In Silico Drug Screening Based on a Protein-Compound Affinity Matrix

We developed a new method to improve the accuracy of molecular interaction data using a protein-compound affinity matrix calculated by protein-compound docking software. We approximated the protein-compound binding free energy as a linear combination of the raw docking scores of the compound with many different proteins. The coefficients of the linear combination were estimated based on the amino-acid sequence similarities among proteins. This method was applied to in silico screening of the active compounds of five target proteins using multiple target screening, and it increased the hit ratio by several times compared to that given by the raw docking scores. The hit ratio also becomes robust against the difference of target proteins. In addition, we have developed some methods based on a protein-compound affinity matrix. When some active compounds were known, a consensus score, which combines the structure-based and ligand-based screening results, was applied to a target. Finally, we could achieve a high hit ratio for some targets by using a combination of screening methods.

[1]  Song Liu,et al.  A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. , 2005, Journal of medicinal chemistry.

[2]  Haruki Nakamura,et al.  Noise Reduction Method for Molecular Interaction Energy: Application to in Silico Drug Screening and in Silico Target Protein Screening. , 2006 .

[3]  Hans Briem,et al.  Flexsim-X: A Method for the Detection of Molecules with Similar Biological Activity , 2000, J. Chem. Inf. Comput. Sci..

[4]  I. Kuntz,et al.  Molecular similarity based on DOCK-generated fingerprints. , 1996, Journal of medicinal chemistry.

[5]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[6]  J. Gasteiger,et al.  ITERATIVE PARTIAL EQUALIZATION OF ORBITAL ELECTRONEGATIVITY – A RAPID ACCESS TO ATOMIC CHARGES , 1980 .

[7]  Y. Fukunishi,et al.  Classification of chemical compounds by protein-compound docking for use in designing a focused library. , 2006, Journal of medicinal chemistry.

[8]  Haruki Nakamura,et al.  Multiple target screening method for robust and accurate in silico ligand screening. , 2006, Journal of molecular graphics & modelling.

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

[10]  G. Vigers,et al.  Multiple active site corrections for docking and virtual screening. , 2004, Journal of medicinal chemistry.

[11]  U. Lessel,et al.  In vitro and in silico affinity fingerprints: Finding similarities beyond structural classes , 2000 .

[12]  Maria Kontoyianni,et al.  Evaluation of library ranking efficacy in virtual screening , 2005, J. Comput. Chem..

[13]  Y. Martin,et al.  A general and fast scoring function for protein-ligand interactions: a simplified potential approach. , 1999, Journal of medicinal chemistry.

[14]  David M. Rocke,et al.  Predicting ligand binding to proteins by affinity fingerprinting. , 1995, Chemistry & biology.

[15]  Robin Taylor,et al.  A new test set for validating predictions of protein–ligand interaction , 2002, Proteins.

[16]  D. E. Clark,et al.  Flexible docking using tabu search and an empirical estimate of binding affinity , 1998, Proteins.

[17]  Y. Fukunishi,et al.  Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening. , 2005, Journal of molecular graphics & modelling.