Current Proteomics - Volume 21, Issue 6, 2024

Recent investigation revealed that arbuscular mycorrhizal fungi (AMF) brought major changes in the transcriptome of non-host plant- Arabidopsis thaliana (A. thaliana) within the AM network constructed by the hyphae of AMF connecting multiple plant roots. Although there is enormous omics data available for A. thaliana, most AM-related information has been restricted to transcriptome studies.

We aimed to provide a comprehensive toolset for analyzing AM signaling-driven molecular interactions in A. thaliana.

We developed ten modules: 1) Epigenetic regulation in protein–nucleic acid interactions (PNI), 2) DNA structure and metal binding profiles, 3) Transcription factor (TF) binding profiles, 4) Protein domain–domain interactions (DDI), 5) Profiling of protein-metal and protein-ligand interactions with complex structures (PLP) based on alignment of similar protein structures, 6) Carbohydrate-lipid-protein interactions (CLP) – analysis of lipidome-proteome interactions, N-glycosylation/glycan structure data, and carbohydrate-active enzyme/substrate predictions, 7) Metabolic pathway analysis, 8) Multiple omics association studies, 9) Gene Ontology (GO) and Plant Ontology (PO) analysis, and 10) Medicago transcriptome and epigenetic information.

For the program demonstration, we generated various comparative datasets based on differentially expressed genes (DEGs) from Arabidopsis thaliana (A. thaliana) of non-arbuscular mycorrhizal (non-AM) and arbuscular mycorrhizal (AM) phenotypes, as well as DEGs from Medicago truncatula (M. truncatula). These datasets were analyzed using statistical methods and artificial neural networks. The program demonstrated a range of advantages in studying molecular interactions related to AM symbiosis.

To aid in the inference of AM-driven changes and the identification of AM-derived molecules during AM symbiosis, the program offers a user-friendly platform for generating datasets with key features, which can then be integrated with various downstream statistical methods. The program code is freely available for download at www.artfoundation.kr.

Glycogen synthase kinase 3-beta (GSK3-β) is a protein that is linked to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles, both of which are characteristic of Alzheimer's disease. The enzyme's hyperactivity phosphorylates tau proteins, forming neurofibrillary tangles that impair material transport between axons and dendrites and disrupt neuronal function. GSK3-β impacts amyloid precursor protein production, causing Aβ accumulation and neurodegeneration.

This study aimed to investigate the inhibitory mechanism of natural compounds from Bryophyllum pinnatum against GSK3-β.

Computational approach, extra precision glide docking, and the Maestro molecular interface of Schrodinger suites were utilized to determine the binding free energy of the compounds against the prepared GSK3-β. The compounds' ADME parameters and Lipinski's rule of five were also evaluated. Using AutoQSAR, predictive models were constructed for both protein targets.

Three hit compounds (kaempferol, quercetin, and 5,7,4'-trihydroxy-3,8-dimethoxyflavone) were found in this study. These compounds met the recommended range for the specified ADME parameters and passed the rule of five. Additionally, the hit compounds' predicted pIC50 values were promising.

Our study suggests that the investigated compounds can be used to design GSK3-β inhibitors.

The application of molecular docking and Machine Learning (ML) calculations in evaluating peptide-based inhibitors allows for the systematic investigation of sequence-activity relationships, guiding the design of potent peptides with optimal binding characteristics.

This study aimed to screen short peptides using computational simulation to identify promising inhibitors against SARS-CoV-2 Mpro.

Short peptides were screened using molecular docking to identify promising candidates. The ML model was applied to confirm the docking outcome. The PreADME server was then used to analyze the HIA and toxicity of the peptides.

168,420 short peptides were docked to identify 5 tetrapeptides with promising docking scores against SARS-CoV-2 Mpro including, PYPW, WWPF, WWPY, HYPW, and WYPF. The obtained results were also confirmed via ML calculations. The analyses highlighted the importance of residues Thr190 and Asn142 that are crucial in the binding process. All of top-lead peptides adopt low toxicity and can be absorbed via the human intestine. They can also cross the blood brain barier.

This work enhances our understanding of Mpro interactions and informs future ligand design, contributing to the development of therapeutic strategies against COVID-19.

Centella asiatica is a tropical medicinal herb traditionally used for the treatment of different diseases, such as arthritis, kidney disease, diabetes mellitus, etc. Diabetes mellitus is emerging as a global health concern, demanding research to provide insights into it.

The current research study aimed at employing the Network Pharmacology and Molecular Docking approach to unearth and validate the possible molecular mechanism involved in the treatment of diabetic mellitus with herbal constituents from Centella asiatica.

The phytocompounds and targets of Centella asiatica were screened from different databases. An herb-core-target-ingredient-diabetes mellitus network was established via Cystoscope 3.7.2. Next, Go and KEGG enrichment analysis was performed. Lastly, the interaction between ligands and targets was investigated via molecular docking.

According to the results obtained, we identified 49 core targets of diabetes mellitus and 37 active ingredients of Centella asiatica. Next, Go and KEGG resulted in a total of 455 biological processes for the treatment of diabetes mellitus. The KEGG enrichment analysis reported that the targets were related to metabolic pathways, insulin signaling pathways, glycolysis/gluconeogenesis, oxidative stress, insulin resistance, etc. On the basis of KEGG enrichment and protein-protein interaction, we selected Fructose-1-6 bisphosphate1 (FBP1), Glucokinase (GCK), Cytochromes P450 (CYP19A1), fatty acid binding protein 1 (FABP1), Interleukin 2 (IL2) and angiotensin-converting enzyme (ACE), and phytocompounds from Centella asiatica for docking. From the docking study, it was concluded that several targets had a stable binding affinity with Centella asiatica phytocompounds.

We explored the biological mechanism of phytocompounds involved in the treatment of diabetes mellitus through different biological processes and signaling pathways, and lastly, docking provides us commending results that direct for experiments ahead.