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

Garg et al. BMC Bioinformatics 2010, 11:125 http://www.biomedcentral.com/1471-2105/11/125 METHODOLOGY ARTICLE Open Access KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials Aarti Garg1,2, Rupinder Tewari2, Gajendra PS Raghava1* Abstract Background: Identification 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. Results: This 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. Conclusions: Our 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. Background An escalating magnitude of drug resistance among bacterial pathogens has been installing a serious threat on the public health and economy of the developed world. A survey report has suggested that the direct cost to US economy alone due to drug resistant bacterial infection is around $4-$5 billion annually [1-3]. Even for pharmaceuticals companies, it turns out to be a heart-dying situation that after investing ~$800 million and about 15 years of atrocious labor to introduce a drug in the market, the pathogens already attains resistance against the * Correspondence: 1 Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India drug. Therefore, there is an urgent need to recognize new inhibitors against novel and/or known targets. Undoubtedly, well-established bacterial targets i.e. cell wall and membrane biosynthesis, protein biosynthesis, nucleic acid etc always the first choice for developing antibacterials. The recent trend in this direction indicates that researchers are looking for novel targets alongside to discover new classes of inhibitors/antibiotics. The amino acids biosynthetic pathways specifically lysine pathway has gained special attention because of its potential role in bacterial cell wall and protein synthesis [4,5]. The D, L-diaminopimelic acid (meso-DAP), an important intermediate in the biosynthetic pathway of lysine is crucial in cross-linking peptidoglycan chains to © 2010 Garg et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Garg et al. BMC Bioinformatics 2010, 11:125 http://www.biomedcentral.com/1471-2105/11/125 provide strength and rigidity to the bacterial cell wall (known as DAP pathway). The absence of this pathway in mammalian system suggests that specific inhibitors of this biosynthetic pathway may be a valuable for developing novel classes of antibacterial agents. In this study, we explored DHDPS enzyme of the pathway, which catalysis condensation of pyruvate and aspartate semialdehyde to form DHDP. Figure 1 shows the established DAP pathway for DAP and lysine biosynthesis. The enzyme is encoded by dapA gene, which has been cloned and expressed from several strains, including Thermatoga maritima, Corynebacterium glutamicum, Mycobacterium tuberculosis and Bacillus anthracis. The Figure 1 Enzymatic action of DHDPS leads to the biosynthesis of bacterial cell wall and protein components. Figure 1 shows the action of DHDPS enzyme involved in protein and cell wall synthesis process. Page 2 of 13 three-dimensional structures of DHDPS enzyme from Escherichia coli, Staphylococcus aureus, M. tuberculosis and B. anthracis enzymes with substrate pyruvate and without have been reported [6-18]. The antibacterial identification using experimental techniques is invariably very expensive, requires extensive pains and labor. Therefore, in silico techniques, which have the power to cut down these unavoidable steps, would be valuable. In recent years, in silico techniques like quantitative structure activity relationship (QSAR) and molecular docking are gaining high popularity in the drug discovery [19-21]. Both these methodologies allow the identification of probable lead candidates expeditiously prior to chemical synthesis and characterization, thereby, making the process more cost effective [22,23]. In the present study, we attempt to integrate power of two in silico potential techniques: QSAR and molecular docking by using docking generated energy-based descriptors for building QSAR models. Using this strategy, the information regarding binding mode of ligands in the active site is accumulated which would in turn assist the accurate prediction of better inhibitor with improved Ki values. To facilitate this we also developed a web-interface to help experimentalist working in the field of designing novel inhibitors against DHDPS enzyme. Results For the docking of 23 inhibitors, E. coli DHDPS crystal structure stored in the PDB file 1YXD was retrieved. The crystal structure of DHDPS consisted of two similar chains (A and B) with inhibitor bound at allosteric site [13]. The water molecules and inhibitor were removed using PYMOL software and chain A was considered for the docking purpose. The python scripts were used for carrying out automated flexible docking of 23 inhibitors on the predefined and experimentally c (...truncated)