Epithelial–mesenchymal transition (EMT) is a key step in transdifferentiation process in solid cancer development. Forthcoming evidence suggest that the stratified program transforms polarized, immotile epithelial cells to migratory mesenchymal cells associated with enhancement of breast cancer stemness, metastasis, and drug resistance. It involves primarily several signaling pathways, such as transforming growth factor‐β (TGF‐β), cadherin, notch, plasminogen activator protein inhibitor, urokinase plasminogen activator, and WNT/beta catenin pathways. However, current understanding on the crosstalk of multisignaling pathways and assemblies of key transcription factors remain to be explored. In this review, we focus on the crosstalk of signal transduction pathways linked to the current therapeutic and drug development strategies. We have also performed the computational modeling on indepth the structure and conformational dynamic studies of regulatory proteins and analyze molecular interactions with their associate factors to understand the complicated process of EMT in breast cancer progression and metastasis. Electrostatic potential surfaces have been analyzed that help in optimization of electrostatic interactions between the protein and its ligand. Therefore, understanding the biological implications underlying the EMT process through molecular biology with biocomputation and structural biology approaches will enable the development of new therapeutic strategies to sensitize tumors to conventional therapy and suppress their metastatic phenotype.

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Angiotensin converting enzyme-I (ACE-I) is a key therapeutic target of the renin−angiotensin−aldosterone system (RAAS), the central pathway of blood pressure regulation. Food-derived peptides with ACE-I inhibitory activities are receiving significant research attention. However, identification of ACE-I inhibitory peptides from different food proteins is a labor-intensive, lengthy, and expensive process. For successful identification of potential ACE-I inhibitory peptides from food sources, a machine learning and structural bioinformatics-based web server has been developed and reported in this study. The web server can take input in the FASTA format or through UniProt ID to perform the in silico gastrointestinal digestion and then screen the resulting peptides for ACE-I inhibitory activity. This unique platform provides elaborated structural and functional features of the active peptides and their interaction with ACE-I. Thus, it can potentially enhance the efficacy and reduce the time and cost in identifying and characterizing novel ACE-I inhibitory peptides from food proteins. URL: http://hazralab.iitr.ac.in/ahpp/index.php.

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