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ICM Docking and Screening


Introduction

ICM Docking and Screening provides a unique set of tools for accurate individual ligand-protein docking, peptide-protein docking, and protein-protein docking. The ICM-Pro desktop modeling GUI interface offers a step-by-step docking menu that makes docking easier than ever. To achieve your docking goals, ICM-Dock does the following:

  • Automatic preparation of a molecule for a flexible docking
  • Assignment of the MMFF atom types based on local connectivity
  • Addition of hydrogens
  • Assignment of partial charges
  • Automatic identification of rotatable bonds
  • Fast grid potentials
  • Scripting for small scale flexible ligand docking
  • Procedures for protein-protein docking
  • Procedures for flexible peptide-receptor docking
  • 2D representations (SMILES): export and import,
  • Automatic 2D to 3D conversion
  • Refinement of docking solutions in full atom representation
  • Graphics Support for Virtual Ligand Screening module (module requires special VLS license)
The ICM docking module also allows for the browsing of docking solutions, binding site analysis, visualization of grid potentials, adjustment of grid potential areas, and configurable preferences for ligand size and score threshholds.

Docking Method

Five types of interaction potentials represent the receptor pocket: (i) van der Waals potential for a hydrogen atom probe; (ii) van der Waals potential for a heavy-atom probe (generic carbon of 1.7A radius); (iii) optimized electrostatic term; (iv) hydrophobic terms; and (v) loan-pair-based potential, which reflects directional preferences in hydrogen bonding. The energy terms are based on the all-atom vacuum force field ECEPP/3 with appended terms to account for solvation free energy and entropic contribution. Conformational sampling is based on the biased probability Monte Carlo (BPMC) procedure (1) which randomly selects a conformation in the internal coordinate space and then makes a step to a new random position independent of the previous one but according to a predefined continuous probability distribution. It has also been shown that after each random step, full local minimization greatly improves the efficiency of the procedure. The ICM program relies on global optimization of the entire flexible ligand in the receptor field and combines large-scale random moves of several types with gradient local minimization and a search history mechanism.

Virtual Screening - Scoring

The scoring function should give a good approximation of the binding free energy between a ligand and a receptor and is usually a function of different energy terms based on a force-field. The ICM scoring function (2) is weighted according to the following parameters (i) internal force-field energy of the ligand, (ii) entropy loss of the ligand between bound and unbound states, (iii) ligand-receptor hydrogen bond interactions, (iv) polar and non-polar solvation energy differences between bound and unbound states, (v) electrostatic energy, (vi) hydrophobic energy, and (vii) hydrogen bond donor or acceptor desolvation. The lower the ICM score, the higher the chance the ligand is a binder.

Success Stories

Please click here to view a selection of published ICM screening success stories.

Independent Evaluations

ICM virtual ligand screening technology has been ranked the best virtual screening tool in comparisons reported by the Scripps Research Institute (3), Astra Zeneca (4), and Wyeth (5). ICM-VLS ranked number one in terms of predicting the ligand pose and enrichment factor (number of compounds you need to test experimentally to find a hit) compared to a selection of other commercially available screening algorithms. For example, scientists at Astra Zeneca screened a database containing 20K random compounds and between 17-622 active ligands per drug target receptor. The enrichment factors for 12 targets at 1% of database subset compared to Schrodinger's Glide software is shown below. ICM molecular modeling and docking also performed very well at "blind" GPCR modeling and docking competitions (See 6-8).

MolSoft's docking and scoring algorithm ranked first place for prediction of ligand pose and screening accuracy in the most recent industry-wide competition organized by OpenEye, GlaxoWellcome, and Merck. The results of the competition were announced at the American Chemical Society Meeting in Anaheim in 2011 and MolSoft's ICM performance is reported here (9). The docking pose prediction accuracy was benchmarked using the modified Astex set of 85 protein-ligand complexes. The top score poses were correct (under 2ÅRMSD) in 60% to over 90% of the cases depending on the docking method. The ICM docking method achieved 78% of the top score poses under 1ÅRMSD and 91% under 2ÅRMSD.

References

1. Abagyan, R. & Totrov, M. Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J. Mol. Biol. 235, 983.1002 (1994).

2. Totrov, M. & Abagyan, R. Derivation of sensitive discrimination potential for virtual ligand screening. in Proc. Third Annu. Int. Conf. Comput. Mol. Biol. 312.320 (ACM, 1999). doi:10.1145/299432.299509

3. Bursulaya, B. D., Totrov, M., Abagyan, R. & Brooks, C. L., 3rd. Comparative study of several algorithms for flexible ligand docking. J. Comput. Aided Mol. Des. 17, 755.763 (2003).

4. Chen, H., Lyne, P. D., Giordanetto, F., Lovell, T. & Li, J. On evaluating molecular-docking methods for pose prediction and enrichment factors. J. Chem. Inf. Model. 46, 401.415 (2006).

5. Cross, J. B. et al. Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 49, 1455.1474 (2009).

6. Michino, M. et al. Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat. Rev. Drug Discov. 8, 455.463 (2009).

7. Katritch, V., Rueda, M., Lam, P. C.-H., Yeager, M. & Abagyan, R. GPCR 3D homology models for ligand screening: lessons learned from blind predictions of adenosine A2a receptor complex. Proteins 78, 197.211 (2010).

8. Kufareva, I., Rueda, M., Katritch, V., Stevens, R. C. & Abagyan, R. Status of GPCR modeling and docking as reflected by community-wide GPCR Dock 2010 assessment. Struct. Lond. Engl. 1993 19, 1108.1126 (2011).

9. Neves, M. A. C., Totrov, M. & Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des. 26, 675.686 (2012).

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