Identification and validation of human DNA ligase inhibitors using computer-aided drug design.

Linking together of DNA strands by DNA ligases is essential for DNA replication and repair. Since many therapies used to treat cancer act by causing DNA damage, there is growing interest in the development of DNA repair inhibitors. Accordingly, virtual database screening and experimental evaluation were applied to identify inhibitors of human DNA ligase I (hLigI). When a DNA binding site within the DNA binding domain (DBD) of hLigI was targeted, more than 1 million compounds were screened from which 192 were chosen for experimental evaluation. In DNA joining assays, 10 compounds specifically inhibited hLigI, 5 of which also inhibited the proliferation of cultured human cell lines. Analysis of the 10 active compounds revealed the utility of including multiple protein conformations and chemical clustering in the virtual screening procedure. The identified ligase inhibitors are structurally diverse and have druglike physical and molecular characteristics making them ideal for further drug development studies.

[1]  Alexander D. MacKerell,et al.  Consideration of Molecular Weight during Compound Selection in Virtual Target-Based Database Screening , 2003, J. Chem. Inf. Comput. Sci..

[2]  J A McCammon,et al.  Accommodating protein flexibility in computational drug design. , 2000, Molecular pharmacology.

[3]  J. An,et al.  Structure-based virtual screening of chemical libraries for drug discovery. , 2006, Current opinion in chemical biology.

[4]  A. Tomkinson,et al.  DNA ligases: structure, reaction mechanism, and function. , 2006, Chemical reviews.

[5]  W. C. Swope,et al.  A computer simulation method for the calculation of equilibrium constants for the formation of physi , 1981 .

[6]  John M. Barnard,et al.  Chemical Similarity Searching , 1998, J. Chem. Inf. Comput. Sci..

[7]  L. Kelley,et al.  An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies. , 1996, Protein engineering.

[8]  C. Lipinski Drug-like properties and the causes of poor solubility and poor permeability. , 2000, Journal of pharmacological and toxicological methods.

[9]  E. Jaeger,et al.  Docking: successes and challenges. , 2005, Current pharmaceutical design.

[10]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[11]  A. Tomkinson,et al.  Human DNA ligase I completely encircles and partially unwinds nicked DNA , 2004, Nature.

[12]  Brown Rd,et al.  An Evaluation of Structural Descriptors and Clustering Methods for Use in Diversity Selection , 1998 .

[13]  X. Chen,et al.  Human DNA ligases I, III, and IV-purification and new specific assays for these enzymes. , 2006, Methods in enzymology.

[14]  R. Hertzberg,et al.  On the mechanism of topoisomerase I inhibition by camptothecin: evidence for binding to an enzyme-DNA complex. , 1989, Biochemistry.

[15]  Benoît Roux,et al.  Dominant solvation effects from the primary shell of hydration: Approximation for molecular dynamics simulations , 1995 .

[16]  S. Wodak,et al.  Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures , 2005, Proteins.

[17]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[18]  Bernard R. Brooks,et al.  New spherical‐cutoff methods for long‐range forces in macromolecular simulation , 1994, J. Comput. Chem..

[19]  Daekyu Sun,et al.  Development of non-electrophoretic assay method for DNA ligases and its application to screening of chemical inhibitors of DNA ligase I. , 2004, Journal of biochemical and biophysical methods.

[20]  Alexander D. MacKerell,et al.  Identification of non-phosphate-containing small molecular weight inhibitors of the tyrosine kinase p56 Lck SH2 domain via in silico screening against the pY + 3 binding site. , 2004, Journal of medicinal chemistry.

[21]  A. Tomkinson,et al.  Interaction between PCNA and DNA ligase I is critical for joining of Okazaki fragments and long-patch base-excision repair , 2000, Current Biology.

[22]  T. Halgren MMFF VI. MMFF94s option for energy minimization studies , 1999, J. Comput. Chem..

[23]  Donald G. Truhlar,et al.  New Class IV Charge Model for Extracting Accurate Partial Charges from Wave Functions , 1998 .

