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Inhibitors of Pantothenate Synthetase: Initiating a Quest for New TB Drugs Reaz Uddin, Michael Brunsteiner, Pavel A. Petukhov* Department of Medicinal.

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Presentation on theme: "Inhibitors of Pantothenate Synthetase: Initiating a Quest for New TB Drugs Reaz Uddin, Michael Brunsteiner, Pavel A. Petukhov* Department of Medicinal."— Presentation transcript:

1 Inhibitors of Pantothenate Synthetase: Initiating a Quest for New TB Drugs Reaz Uddin, Michael Brunsteiner, Pavel A. Petukhov* Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago 833, S. Wood St. Chicago, IL Tuberculosis (TB) an infectious disease caused by the bacillus Mycobacterium tuberculosis (MTB). MTB is a major human health threat, with an annual rate of 8.8 million new cases and 1.7 million deaths. One of the hallmarks of M. tuberculosis is the persistent phase of infection, when the bacteria are not actively growing and overall metabolic activity is down regulated, often termed non- replicating persistence (NRP). Most currently available drugs are not effective against NRP-TB thus requiring a minimum of 6 months of therapy to prevent relapse. Long-term chemotherapy inevitably increases the risk of drug resistance. Therefore the discovery and development of drugs effective against NRP-TB is considered the highest priority among TB drug discovery efforts. Pantothenate synthetase (PS) catalyzes the formation of pantothenate from ATP, D- pantoate and β-alanine in MTB. This pathway was found to be active during the NRP phase of the MTB life cycle. Thus, PS inhibitors form a promising alternative to existing antibiotics. Available experimental SAR data for PS are limited. Crystal structures are available for the apo-protein (PDB: 2A882) and a number of complexes with the natural substrate or reaction intermediates (e.g. PDB:1N2I2, Figure 1). Recently, an NIH Molecular Libraries Small Molecule Repository (MLSMR) library was tested by SRI against PS and the results were published in PubMed (http://pubchem.ncbi.nlm.nih.gov/assay/ assay.cgi? aid= 375). Out of 10,009 compounds, two molecules were found to inhibit PS (Figure 2). We used this data to developed an efficient protocol for virtual screening for PS inhibitors. Particular attention was paid to the role of crystal waters and receptor flexibility by extending the docking procedure to include multiple binding site models. We present the results of these efforts and discuss implications for further virtual screening for PS inhibitors.http://pubchem.ncbi.nlm.nih.gov/assay/ assay.cgi? aid= 375 INTRODUCTION Figure 1: The binding site of PS with a complexed reaction intermediate (PDB code 1N2I). Conserved crystal water molecules considered here are colored yellow. The surface of the binding site and the electrostatic potential (indicated by the surface color) were calculated using the programs MSMS and APBS. Figure 3: Overlay of seven structures of PS- substrate complexes and the apo-protein reveals four conserved water molecules that interact with both the protein and the ligand. (H- bonds are shown in blue) Comparison of the protein structure of the apo-protein and a complex between PS and the reaction intermediate reveals that substrate binding entails modification of the binding site geometry. Overlaying a number of PS crystal structures also shows the presence of several conserved water molecules in the binding site (Figure 3). To cover a range of possible binding site geometries we used two receptors for docking: the apo-protein (PDB:2A88) and the protein part of a complex with a reaction intermediate (PDB:1N2I). Each of these protein structures was combined with different combinations of the four conserved water molecules (Figure 3, Table 1). To make use of all information provided by the individual scores we decided to use the “rank-by-score” consensus scoring. To do so we needed to render the scores from different scoring functions comparable. Histograms were generated from the raw scores, s i, a normal distribution was fitted to the resulting histogram and the mean score  and standard deviation  for each scoring function were calculated, to generate new scores s i = (s i -  )/  Figure 8:Normalization of scores, to allow rank-by-score consensus scoring. Figure 9:Ranks obtained with two types of consensus scoring; consensus-a) with all scoring functions, consensus-f) with three best scoring functions i.e., shapegauss, oechem and chemgauss3. All possible combinations between different groups of scoring functions were considered (see Figure 9 for two examples) and found that the enrichment was not improved compared with the best single scoring function. For the molecular target considered here consensus scoring turned out to perform worse than the best single scoring function in OE/Fred We found a protocol that ranked the known binders within the best 3% of the compounds tested. The protocol is very efficient in terms of the required CPU-time (on a 30 processor Linux cluster 200,000 compounds could be processed in one day) N2I A WaterProt.Label Table 1: Combinations between x-tal structures and water occupancies used for docking. Conformer generation: Omega (OpenEye) was used to generate a maximum of 100 conformers for each compound. Docking: 10,000 compounds including the three known binders (Figure 2) were docked using Fred (OpenEye) into each of the PS binding sites labeled 1-8 in Table 1. The pose of the reaction-intermediate agrees with its crystal structure with an RMSD of 0.75 Å (Figure 5) Figure 4: Comparison of poses from crystal structure (pink) and docking (gray) Figure 2: PS inhibitors from NIH/SRI MLSC HTS screening (left, center) and a reaction intermediate (RI). We compared the accuracy of the six Fred/OE scoring functions and consensus scoring. Figure 6: Ranks of the three known binders (Figure 2) after scoring each of the docked poses with six different scoring functions available in Fred. (x-axes: labeled from table 1) Figure 7: Ranks of the known binders resulting when each compound is given the best score out of the eight scores calculated, one for each receptor geometry. 1: chemgauss, 2: oechemscore, 3: screenscore, 4: shapegauss, 5: PLP This research was supported by the National Institute of Health and the National Institute of Allergy and Infectious Diseases grant R21 AI and Institute for Tuberculosis Research at University of Illinois at Chicago. The financial support for Reaz Uddin was provided by Higher Education Commission of Pakistan. CONSERVED WATERS KNOWN INHIBITORS RESULTS: CONSENSUS SCORING SUMMARY ACKNOWLEDGEMENTS METHODS RESULTS: SCORING FOLLOW UP We used the above described protocol to screen the Chembridge library of 200,000 drug-like compounds. We currently use/develop advanced procedures to re-rank the top 5% of the screened library for further biological tests.


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