## ICM Homology |

**NOTE:** This product is an add-on to ICM-Pro.

The **ICM-Homology** modeling algorithm has proved to be one of the more robust modeling tools (1-6). After initial placement of the aligned
polypeptide chain onto the template structure, the side-chain torsion angles are predicted by simultaneous global optimization of the energy
for all non-identical residues.

Methodology for conformational modeling of protein side chains and loops, implemented in ICM, relies on
internal coordinate definition of the molecular object combined with computationally efficient **ICM Biased Probability Monte Carlo** (BPMC)
optimization (7). An extended force field includes surface terms, electrostatics with the boundary element solution of the Poisson
equation (8), side chain entropy terms, and a fast algorithm for calculating molecular surfaces. The quality of the resulting 3D model is
assessed by a specialized ICM procedure, which also predicts possible backbone deviations between the homologues.

The ICM homology modeling algorithm has demonstrated excellent accuracy in blind predictions at the CASP2 competition 6 and in several protein engineering applications (9-16). Moreover, recent results show that ICM models built with as little as 35% identity can be accurate enough to be successfully used in receptor based rational drug design (1-6,17).

**Key features of ICM-Homology include:**

- Sensitive sequence search for template identification.
- Fast model building - the algorithm builds the model with all loops in seconds.
- Loop prediction using a loop PDB database.
- Loop prediction through local energy optimization.
- Multi-template modeling.
- Loop grafting.
- Protein sculpting.
- Peptide modeling.
- Search the PDB for similar loop conformation.
- Calculate relative residue frequency in similar loops from the PDB.
- Prediction of Disulfide bonds.
- Local reliability prediction and model validation features.
- Model refinement using ICM global optimization.
- Membrane protein modeling.
- High-throughput homology modeling.

**References**

(1) Schapira, M., B.M. Raaka, H.H. Samuels, andR. Abagyan. 2001. In Silico discovery of novel Retinoic Acid Receptor agonist structures. BMC Structural Biology 1:1.

(2) Schapira, M., R. Abagyan, andM. Totrov. 2002. Structural model of nicotinic acetylcholine receptor isotypes bound to acetylcholine and nicotine. BMC Struct Biol 2(1):1.

(3) Schapira, M., R. Abagyan, andM. Totrov. 2003. Nuclear hormone receptor targeted virtual screening. J Med Chem 46(14):3045-3059.

(4) Schapira, M., B.M. Raaka, S. Das, L. Fan, M. Totrov, Z. Zhou, S.R. Wilson, R. Abagyan, andH.H. Samuels. 2003. Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking. Proc Natl Acad Sci U S A 100(12):7354-7359.

(5) Schapira, M., B.M. Raaka, H.H. Samuels, andR. Abagyan. 2000. Rational discovery of novel nuclear hormone receptor antagonists. Proc. Natl. Acad. Sci. U. S. A. 97(3):1008-1013.

(6) Abagyan, R., S. Batalov, T. Cardozo, M. Totrov, J. Webber, andY.Y. Zhou. 1997. Homology modeling with internal coordinate mechanics: Deformation zone mapping and improvements of models via conformational search. Proteins:29-37.

(7) Abagyan, R.A., and M.M. Totrov. 1996. Biased probability Monte Carlo as a powerful global energy optimization method for biomolecular structure prediction. Abstr. Pap. Am. Chem. Soc. 211:35-COMP.

(8) Totrov, M., andR. Abagyan. 2001. Rapid boundary element solvation electrostatics calculations in folding simulations: successful folding of a 23-residue peptide. Biopolymers 60(2):124-133.

(9) Borchert, T.V., R. Abagyan, R. Jaenicke, andR.K. Wierenga. 1994. Design, Creation, and Characterization of a Stable, Monomeric Triosephosphate Isomerase. Proc. Natl. Acad. Sci. U. S. A. 91(4):1515-1518.

(10) Borchert, T.V., K.V. Kishan, J.P. Zeelen, W. Schliebs, N. Thanki, R. Abagyan, R. Jaenicke, andR.K. Wierenga. 1995. Three new crystal structures of point mutation variants of monoTIM: conformational flexibility of loop-1, loop-4 and loop-8. Structure 3(7):669-679.

(11) Norledge, B.V., A.M. Lambeir, R.A. Abagyan, A. Rottmann, A.M. Fernandez, V.V. Filimonov, M.G. Peter, andR.K. Wierenga. 2001. Modeling, mutagenesis, and structural studies on the fully conserved phosphate-binding loop (loop 8) of triosephosphate isomerase: Toward a new substrate specificity. Proteins 42(3):383-389.

(12) Thanki, N., J.P. Zeelen, M. Mathieu, R. Jaenicke, R.A. Abagyan, R.K. Wierenga, andW. Schliebs. 1997. Protein engineering with monomeric triosephosphate isomerase (monoTIM): the modelling and structure verification of a seven-residue loop. Protein Eng 10(2):159-167

(13) Cardozo, T., Totrov, M.M. & Abagyan, R.A (1995) Homology modeling by the ICM method. Proteins, 23, 403-414.

(14) Rashin, A.A., Rashin, B.H., Rashin A., Abagyan, R. (1997) Evaluating the energetics of empty cavities and internal mutations in proteins. Protein Science, 6, 2143-2158.

(15) Norledge, B.V., Lambeir, A.M., Abagyan, R.A., Rottmann, A., Fernandez, A.M., Filimonov, V., Peter, M.G., and Wierenga, R.K. (2001). Modeling, mutagenesis, and structural studies on the fully conserved phosphateloop (loop 8) of triosephosphate isomerase: toward a new substrate specificity. Proteins Feb 15;42(3), 383-9

(16) Cardozo, T.J., Abagyan, R. (1998) Molecular Modeling of the Domain Shared Between CED-4 and its Mammalian Homologue Apaf-1: A Structural Relationship to the G-proteins. J. Mol. Model., 4, 83-93.

(17) Abagyan, R., andM. Totrov. 2001. High-throughput docking for lead generation. Curr Opin Chem Biol 5(4):375-382.