MycPermCheck: the Mycobacterium tuberculosis permeability prediction tool for small molecules

MOTIVATION With >8 million new cases in 2010, particularly documented in developing countries, tuberculosis (TB) is still a highly present pandemic and often terminal. This is also due to the emergence of antibiotic-resistant strains (MDR-TB and XDR-TB) of the primary causative TB agent Mycobacterium tuberculosis (MTB). Efforts to develop new effective drugs against MTB are restrained by the unique and largely impermeable composition of the mycobacterial cell wall. RESULTS Based on a database of antimycobacterial substances (CDD TB), 3815 compounds were classified as active and thus permeable. A data mining approach was conducted to gather the physico-chemical similarities of these substances and delimit them from a generic dataset of drug-like molecules. On the basis of the differences in these datasets, a regression model was generated and implemented into the online tool MycPermCheck to predict the permeability probability of small organic compounds. DISCUSSION Given the current lack of precise molecular criteria determining mycobacterial permeability, MycPermCheck represents an unprecedented prediction tool intended to support antimycobacterial drug discovery. It follows a novel knowledge-driven approach to estimate the permeability probability of small organic compounds. As such, MycPermCheck can be used intuitively as an additional selection criterion for potential new inhibitors against MTB. Based on the validation results, its performance is expected to be of high practical value for virtual screening purposes. AVAILABILITY The online tool is freely accessible under the URL http://www.mycpermcheck.aksotriffer.pharmazie.uni-wuerzburg.de

[1]  Gerhard Klebe,et al.  Comparison of Automatic Three-Dimensional Model Builders Using 639 X-ray Structures , 1994, J. Chem. Inf. Comput. Sci..

[2]  Sang-Nae Cho,et al.  Identification of novel antitubercular compounds through hybrid virtual screening approach. , 2010, Bioorganic & Medicinal Chemistry.

[3]  Lynn Rasmussen,et al.  High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv. , 2009, Tuberculosis.

[4]  J. Liu,et al.  Mycolic Acid Structure Determines the Fluidity of the Mycobacterial Cell Wall* , 1996, The Journal of Biological Chemistry.

[5]  Tobias Müller,et al.  Bioinformatics Applications Note Systems Biology Bionet: an R-package for the Functional Analysis of Biological Networks , 2022 .

[6]  A. Hopfinger,et al.  Molecular modeling and simulation of Mycobacterium tuberculosis cell wall permeability. , 2004, Biomacromolecules.

[7]  R. Benz,et al.  Permeability of the cell wall of Mycobacterium smegmatis , 1994, Molecular microbiology.

[8]  Peter J Tonge,et al.  A Slow, Tight Binding Inhibitor of InhA, the Enoyl-Acyl Carrier Protein Reductase from Mycobacterium tuberculosis* , 2010, The Journal of Biological Chemistry.

[9]  Thomas Lengauer,et al.  ROCR: visualizing classifier performance in R , 2005, Bioinform..

[10]  H. Nikaido,et al.  Permeability barrier to hydrophilic solutes in Mycobacterium chelonei , 1990, Journal of bacteriology.

[11]  P. Ortiz de Montellano,et al.  Inhibition of the Mycobacterium tuberculosis enoyl acyl carrier protein reductase InhA by arylamides. , 2007, Bioorganic & medicinal chemistry.

[12]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[13]  Sean Ekins,et al.  Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. , 2009, Drug discovery today.

[14]  Robert Stroud,et al.  Pyrrolidine carboxamides as a novel class of inhibitors of enoyl acyl carrier protein reductase from Mycobacterium tuberculosis. , 2006, Journal of medicinal chemistry.

[15]  Brook G. Milligan,et al.  Estimating and Analyzing Demographic Models Using the popbio Package in R , 2007 .

[16]  M. Daffé,et al.  The cell envelope of Mycobacterium tuberculosis with special reference to the capsule and the outer permeability barrier , 2005 .

[17]  CHUN WEI YAP,et al.  PaDEL‐descriptor: An open source software to calculate molecular descriptors and fingerprints , 2011, J. Comput. Chem..

[18]  M. Daffé,et al.  Transport assays and permeability in pathogenic mycobacteria. , 2009, Methods in molecular biology.

[19]  Peter J Tonge,et al.  High affinity InhA inhibitors with activity against drug-resistant strains of Mycobacterium tuberculosis. , 2006, ACS chemical biology.

[20]  Eric Arnoult,et al.  The challenge of new drug discovery for tuberculosis , 2011, Nature.

[21]  Tao Jiang,et al.  ChemmineR: a compound mining framework for R , 2008, Bioinform..

[22]  J C Sacchettini,et al.  Enzymatic characterization of the target for isoniazid in Mycobacterium tuberculosis. , 1995, Biochemistry.

[23]  C. Fréhel,et al.  Triple-layered structure of mycobacterial cell wall: Evidence for the existence of a polysaccharide-rich outer layer in 18 mycobacterial species , 1986, Current Microbiology.

[24]  Brian K. Shoichet,et al.  ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..

[25]  R. Venkataraghavan,et al.  Atom pairs as molecular features in structure-activity studies: definition and applications , 1985, J. Chem. Inf. Comput. Sci..

[26]  R. Reynolds,et al.  High Throughput Screening for Inhibitors of Mycobacterium tuberculosis H 37 Rv , 2012 .

[27]  P. Legendre,et al.  vegan : Community Ecology Package. R package version 1.8-5 , 2007 .

[28]  G. V. Paolini,et al.  Quantifying the chemical beauty of drugs. , 2012, Nature chemistry.

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  Sean Ekins,et al.  A collaborative database and computational models for tuberculosis drug discovery. , 2010, Molecular bioSystems.

[31]  Joel S. Freundlich,et al.  Triclosan Derivatives: Towards Potent Inhibitors of Drug‐Sensitive and Drug‐Resistant Mycobacterium tuberculosis , 2009, ChemMedChem.

[32]  Stan Pounds,et al.  Estimating the Occurrence of False Positives and False Negatives in Microarray Studies by Approximating and Partitioning the Empirical Distribution of P-values , 2003, Bioinform..

[33]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.