Ki DoQ: using docking based energy scores to develop ligand based model for predicting antibacterials

Abstract

BackgroundIdentification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general.ResultsThis paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitative structure activity relationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value (Ki) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R/q2 of 0.82/0.67 and MAE of 2.43. Additionally, Support Vector Machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R/q2 values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values (q2/r2) in the range of 0.78-0.83/0.93-0.95.ConclusionsOur results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.osdd.net/raghava/kidoq, which allows the prediction of Ki value of a new ligand molecule against DHDPS.

Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv)

“…We observed that the reduction of descriptors even at > =0.6 correlation cutoff, is sufficient to develop a robust classification model. As reported in different studies, we also observed that descriptors selection was playing an important role in efficient model building [17,18]. In the present work, we have introduced a new algorithm named MCCA (Matthews Correlation Coefficient Algorithm) for selection of informative descriptors/fingerprints.…”

“…Despite the enormous progress in computational and medicinal chemistry, only few webservers namely KiDoQ [17], GDoQ [18] and CDD [19] for predicting the efficacy of potential antimycobacterial drug like molecules are freely available to the scientific community. In order to assist researchers in discovering new chemical entity (NCE) against tuberculosis, a systematic algorithm has been developed to predict the inhibitors of replicative and non-replicative drug tolerant M.tb H37Rv .…”

Interactions between Activin-Like Kinase 5 (ALK5) receptor and its inhibitors and the construction of a Docking Descriptor-Based QSAR model

“…AutoDockTools are capable of calculating 8 types of energy values that consist of: (i) estimated free energy of binding (E FreeBind ); (ii) estimated inhibition constant (k i ); (iii) final intermolecular energy (E InterMol ), which is the sum of the following three types of energy (iv) vdW + Hbond + desolv Energy (E VHD ); (v) electrostatic energy (E Elec ); (vi) final total energy (E FTot ); (vii) torsional free energy (E Tors ); (viii) unbounded system's energy (E Unb ), 33 and the Gasteiger charge descriptor. Then, the docking conformer at its minimum E FreeBind was loaded into BINANA to calculate the descriptors.…”

Prioritization of active antimalarials using structural interaction profile ofPlasmodium falciparumenoyl-acyl carrier protein reductase (PfENR)-triclosan derivatives

“…Further, a multivariate linear method was effectively applied to model predictions of queried molecules in response to trained 23 dihydrodipicolinate synthase (DHDPS) inhibitors [11]. This study employed only 11 Autodock (version 4) [12] energy-based descriptors using multiple linear regression (MLR) and support vector machine (SVM) and selected three descriptors to correlate DHDPS inhibitory constants (K i ) [11].…”

“…Consequently, the removal of these compounds enhanced the q 2 parameter of the presented statistical model, and its outlier behaviour was primarily attributed to diverse dock conformations that were not present in the structurally similar molecules and/or the variations in the residue interaction profile as per activity trend of the internal dataset compounds. The presence of an aliphatic chain in compounds 16 (pIC 50 = 4.57) and 17 (pIC 50 = 4.72) at the R 1 site and the presence of heterocycles at the R 1 site of the internal dataset molecules (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) which represent bulky groups, generated an improbable residue interaction profile for 16 and 17 that could not be modelled; hence the internal dataset must also constitute compounds having aliphatic groups at various chemical configurations so as to ensure optimal dock poses scoring. Compound 26, which is structurally similar to 27 besides possessing an identical pIC 50 value of 6.92, correlates with the activity in the regression model and the variation in the residue interaction profile (residues: Asn218, Ala319 and Ile369) (Table S1), making compound 27 an outlier.…”