Exploring the Structural Aspects of Ureido Amino Acid-Based APN Inhibitors through Validated Comparative Regression and Classification Based Molecular Modeling Studies
Posted: 11 Feb 2020
Date Written: February 6, 2020
The family of aminopeptidase enzymes is well reputed for their ability to catalyze the cleavage of amino acids or peptide chains at the N-terminal. The exopeptidase enzyme aminopeptidase N (APN) is a Zn2+ dependent metalloenzyme of the aminopeptidase family. APN is widely studied because of its versatile nature. The multi-functionality of APN as a peptidase, a receptor and as a signalling molecule has provided it the access to influence a number of disease conditions, angiogenesis, metastasis, viral diseases and cellular invasion including a variety of cancer conditions. The versatile contribution of APN in tumor angiogenesis and metastasis, cellular invasion and migration clearly encourage the researcher to develop potent and effective APN inhibitors as anti-cancer agents. Although a number of molecules have been designed and developed as potent APN inhibitors, the lack of effective and approved inhibitors for the treatment of diseases related to it is a major setback in this task. Hence, study of the APN and its existing inhibitors is a crucial and fundamental tread for identification of significant structural features of this enzyme and its inhibitors which will aid the designing of newer molecules as potent and effective APN inhibitors as anticancer agents.
The quantitative structure-activity relationship (QSAR) utilizes different mathematical equations to develop statistically significant models and to correlate the important structural attributes of molecules with their biological responses. Thus, the implementation of the QSAR study is a quite useful and efficient technique to identify important molecular features and designing of newer drug molecules.
Therefore, in this study, four different 2D-QSAR approaches namely multiple linear regression study (MLR), classification based QSAR like linear discrimination analysis study (LDA), Bayesian classification study and hologram based QSAR study (HQSAR) were adopted to identify the important structural features of 80 ureido group-containing amino acid derivate inhibitors with a wide range of APN inhibitory activity. All these constructed QSAR models were validated internally and external which provided significant statistical outcomes. Besides the statistical outcomes, the models were also able to identify different structural features of these amino acid derivatives those are important for their APN inhibitory potency. Additionally, some of these observations were also aligned with the outcomes of the previously performed QSAR studies conducted on different APN inhibitors. Therefore, in a nutshell, these observations of the current study will help us to guide the designing of potent APN inhibitors as effective anticancer agents and may help others to design potent and APN inhibitors in the future.
Keywords: Aminopeptidase-N, APN inhibitor, 2D-QSAR, LDA, Bayesian classification model, HQSAR
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