[24]  Ruth Nussinov,et al.  Predicting molecular interactions in silico: II. Protein-protein and protein-drug docking. , 2003 .

[25]  H. Carlson Protein flexibility and drug design: how to hit a moving target. , 2002, Current opinion in chemical biology.

[26]  B. Shoichet,et al.  High-throughput assays for promiscuous inhibitors , 2005, Nature chemical biology.

[27]  A. Tomkinson,et al.  DNA Ligase III Is Recruited to DNA Strand Breaks by a Zinc Finger Motif Homologous to That of Poly(ADP-ribose) Polymerase , 1999, The Journal of Biological Chemistry.

[28]  M. L. Connolly Solvent-accessible surfaces of proteins and nucleic acids. , 1983, Science.

[29]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[30]  M. L. Connolly Analytical molecular surface calculation , 1983 .

[31]  D. Beglov,et al.  Finite representation of an infinite bulk system: Solvent boundary potential for computer simulations , 1994 .

[32]  M. Karplus,et al.  Deformable stochastic boundaries in molecular dynamics , 1983 .

[33]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[34]  B. Shoichet,et al.  A specific mechanism of nonspecific inhibition. , 2003, Journal of medicinal chemistry.

[35]  Jürgen Bajorath,et al.  Combinatorial Preferences Affect Molecular Similarity/Diversity Calculations Using Binary Fingerprints and Tanimoto Coefficients , 2000, J. Chem. Inf. Comput. Sci..

[36]  I. Kuntz,et al.  Conformational analysis of flexible ligands in macromolecular receptor sites , 1992 .

[37]  Donald G. Truhlar,et al.  MODEL FOR AQUEOUS SOLVATION BASED ON CLASS IV ATOMIC CHARGES AND FIRST SOLVATION SHELL EFFECTS , 1996 .

[38]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[39]  James G. Nourse,et al.  Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..

[40]  Alexander D. MacKerell,et al.  Computational identification of inhibitors of protein-protein interactions. , 2007, Current topics in medicinal chemistry.

[41]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[42]  Lance Stewart,et al.  The mechanism of topoisomerase I poisoning by a camptothecin analog , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Y. Martin,et al.  An evaluation of structural descriptors and clustering methods for use in diversity selection. , 1998, SAR and QSAR in environmental research.

[44]  Alexander D. MacKerell,et al.  Identification of novel extracellular signal-regulated kinase docking domain inhibitors. , 2005, Journal of medicinal chemistry.

[45]  Alexander D. MacKerell,et al.  Identification and characterization of small molecule inhibitors of the calcium-dependent S100B-p53 tumor suppressor interaction. , 2004, Journal of medicinal chemistry.

[46]  Tudor I. Oprea,et al.  Is There a Difference Between Leads and Drugs? A Historical Perspective. , 2001 .

[47]  G. Tan,et al.  Natural-product inhibitors of human DNA ligase I. , 1996, The Biochemical journal.

[48]  Jan A Snyman,et al.  Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .

[49]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[50]  J. Mccammon,et al.  Accommodating Protein Flexibility in Computational Drug Design 1 , 2 , 2000 .

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

[52]  T. Halgren MMFF VII. Characterization of MMFF94, MMFF94s, and other widely available force fields for conformational energies and for intermolecular‐interaction energies and geometries , 1999, Journal of computational chemistry.

[53]  Conrad C. Huang,et al.  The MIDAS display system , 1988 .

[54]  D. Dube,et al.  Mycobacterium tuberculosis NAD+-dependent DNA ligase is selectively inhibited by glycosylamines compared with human DNA ligase I , 2005, Nucleic acids research.

[55]  Alexander D. MacKerell Empirical force fields for biological macromolecules: Overview and issues , 2004, J. Comput. Chem..

[56]  Alexander D. MacKerell,et al.  Rational design of human DNA ligase inhibitors that target cellular DNA replication and repair. , 2008, Cancer research.

[57]  Kuo-Chen Chou,et al.  Assessment of chemical libraries for their druggability , 2005, Comput. Biol. Chem..