Modeling of human M1 aminopeptidases for in silico screening of potential Plasmodium falciparum alanine aminopeptidase (PfA-M1) specific inhibitors

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Title	*Modeling of human M1 aminopeptidases for in silico* screening of potential Plasmodium falciparum alanine aminopeptidase (PfA-M1) specific inhibitors**
Authors	Shakti Sahi, Sneha Rai, Meenakshi Chaudhary & Vikrant Nain
Affiliation	School of Biotechnology, Gautam Buddha University, Greater Noida, 201312, India
Email	shaktis@gbu.ac.in; vikrant@gbu.ac.in; *Corresponding authors
Article Type	Hypothesis
Date	Received June 18, 2014; Accepted June 27, 2014; Published August 30, 2014
Abstract	Plasmodium falciparum alanine M1-aminopeptidase (PfA-M1) is a validated target for anti-malarial drug development. Presence of significant similarity between PfA-M1 and human M1-aminopeptidases, particularly within regions of enzyme active site leads to problem of non-specificity and off-target binding for known aminopeptidase inhibitors. Molecular docking based in silico screening approach for off-target binding has high potential but requires 3D-structure of all human M1-aminopeptidaes. Therefore, in the present study 3D structural models of seven human M1-aminopeptidases were developed. The robustness of docking parameters and quality of predicted human M1-aminopeptidases structural models was evaluated by stereochemical analysis and docking of their respective known inhibitors. The docking scores were in agreement with the inhibitory concentrations elucidated in enzyme assays of respective inhibitor enzyme combinations (r²≈0.70). Further docking analysis of fifteen potential PfA-M1 inhibitors (virtual screening identified) showed that three compounds had less docking affinity for human M1-aminopeptidases as compared to PfA-M1. These three identified potential lead compounds can be validated with enzyme assays and used as a scaffold for designing of new compounds with increased specificity towards PfA-M1.
Keywords	Drug designing, in silico screening, malaria, molecular docking, homology modeling.
Citation	*Sahi et al.* Bioinformation 10(8): 518-525 (2014)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.