Computational studies on N-phenyl pyrrole derivatives as MmpL3 inhibitors in Mycobacterium tuberculosis. (February 2019)

• Record Type: Journal Article
• Authors: Munnaluri, RamaKrishna; Reddy Peddi, Saikiran; Kanth Sivan, Sree; Manga, Vijjulatha
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 81
• Page End: 94
• Publication Date: 2019-02
• Subjects: Tuberculosis -- 3D QSAR -- Comparative molecular field analysis -- Comparative molecular similarity indices analysis -- Mycobacterial membrane proteins large 3; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.11.007
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: 3D QSAR studies were performed on N-phenyl pyrrole derivatives as MmpL3 inhibitors. The developed 3D QSAR models were further evaluated for their veracity by employing multiple validation tools. Structural requirements for enhancing inhibitory activity was derived from contour map analysis. Fifteen molecules as potent candidates against MmpL3 with good predicted activity and ADMET profile were designed. Abstract: The fight against tuberculosis (TB) is a time immemorial one and the emergence of new drug resistant strains of Mycobacterium tuberculosis keeps throwing new challenges to the scientific community immersed in finding mechanisms to control this dreaded disease. Computer aided drug designing (CADD) is one of the several approaches that can assist in identifying the potent actives against Mycobacterium. In this work, a series of 109 known Mycobacterial membrane proteins large 3 (MmpL3) inhibitors were pooled and atom based 3D QSAR analysis was performed to understand the structural features essential for inhibitory activity against the MmpL3, known to be a key player in transporting substances critical for cell wall integrity of Mycobacterium. The data set employed was randomly split into training set and test set molecules. The training set of 74 molecules was used to derive CoMFA and CoMSIA models that were statistically reliable (CoMFA: q 2 loo = 0.53; r 2 ncv = 0.93 and CoMSIA: q 2 loo = 0.60; r 2 ncv = 0.93). The derived models also exhibited good external predictive ability (CoMFA: r 2 pred = 0.78 and CoMSIA: r 2 pred = 0.79). The results are quite encouraging and information derived from these analyses was applied to design new molecules. The designed molecule showed appreciable predicted activity values and reasonably good ADMET profile. The strategy used in designing new molecules can be pursued in the hunt for new chemical entities targeting MmpL3, expanding the existing arsenal against TB.