (+)-pipoxide, Zinc000095485961, Naamidine A, (-)-pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside, were identified as potential inhibitors of aldose reductase. Molecular docking results showed that these compounds exhibited binding energies ranging from -12.3 to -10.7 kcal/mol, which were better than the standard inhibitors (zopolrestat, epalrestat, IDD594, tolrestat, and sorbinil) used in this study. The ADMET and protein-ligand interaction results revealed that these compounds interacted with key inhibiting residues through hydrogen and hydrophobic interactions and demonstrated favorable pharmacological and low toxicity profiles. Prediction of biological activity highlighted Zinc000095485961 and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside as having significant inhibitory activity against aldose reductase. Molecular dynamics simulations and MM-PBSA analysis confirmed that the compounds bound to AR exhibited high stability and less conformational change to the AR-inhibitor complex.

Conclusion

This study highlighted the potential inhibitory activity of 5 compounds that belong to the African region: (+)-Pipoxide, Zinc000095485961, Naamidine A, (-)-Pipoxide, and 1,6-di-o-p-hydroxybenzoyl-beta-d-glucopyranoside. These molecules inhibiting the aldose reductase, the key enzyme of the polyol pathway, can be developed as therapeutic agents to manage diabetic complications. However, we recommend in vitro and in vivo studies to confirm our findings.

Identification and validation of CDK1 as a promising therapeutic target for Eriocitrin in colorectal cancer: a combined bioinformatics and experimental approach

BACKGROUND

Colorectal cancer (CRC) is a prevalent malignancy worldwide, associated with significant morbidity and mortality. Cyclin-dependent kinase 1 (CDK1) plays a crucial role in cell cycle regulation and has been implicated in various cancers. This study aimed to evaluate the prognostic value of CDK1 in CRC and to identify traditional Chinese medicines (TCM) that can target CDK1 as potential treatments for CRC.

METHODS

The expression and prognostic value of CDK1 were analyzed through TCGA, GEO, GEPIA, UALCAN and HPA databases. An ESTIMATE analysis was applied to estimate the proportions of stromal and immune cells in tumor samples. GO and KEGG enrichment analyses were performed to clarify the functional roles of CDK1-related genes. CCK-8, colony formation, cell migration, cell invasion, and wound healing assays were employed to explore tumor-promoting role of CDK1. Molecular docking, cellular thermal shift, and isothermal dose-response assays were employed to identify potential inhibitors of CDK1.

RESULTS

CDK1 was highly expressed in CRC and associated with a poorer prognosis. The expression of CDK1 was also correlated with the levels of immune cells infiltration. CDK1-related genes were primarily involved in the cell cycle and the P53 signaling pathway. Knockdown of CDK1 inhibited the proliferation, migration, and invasion of CRC cells in vitro. Furthermore, Eriocitrin emerged as a potential inhibitor, exerting its anti-tumor effects by targeting and inhibiting CDK1 activity.

CONCLUSION

CDK1 plays a critical role in CRC prognosis. Eriocitrin, a potential CDK1 inhibitor derived from TCM, highlights a promising new therapeutic strategy for CRC treatment.

Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment

Introduction

Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure. TGR5 agonists are attractive candidates for T2D therapy because of their ability to improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.

Methods

Bioactivity data for known TGR5 agonists were obtained from the ChEMBL database. The dataset was cleaned and molecular descriptors based on Lipinski's rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. The optimized ML model was used to screen the COCONUT database and predict potential TGR5 agonists based on their molecular features. 6,656 compounds predicted from the COCONUT database were docked within the active site of TGR5 to calculate their binding energies. The four top-scoring compounds with the lowest binding energies were selected and their activities were compared to those of the co-crystallized ligand. A 100 ns MDS was used to assess the binding stability of the compounds to TGR5.

Results

Molecular docking results showed that the lead compounds had a stronger affinity for TGR5 than the cocrystallized ligand. MDS revealed that the lead compounds were stable within the TGR5 binding pocket.

Discussion

The combination of ML, MD, and MDS provides a powerful approach for predicting new TGR5 agonists that can be optimised for T2D treatment.

Machine learning and molecular docking prediction of potential inhibitors against dengue virus

Introduction

Dengue Fever continues to pose a global threat due to the widespread distribution of its vector mosquitoes, Aedes aegypti and Aedes albopictus. While the WHO-approved vaccine, Dengvaxia, and antiviral treatments like Balapiravir and Celgosivir are available, challenges such as drug resistance, reduced efficacy, and high treatment costs persist. This study aims to identify novel potential inhibitors of the Dengue virus (DENV) using an integrative drug discovery approach encompassing machine learning and molecular docking techniques.

Method

Utilizing a dataset of 21,250 bioactive compounds from PubChem (AID: 651640), alongside a total of 1,444 descriptors generated using PaDEL, we trained various models such as Support Vector Machine, Random Forest, k-nearest neighbors, Logistic Regression, and Gaussian Naïve Bayes. The top-performing model was used to predict active compounds, followed by molecular docking performed using AutoDock Vina. The detailed interactions, toxicity, stability, and conformational changes of selected compounds were assessed through protein-ligand interaction studies, molecular dynamics (MD) simulations, and binding free energy calculations.

Results

We implemented a robust three-dataset splitting strategy, employing the Logistic Regression algorithm, which achieved an accuracy of 94%. The model successfully predicted 18 known DENV inhibitors, with 11 identified as active, paving the way for further exploration of 2683 new compounds from the ZINC and EANPDB databases. Subsequent molecular docking studies were performed on the NS2B/NS3 protease, an enzyme essential in viral replication. ZINC95485940, ZINC38628344, 2',4'-dihydroxychalcone and ZINC14441502 demonstrated a high binding affinity of -8.1, -8.5, -8.6, and -8.0 kcal/mol, respectively, exhibiting stable interactions with His51, Ser135, Leu128, Pro132, Ser131, Tyr161, and Asp75 within the active site, which are critical residues involved in inhibition. Molecular dynamics simulations coupled with MMPBSA further elucidated the stability, making it a promising candidate for drug development.

