Similarity Searching Using Fingerprints of Molecular Fragments Involved in Protein-Ligand Interactions

To incorporate protein-ligand interaction information into conventional two-dimensional (2D) fingerprint searching, interacting fragments of active compounds were extracted from X-ray structures of protein-ligand complexes and encoded as structural key-type fingerprints. Similarity search calculations with fingerprints derived from interacting fragments were compared to fingerprints of complete ligands and control fragments. In these calculations, fingerprints of interacting fragments produced significantly higher compound recall than other fingerprints. These results indicate that ligand fragments involved in protein-ligand interactions carry much activity-specific chemical information that can be exploited in similarity searching without explicitly accounting for interaction information.

[1]  Jürgen Bajorath,et al.  New methodologies for ligand-based virtual screening. , 2005, Current pharmaceutical design.

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

[3]  Zhan Deng,et al.  Knowledge-based design of target-focused libraries using protein-ligand interaction constraints. , 2006, Journal of medicinal chemistry.

[4]  Stefano Costanzi,et al.  Discovery of novel agonists and antagonists of the free fatty acid receptor 1 (FFAR1) using virtual screening. , 2008, Journal of medicinal chemistry.

[5]  K.-C. Chou,et al.  Anti-SARS drug screening by molecular docking , 2006, Amino Acids.

[6]  William L. Jorgensen,et al.  Search for Non-Nucleoside Inhibitors of HIV-1 Reverse Transcriptase Using Chemical Similarity, Molecular Docking, and MM-GB/SA Scoring , 2007, J. Chem. Inf. Model..

[7]  Robert P. Sheridan,et al.  Comparison of Topological, Shape, and Docking Methods in Virtual Screening. , 2007 .

[8]  Hanna Geppert,et al.  Integrating Structure‐ and Ligand‐Based Virtual Screening: Comparison of Individual, Parallel, and Fused Molecular Docking and Similarity Search Calculations on Multiple Targets , 2008, ChemMedChem.

[9]  P. Willett,et al.  Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures. , 2004, Organic & biomolecular chemistry.

[10]  Peter Willett,et al.  Similarity-based virtual screening using 2D fingerprints. , 2006, Drug discovery today.

[11]  Pierre Baldi,et al.  Structure-based inhibitor design of AccD5, an essential acyl-CoA carboxylase carboxyltransferase domain of Mycobacterium tuberculosis. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Brian K. Shoichet,et al.  Virtual screening of chemical libraries , 2004, Nature.

[13]  Gabriele Cruciani,et al.  A Common Reference Framework for Analyzing/Comparing Proteins and Ligands. Fingerprints for Ligands And Proteins (FLAP): Theory and Application , 2007, J. Chem. Inf. Model..

[14]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[15]  Jürgen Bajorath,et al.  Integration of virtual and high-throughput screening , 2002, Nature Reviews Drug Discovery.

[16]  Z. Deng,et al.  Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. , 2004, Journal of medicinal chemistry.