Computational Screening for the Dipeptidyl Peptidase-IV Inhibitory Peptides from Putative Hemp Seed Hydrolyzed Peptidome as a Potential Antidiabetic Agent.

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors.

Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction.

The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework.

Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew's correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins.Communicated by Ramaswamy H. Sarma.

PI3K/Akt Signaling Involved with Osteoinductive Effects of Achatina fulica Mucus

Abstract Mesenchymal stem cells and osteoblasts play important roles in bone formation. Achatina fulica mucus presented the property of osteoinduction. This study aimed to examine the effects of A. fulica mucus on human mesenchymal stem cell (hMSC) and human fetal osteoblastic cell line (HFOB) differentiation. The integrated effects of A. fulica mucus and polycaprolactone (PCL) on the differentiation of hMSCs were tested. The cell viability of hMSCs treated with A. fulica mucus was investigated by the MTT assay. The cell mineralization was observed by Alizarin Red S staining, the gene expression was investigated using RT-PCR, and the PI3K activation was studied using flow cytometry. The results indicated that A. fulica mucus induced osteogenic differentiation in hMSCs and HFOBs by upregulation of the osteogenic markers; osteopontin (OPN) and osteocalcin (OCN). The results of the Alizarin Red S staining indicated that A. fulica mucus supported mineralization in both hMSCs and HFOBs. The hMSCs cultured on PCL supplemented with A. fulica mucus showed significantly increased RUNX2 and OPN expressions. A. fulica mucus was observed to increase PI3K activation in hMSCs. The findings of this study suggested that A. fulica mucus and biomaterials could be applied together for use in bone regeneration in the future.