Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening.
The pharmacophoric space of streptococcal MurF was explored using a set of 39 known inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that access self-consistent quantitative structure-activity relationship (QSAR) (r(2)=0.93,F=56.9,r(LOO)(2)=0.91,r(PRESS)(2) against eight external test inhibitors=0.75). Two orthogonal pharmacophores (of cross-correlation r(2)=0.26) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within MurF binding pocket. The validity of the QSAR equation and the associated pharmacophore models was experimentally established by the identification of three promising new MurF inhibitors retrieved from the NCI database. Docking studies conducted on active hits supported the binding modes suggested by the pharmacophore/QSAR analysis.
Taha
Mutasem O
MO
Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan. mutasem@ju.edu.jo
Atallah
Naji
N
Al-Bakri
Amal G
AG
Paradis-Bleau
Catherine
C
Zalloum
Hiba
H
Younis
Khaled S
KS
Levesque
Roger C
RC
D019295:Computational Biology; D030541:Databases, Genetic; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008024:Ligands; D008958:Models, Molecular; D009124:Muscle Proteins / Q000037:antagonists & inhibitors* / Q000737:chemistry* / Q000378:metabolism; D017434:Protein Structure, Tertiary; D011550:Pseudomonas aeruginosa / Q000235:genetics / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012984:Software
D008024:Ligands; D009124:Muscle Proteins