Exploring in house glutamate inhibitors of matrix metalloproteinase-2 through validated robust chemico-biological quantitative approaches

Abstract Matrix metalloproteinase-2 (MMP-2) is established as one of the most important metalloenzymes for targeting cancer. However, homologous MMP-9 of the gelatinase family is implicated as an antitarget of cancer. Therefore, it is an important and challenging task to achieve MMP-2 selectivity over MMP-9. In this article, robust validated chemico-biological quantitative approaches were conducted on a series of in house glutamate-based selective MMP-2 inhibitors over MMP-9 for further refinement of our MMP-2 inhibitor designing approach. The two-dimensional quantitative structure-activity relationship (2D-QSAR) study suggested that arylsulfonamide moiety was better than arylcarboxamide function, which in turn, supported by the hologram QSAR (HQSAR), 3D-QSAR comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies. Regarding the MMP-2 selectivity, glutamines were better than isoglutamines as evidenced by the quantitative activity-activity relationship (QAAR) and molecular docking studies. Favorable hydrophobic and steric features of aryl function directed towards the S1′ pocket were also well attributed. Naphthyl and p-bromophenoxyphenyl moieties in place of biphenyl function were found to be unfavorable for MMP-2 inhibition and selectivity over MMP-9. Linear or cyclic aliphatic group directed towards the S2′ pocket was favorable, whereas branching was unfavorable for MMP-2 inhibition and selectivity. The importance of biphenyl and 3,5-bistrifluoromethylbenzyl functions directed towards the S1′ and S2′ pockets, respectively, was well attributed for potent MMP-2 inhibition and selectivity over MMP-9..

Medienart:

E-Artikel

Erscheinungsjahr:

2017

Erschienen:

2017

Enthalten in:

Zur Gesamtaufnahme - volume:29

Enthalten in:

Structural chemistry - 29(2017), 1 vom: 20. Sept., Seite 285-297

Sprache:

Englisch

Beteiligte Personen:

Adhikari, Nilanjan [VerfasserIn]
Amin, Sk. Abdul [VerfasserIn]
Saha, Achintya [VerfasserIn]
Jha, Tarun [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

BKL:

35.00

Themen:

CoMFA
CoMSIA
Glutamate
MMP-2
Machine learning
Molecular docking
QAAR
QSAR
Selectivity

doi:

10.1007/s11224-017-1028-6

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

SPR017890675