Ligand Binding Site Prediction for HIV-1 Protease using Shape Comparison Techniques Manasi Jahagirdar 1, Vivek K Jalahalli 2, Sunil Kumar 1, A. Srinivas Reddy 3, Xiaoyu Zhang 4 and Rajni Garg 5 1 Dept. of Electrical And Computer Engineering, San Diego State University, CA, 2 Dept. of Mathematics and Statistics, San Diego State University, CA 3 Molecular Modeling Group, Indian Institute of Chemical Technology, Hyderabad, India 4 Chemistry and Biochemistry Dept., California State University, San Marcos, CA, 5 Computer Science Dept., California State University, San Marcos, CA IntroductionIntroductionDescriptionDescription Future Work Future Work ReferencesReferences Discussion Discussion 1. Laskowski, R. A., Luscombe, N. M., Swindells, M. B. & Thornton, J. M. (1996) Protein Sci. 5, Dong, Q., Wang, X., Lin, L., Guan., Y. (2007) BMC Bioinformatics, 8, Zhang, X. (2006) Volume Graphics 4. Campbell, S. J., Gold, N. D., Jackson, R. M. & Westhead, D. R. (2003) Curr. Opin. Struct. Biol. 13,389– Binkowski, A., Naghibzadeg, S., Liang, J. (2003) Nucleic Acid Research, 31: L.Young, R.L.Jernigan, D.G.Covell. (1994) Protein Sci. 3: C.J.Tsai, S.L.Lin, H.J.Wolfson, R. Nussinov. (1997) Protein Sci. 6:  Effective binding site prediction is a primary step in the molecular recognition mechanism and function of a protein with an application in discovery of new HIV protease inhibitors that are active against mutant viruses  Accuracy of binding-site prediction can be improved using a combination of shape descriptors for the interfaces  We use geometrical, topological and functional descriptors in combination for ligand binding site prediction of HIV-1 protease  Research and statistical results has proved the importance of utilizing a combination of descriptors in predicting binding sites of proteins. In the future, we plan to extend the algorithm to include more shape descriptors like tightness of fit, curvature in fine tuning the binding site prediction  We plan to study the alternative sites for binding and the role of the attributes like volume, dipole moment, moment of inertia, quadruple moment, hydrophobicity, residue interface propensity, integral of properties, and, Betti numbers in the alternate binding site prediction  This study can be extended for other HIV targets namely reverse transcriptase, integrase, gp41 and their inhibitors Dataset: Mutated and Wild Proteins  The dataset for the algorithm for binding site prediction and extraction : 90 HIV protease protein (21 wild type, and 69 mutated) PDBs  The descriptors such as volume, dipole moment, moment of inertia, quadruple moment, hydrophobicity, residue interface propensity, integral of properties, and, Betti numbers are used for predicting the binding site  The largest pocket of the protein is invariably the binding site for the ligand and hence residue interface propensity and hydrophobicity values are calculated for this pocket  Predicted interface residues are residues with propensity >= 1.5. A propensity of 0 indicates that the amino acid has the same frequency in the interface and surface area  For this dataset, ALA, ASP, ARG and VAL have high preference in the interface  Predicted interface residues are distinctly hydrophobic. Protein Pocket Ligand Binding Site  PDB : 1B6J  Mutation : C67ABA, C95ABA, C167ABA, C195ABA  1B6J is a HIV protease complexed with macrocyclic peptidomimetic inhibitor 3D visualisation of protein, pocket and ligand and descriptor information Residue propensity and Hydrophobicity results for protein pocket Computational Approach:  Extract binding pockets present in mutated HIV protease proteins  Assign various descriptors such as area, volume, inertia, electrostatic potential, Betti numbers, residue interface propensity and hydrophobicity to nodes in the pockets for ‘matching score calculation’ and hence binding site prediction Residue Interface Propensity and Hydrophobicity:  Propensity for each amino acid is calculated as a fraction of the frequency that the amino acid contributes to the protein-ligand interface compared to the frequency that it contributes to the protein surface  As per the scale we use, hydrophobic residues are: Ala, Val, Leu, Ile, Pro, Met, Phe, Trp and Gly and the rest as hydrophilic MethodMethod Residue Interface Propensity Values PDBLargest Pocket Vol/ Ligand Vol Dipole Moment Len InertiaQuarduple moment p_IntegralBetti Numbers Protein 1B6J Ligand PI Algorithm for extracting and comparing binding sites:  Compute a volumetric pocket function to represent the 3D shapes of protein pockets  Compute an affine-invariant data structure called Multi-resolution contour tree (MACT) as a signature of the pocket function  Compute and assign geometrical, topological and functional attributes to the MACT and check for compatibility of proteins and ligands by comparing their MACTs  Ligands are commonly found to bind with one of the strongest hydrophobic clusters on the surface of the target protein molecule  If the distribution of residues occurring in the interface is compared with the distribution of residues occurring on the protein surface as a whole (residue interface propensity), a general indication of the hydrophobicity is obtained  Combination of these two features appears to be a powerful tool for fine tuning the binding pocket surface area to be considered for binding site prediction of proteins