Context: Several epidemiological researches imply a link between exposure to A1 beta casein (BC) containing milk and the incidence of non-communicable diseases in humans. Many breeding policies support BC variants not releasing Beta Casomorphin-7 (BCM-7).Objective: Insilico network analysis was performed to determine the bioactive targets, pathways and diseases associated with A1 and A2 beta casein and BCM-7. Results:The analysis revealed 63 bioactive targets for A1 and A2 beta casein with 118 pathways and 30 diseases associated. There were no differences in bioactive targets between A1 and A2 beta casein. For BCM-7, 15 bioactive targets were identified, which were found to be associated with 18 pathways and 4 diseases. Conclusion:The results showed that the change in amino acid at the 67th position in A1 and A2 beta casein did not affect the bioactive targets. BCM-7 may be held responsible for adverse effects until challenged by clinical trials.
Goat milk is less allergic because concentration of milk proteins is lower than that of cattle milk. But we can't rule out the possibility of allergies from goat milk. This study is conducted to check whether goat milk is allergic and antigenic epitopes are found. Six major milk proteins were downloaded from public domain. CTL, HTL and B cell epitopes were detected and 1 to 5 epitopes from each type in each study proteins crossed threshold, hence proving goat milk would also be allergic. Further topmost epitope from each category and linkers were used to develop vaccine against goat milk allergy. The designed vaccine was of the length 371 amino acid residues and was demonstrated to be strong antigenic while being non-allergic and non-toxic. Molecular docking of the epitopes was done against TLR3 and TLR4 and found to be very well interacting with the epitopes which was indicated in negative binding energies.Further immuno simulations were done and found that the designed vaccine was able to stimulate production of immune molecules.18.4% of the children with cow-milk allergies were also allergic to camel milk, 63.2% of them to goat milk, and 15.8% to cow, goat, and camel milks. Keeping this in background, as a measure of prevention to goat milk allergy here we attempted to computationally design an active immunotherapy approach i.e., vaccination for goat milk allergy.
Deep learning has emerged as a powerful tool in genomics, utilizing neural networks to uncover complex patterns in large datasets. This review explores the application of deep learning in genomics, focusing on supervised and unsupervised learning tasks.The process involves training models with appropriate evaluation metrics and curated datasets to optimize performance. Balancing training data and model flexibility is crucial to avoid underfitting or overfitting. Deep learning models, with their high capacity and flexibility, outperform traditional techniques like logistic regression and support vector machines in genomics. Various applications of deep learning in genomics are includes predicting protein sequence specificity, determining cis-regulatory elements, analyzing splicing regulation and gene expression, and predicting genomic variants. Deep learning proves particularly effective in studying functional genomics and regulatory elements, leveraging techniques from computer vision and natural language processing. Overall, deep learning shows promise in advancing genomics research and understanding complex biological processes.
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