Conclusion

Overall, this integrative approach, combining machine learning, molecular docking, and dynamics simulations, highlights the strength and utility of computational tools in drug discovery. It suggests a promising pathway for the rapid identification and development of novel antiviral drugs against DENV. These in silico findings provide a strong foundation for future experimental validations and in-vitro studies aimed at fighting DENV.

Prostruc: an open-source tool for 3D structure prediction using homology modeling

Introduction

Homology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical for understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise and computational resources, presenting a barrier for many researchers.

Methods

Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. Integrating Biopython for sequence alignment, BLAST for template identification, and ProMod3 for structure generation, Prostruc streamlines complex workflows into a user-friendly interface. The tool enables researchers to input protein sequences, identify homologous templates from databases such as the Protein Data Bank (PDB), and generate high-quality 3D structures with minimal computational expertise. Prostruc implements a two-stage vSquarealidation process: first, it uses TM-align for structural comparison, assessing Root Mean Deviations (RMSD) and TM scores against reference models. Second, it evaluates model quality via QMEANDisCo to ensure high accuracy.

Results

The top five models are selected based on these metrics and provided to the user. Prostruc stands out by offering scalability, flexibility, and ease of use. It is accessible via a cloud-based web interface or as a Python package for local use, ensuring adaptability across research environments. Benchmarking against existing tools like SWISS-MODEL,I-TASSER and Phyre2 demonstrates Prostruc's competitive performance in terms of structural accuracy and job runtime, while its open-source nature encourages community-driven innovation.

Discussion

Prostruc is positioned as a significant advancement in homology modeling, making high-quality protein structure prediction more accessible to the scientific community.

NeuroVar: an open-source tool for the visualization of gene expression and variation data for biomarkers of neurological diseases

The expanding availability of large-scale genomic data and the growing interest in uncovering gene-disease associations call for efficient tools to visualize and evaluate gene expression and genetic variation data. Here, we developed a comprehensive pipeline that was implemented as an interactive Shiny application and a standalone desktop application. NeuroVar is a tool for visualizing genetic variation (single nucleotide polymorphisms and insertions/deletions) and gene expression profiles of biomarkers of neurological diseases. Data collection involved filtering biomarkers related to multiple neurological diseases from the ClinGen database. NeuroVar provides a user-friendly graphical user interface to visualize genomic data and is freely accessible on the project's GitHub repository (https://github.com/omicscodeathon/neurovar).

Synthesis and biological evaluation of novel benzothiazole derivatives as potential anticancer and antiinflammatory agents

Introduction: Cancer, a significant global health concern, necessitates innovative treatments. The pivotal role of chronic inflammation in cancer development underscores the urgency for novel therapeutic strategies. Benzothiazole derivatives exhibit promise due to their distinctive structures and broad spectrum of biological effects. This study aims to explore new anti-tumor small molecule drugs that simultaneously anti-inflammatory and anticancer based on the advantages of benzothiazole frameworks. Methods: The compounds were characterized by nuclear magnetic resonance (NMR), liquid chromatograph-mass spectrometer (LC-MS) and high performance liquid chromatography (HPLC) for structure as well as purity and other related physicochemical properties. The effects of the compounds on the proliferation of human epidermoid carcinoma cell line (A431) and human non-small cell lung cancer cell lines (A549, H1299) were evaluated by MTT method. The effect of compounds on the expression levels of inflammatory factors IL-6 and TNF-α in mouse monocyte macrophages (RAW264.7) was assessed using enzyme-linked immunosorbent assay (ELISA). The effect of compounds on apoptosis and cell cycle of A431 and A549 cells was evaluated by flow cytometry. The effect of compounds on A431 and A549 cell migration was evaluated by scratch wound healing assay. The effect of compounds on protein expression levels in A431 and A549 cells was assessed by Western Blot assay. The physicochemical parameters, pharmacokinetic properties, toxicity and drug similarity of the active compound were predicted using Swiss ADME and admetSAR web servers. Results: Twenty-five novel benzothiazole compounds were designed and synthesized, with their structures confirmed through spectrogram verification. The active compound 6-chloro-N-(4-nitrobenzyl) benzo[d] thiazol-2-amine (compound B7) was screened through a series of bioactivity assessments, which significantly inhibited the proliferation of A431, A549 and H1299 cancer cells, decreased the activity of IL-6 and TNF-α, and hindered cell migration. In addition, at concentrations of 1, 2, and 4 μM, B7 exhibited apoptosis-promoting and cell cycle-arresting effects similar to those of the lead compound 7-chloro-N-(2, 6-dichlorophenyl) benzo[d] thiazole-2-amine (compound 4i). Western blot analysis confirmed that B7 inhibited both AKT and ERK signaling pathways in A431 and A549 cells. The prediction results of ADMET indicated that B7 had good drug properties. Discussion: This study has innovatively developed a series of benzothiazole derivatives, with a focus on compound B7 due to its notable dual anticancer and anti-inflammatory activities. B7 stands out for its ability to significantly reduce cancer cell proliferation in A431, A549, and H1299 cell lines and lower the levels of inflammatory cytokines IL-6 and TNF-α. These results position B7B7 as a promising candidate for dual-action cancer therapy. The study's mechanistic exploration, highlighting B7's simultaneous inhibition of the AKT and ERK pathways, offers a novel strategy for addressing both the survival mechanisms of tumor cells and the inflammatory milieu facilitating cancer progression.