Insight into the structural requirements of thiophene-3-carbonitriles-based MurF inhibitors by 3D-QSAR, molecular docking and molecular dynamics study

Abstract The discovery of clinically relevant inhibitors against MurF enzyme has proven to be a challenging task. In order to get further insight into the structural features required for the MurF inhibitory activity, we performed pharmacophore and atom-based three-dimensional quantitative structure–activity relationship studies for novel thiophene-3-carbonitriles based MurF inhibitors. The five-feature pharmacophore model was generated using 48 inhibitors having IC50 values ranging from 0.18 to 663 μm. The best-fitted model showed a higher coefficient of determination (R2 = 0.978), cross-validation coefficient (Q2 = 0.8835) and Pearson coefficient (0.9406) at four component partial least-squares factor. The model was validated with external data set and enrichment study. The effectiveness of the docking protocol was validated by docking the co-crystallized ligand into the catalytic pocket of MurF enzyme. Further, binding free energy calculated by the molecular mechanics generalized Born surface area approach showed that van der Waals and non-polar solvation energy terms are the main contributors to ligand binding in the active site of MurF enzyme. A 10-ns molecular dynamic simulation was performed to confirm the stability of the 3ZM6-ligand complex. Four new molecules are also designed as potent MurF inhibitors. These results provide insights regarding the development of novel MurF inhibitors with better binding affinity.

[1]  G. Schulz Binding of nucleotides by proteins , 1992, Current Biology.

[2]  C. Walsh,et al.  Intracellular steps of bacterial cell wall peptidoglycan biosynthesis: enzymology, antibiotics, and antibiotic resistance. , 1992, Natural product reports.

[3]  M. Klein,et al.  Nosé-Hoover chains : the canonical ensemble via continuous dynamics , 1992 .

[4]  G. Schulz Binding of nucleotides by proteins , 1992, Current Biology.

[5]  M. Klein,et al.  Constant pressure molecular dynamics algorithms , 1994 .

[6]  T. Darden,et al.  A smooth particle mesh Ewald method , 1995 .

[7]  D. Pompliano,et al.  Kinetic mechanism of the Escherichia coli UDPMurNAc-tripeptide D-alanyl-D-alanine-adding enzyme: use of a glutathione S-transferase fusion. , 1996, Biochemistry.

[8]  D. Mengin-Lecreulx,et al.  Identification of the mpl gene encoding UDP-N-acetylmuramate: L-alanyl-gamma-D-glutamyl-meso-diaminopimelate ligase in Escherichia coli and its role in recycling of cell wall peptidoglycan , 1996, Journal of bacteriology.

[9]  D. Mengin-Lecreulx,et al.  Invariant amino acids in the Mur peptide synthetases of bacterial peptidoglycan synthesis and their modification by site-directed mutagenesis in the UDP-MurNAc:L-alanine ligase from Escherichia coli. , 1997, Biochemistry.

[10]  David J. Miller,et al.  Aminoalkylphosphinate inhibitors of D-Ala-D-Ala adding enzyme , 1998 .

[11]  Youwei Yan,et al.  Crystal structure of Escherichia coli UDPMurNAc-tripeptide d-alanyl-d-alanine-adding enzyme (MurF) at 2.3 A resolution. , 2000, Journal of molecular biology.

[12]  Robert P. Sheridan,et al.  Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches , 2001, J. Chem. Inf. Comput. Sci..

[13]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[14]  R. Levesque,et al.  Structure and function of the Mur enzymes: development of novel inhibitors , 2002, Molecular microbiology.

[15]  B. Honig,et al.  A hierarchical approach to all‐atom protein loop prediction , 2004, Proteins.

[16]  David D. Anderson,et al.  Structure-activity relationships of novel potent MurF inhibitors. , 2004, Bioorganic & medicinal chemistry letters.

[17]  Alexander Golbraikh,et al.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection , 2004, Molecular Diversity.

[18]  P. Hajduk,et al.  Structure of MurF from Streptococcus pneumoniae co‐crystallized with a small molecule inhibitor exhibits interdomain closure , 2005, Protein Science.

[19]  David E. Shaw,et al.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results , 2006, J. Comput. Aided Mol. Des..

[20]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[21]  K. Comess,et al.  An Ultraefficient Affinity-Based High-Throughout Screening Process: Application to Bacterial Cell Wall Biosynthesis Enzyme MurF , 2006, Journal of biomolecular screening.

[22]  David D. Anderson,et al.  Structure‐based Optimization of MurF Inhibitors , 2006, Chemical biology & drug design.

[23]  K. Bush,et al.  Utility of Muropeptide Ligase for Identification of Inhibitors of the Cell Wall Biosynthesis Enzyme MurF , 2006, Antimicrobial Agents and Chemotherapy.

[24]  Alpeshkumar K. Malde,et al.  Design of Inhibitors of the MurF Enzyme of Streptococcus pneumoniae Using Docking, 3D-QSAR, and de Novo Design , 2007, J. Chem. Inf. Model..

[25]  R. Levesque,et al.  Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening. , 2008, Bioorganic & medicinal chemistry.

[26]  A. Lloyd,et al.  Phage display-derived inhibitor of the essential cell wall biosynthesis enzyme MurF , 2008, BMC Biochemistry.

[27]  Janez Konc,et al.  Discovery of new inhibitors of D-alanine:D-alanine ligase by structure-based virtual screening. , 2008, Journal of medicinal chemistry.

[28]  Simona Distinto,et al.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection—What can we learn from earlier mistakes? , 2008, J. Comput. Aided Mol. Des..

[29]  R. Murakami,et al.  A novel assay of bacterial peptidoglycan synthesis for natural product screening , 2009, The Journal of Antibiotics.

[30]  K. Bush,et al.  MurF Inhibitors with Antibacterial Activity: Effect on Muropeptide Levels , 2009, Antimicrobial Agents and Chemotherapy.

[31]  S. Turk,et al.  Discovery of new inhibitors of the bacterial peptidoglycan biosynthesis enzymes MurD and MurF by structure-based virtual screening. , 2009, Bioorganic & medicinal chemistry.

[32]  W. Sherman,et al.  Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field. , 2010, Journal of chemical theory and computation.

[33]  Woody Sherman,et al.  ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers , 2010, J. Chem. Inf. Model..

[34]  W. Sherman,et al.  Probing the α‐Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis , 2010, Chemical biology & drug design.

[35]  R. Friesner,et al.  The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling , 2011, Proteins.

[36]  P. Herdewijn,et al.  Synthesis of modified peptidoglycan precursor analogues for the inhibition of glycosyltransferase. , 2012, Journal of the American Chemical Society.

[37]  Woody Sherman,et al.  Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments , 2013, Journal of Computer-Aided Molecular Design.

[38]  S. Turk,et al.  Structure-activity relationships of new cyanothiophene inhibitors of the essential peptidoglycan biosynthesis enzyme MurF. , 2013, European journal of medicinal chemistry.

[39]  M. Anderluh,et al.  Design, synthesis and evaluation of second generation MurF inhibitors based on a cyanothiophene scaffold. , 2014, European journal of medicinal chemistry.

[40]  Simona Golic Grdadolnik,et al.  Furan-based benzene mono- and dicarboxylic acid derivatives as multiple inhibitors of the bacterial Mur ligases (MurC–MurF): experimental and computational characterization , 2015, Journal of Computer-Aided Molecular Design.

[41]  Rainer Riedl,et al.  Targeting Antibiotic Resistance , 2016, Angewandte Chemie.