A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method.

We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.

[1]  A. Kastin,et al.  A potent and selective endogenous agonist for the µ-opiate receptor , 1997, Nature.

[2]  D. L. Freeman,et al.  Reducing Quasi-Ergodic Behavior in Monte Carlo Simulations by J-Walking: Applications to Atomic Clusters , 1990 .

[3]  Jürgen Bajorath,et al.  Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. , 2007, Drug discovery today.

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

[5]  D. Landau,et al.  Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.

[6]  Haruki Nakamura,et al.  A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening , 2008, Advances and applications in bioinformatics and chemistry : AABC.

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

[8]  Multicanonical molecular dynamics algorithm employing an adaptive force-biased iteration scheme. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  P. Goodford A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. , 1985, Journal of medicinal chemistry.

[10]  Schmid,et al.  "Scaffold-Hopping" by Topological Pharmacophore Search: A Contribution to Virtual Screening. , 1999, Angewandte Chemie.

[11]  A. Hopfinger,et al.  Construction of 3D-QSAR Models Using the 4D-QSAR Analysis Formalism , 1997 .

[12]  A. Kidera,et al.  Multicanonical Ensemble Generated by Molecular Dynamics Simulation for Enhanced Conformational Sampling of Peptides , 1997 .

[13]  Shen Wang,et al.  Construction of a Virtual High Throughput Screen by 4D-QSAR Analysis: Application to a Combinatorial Library of Glucose Inhibitors of Glycogen Phosphorylase b , 1999, J. Chem. Inf. Comput. Sci..

[14]  Han van de Waterbeemd,et al.  Computer-Assisted Lead Finding and Optimization , 1997 .

[15]  Haruki Nakamura,et al.  Application of MDGRAPE‐3, a special purpose board for molecular dynamics simulations, to periodic biomolecular systems , 2009, J. Comput. Chem..

[16]  Johann Gasteiger,et al.  A new model for calculating atomic charges in molecules , 1978 .

[17]  Shuichi Hirono,et al.  Camdas: An automated conformational analysis system using molecular dynamics , 1997, J. Comput. Aided Mol. Des..

[18]  J. A. Grant,et al.  A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape , 1996, J. Comput. Chem..

[19]  Toshikazu Ebisuzaki,et al.  A special-purpose computer for gravitational many-body problems , 1990, Nature.

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

[21]  B. Berg,et al.  Multicanonical algorithms for first order phase transitions , 1991 .

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

[23]  A. Kidera,et al.  Generalized form of the conserved quantity in constant-temperature molecular dynamics , 2002 .

[24]  Soma Mandal,et al.  Rational drug design. , 2009, European journal of pharmacology.

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

[26]  R. Wade,et al.  Prediction of drug binding affinities by comparative binding energy analysis. , 1997, Journal of medicinal chemistry.

[27]  K. Hukushima,et al.  Exchange Monte Carlo Method and Application to Spin Glass Simulations , 1995, cond-mat/9512035.

[28]  R. Cramer,et al.  Recent advances in comparative molecular field analysis (CoMFA). , 1989, Progress in clinical and biological research.

[29]  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.

[30]  Y. Fukunishi,et al.  The Filling Potential Method: A Method for Estimating the Free Energy Surface for Protein−Ligand Docking , 2003 .

[31]  A. Lyubartsev,et al.  New approach to Monte Carlo calculation of the free energy: Method of expanded ensembles , 1992 .

[32]  Lee,et al.  New Monte Carlo algorithm: Entropic sampling. , 1993, Physical review letters.

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

[34]  A. J. Hopfinger INHIBITION OF DIHYDROFOLATE REDUCTASE: STRUCTURE-ACTIVITY CORRELATIONS OF 2,4-DIAMINO-5-BENZYLPYRIMIDINES BASED UPON MOLECULAR SHAPE ANALYSIS , 1981 .

[35]  P. Hawkins,et al.  Comparison of shape-matching and docking as virtual screening tools. , 2007, Journal of medicinal chemistry.

[36]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[37]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[38]  Haruki Nakamura,et al.  Prediction of protein-ligand complex structure by docking software guided by other complex structures. , 2008, Journal of molecular graphics & modelling.

[39]  J. Gasteiger,et al.  Chemoinformatics: A Textbook , 2003 .

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

[41]  J. P. Valleau,et al.  Density‐scaling Monte Carlo study of subcritical Lennard‐Jonesium , 1993 .

[42]  A. Kastin,et al.  A potent and selective endogenous agonist for the mu-opiate receptor. , 1997, Nature.

[43]  Annick Panaye,et al.  Comparative Molecular Field Analysis , 2011 .

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

[45]  Robert S. Pearlman,et al.  Metric Validation and the Receptor-Relevant Subspace Concept , 1999, J. Chem. Inf. Comput. Sci..

[46]  Berg,et al.  New approach to spin-glass simulations. , 1992, Physical review letters.

[47]  A. Hopfinger A QSAR investigation of dihydrofolate reductase inhibition by Baker triazines based upon molecular shape analysis , 1980 .

[48]  J. A. Grant,et al.  A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. , 2005, Journal of medicinal chemistry.