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Ligand Binding Site Prediction for HIV-1 Protease using Shape Comparison Techniques Manasi 1 Jahagirdar , Vivek K 2 Jalahalli , Sunil 1 Kumar , A. Srinivas 3 Reddy , Xiaoyu 4 Zhang and Rajni 5 Garg 1Dept. of Electrical And Computer Engineering, San Diego State University, CA, 2Dept. of Mathematics and Statistics, San Diego State University, CA 3Molecular Modeling Group, Indian Institute of Chemical Technology, Hyderabad, India 4Chemistry and Biochemistry Dept., California State University, San Marcos, CA, 5Computer Science Dept., California State University, San Marcos, CA Introduction 3D visualisation of protein, pocket and ligand and descriptor information Protein MUT-Details Q7K, L33I, C67ABA, C95ABA, Q107K, L133I, C167ABA, C195ABA Q7K, L33I, C67ABA, C95ABA A71V, V82T, I84V Q7K, D25N, L63P, V82A - PDB : 1B6J Mutation : C67ABA, C95ABA, C167ABA, C195ABA 1B6J is a HIV protease complexed with macrocyclic peptidomimetic inhibitor Pocket PDB Ligand Protein 1B6J Largest Pocket Vol/ Ligand Vol Dipole Moment Len Inertia 862.77362 11.0749 151.16163 141.32252 27.399763 Quarduple moment p_Integral Residue Interface Propensity Values Amino Acid ALA ARG ASN ASP CYS GLN GLU GLY HIS ILE LEU LYS MET PHE PRO SER THR TRP TYR VAL PDB Ligands 1B6P PI7 1D4K PI8 1LZQ BOC- POO-pep(FEF)- NH2 1MT8 pep(ARVLAEAM) 1AAQ PSI 1C70 L75 1DMP 450 1EC2 BEJ Residue Interface Propensity - 1D4K 123.522423 -22.102247 -101.420181 -2910.009 0.00000 33.238232 30.054001 6.967624 49.356991 -19.902149 -29.454842 354.51690 1D4K 4.18 2.09 0 3.14 0 0 0 1.09 0 1.47 2.09 0 0 0 1.39 0 0.69 0 0 4.18 1LZQ 5.08 2.54 0 2.91 0 0 0 1.27 0 0.78 1.27 0 0 0 2.03 0 1.34 0 0 2.5 1MT8 4.48 1.24 0 0.93 0 0 0.56 1.12 0 1.53 2.4 0 0 1.4 0.93 0 0.93 0 0 5.6 1AAQ 2.13 2.13 0 3.19 0 0 0 1.16 0 1.42 1.42 0 0 0 0.85 0 0.71 0 0 4.26 1C70 4.08 1.02 0 3.06 0 0 0 5.75 0 1.78 1.02 0 0 2.04 1.22 0 0.34 0 0 2.72 1DMP 3.04 1.52 0 3.42 0 0 0 1.24 0 1.71 1.14 0 0 0 0.83 0 0.83 0 0 3.04 1EC2 4.05 2.02 0 3.04 0 0 0 1.05 0 1.52 1.62 0 0 0 1.22 0 0.67 0 0 3.24 141 000 060 11011 4.5 4 4 3.5 Future Work 3 3 2.5 2 2.5 2 1.5 1.5 1 1 0.5 0.5 LE U LY S M E T PH E PR O SE R TH R TR P TY R VA L Am ino Acid Am ino Acid Hydrophobic and hydrophilic amino acid distribution at interface for 8 proteins in the dataset Comparison of residue interface propensity values 7 40 6 35 5 Total Amino acid count Computational Approach: Algorithm for extracting and Extract binding pockets present in mutated HIV comparing binding sites: protease proteins Compute a volumetric pocket Assign various descriptors such as area, volume, function to represent the 3D inertia, electrostatic potential, Betti numbers, shapes of protein pockets residue interface propensity and hydrophobicity Compute an affine-invariant data to nodes in the pockets for ‘matching score structure called Multi-resolution calculation’ and hence binding site prediction contour tree (MACT) as a Residue Interface Propensity and Hydrophobicity: signature of the pocket function Propensity for each amino acid is calculated as a Compute and assign geometrical, fraction of the frequency that the amino acid topological and functional contributes to the protein-ligand interface attributes to the MACT and check compared to the frequency that it contributes to for compatibility of proteins and the protein surface ligands by comparing their As per the scale we use, hydrophobic residues MACTs are: Ala, Val, Leu, Ile, Pro, Met, Phe, Trp and Gly and the rest as hydrophilic IL E HI S P CY S G LN G LU G LY AS AL A AR G R TR P TY R VA L R TH E T O SE PR PH M E LE U LY S IL E IS H G LY LU G LN G YS P C AS N AS N 0 0 AS Method 4 3 2 1 0 ALA ARG ASN ASP CYS GLN GLU GLY HIS ILE LEU LYS MET PHE PRO SER THR TRP TYR VAL 30 25 20 15 10 Ala Amino Acid Val Leu Ile Pro Met Phe Trp Gly Ser Thr Cys Asn Gln Tyr Asp Glu Lys Arg Amino acid 1B6P 1D4K 1LZQ 1MT8 1AAQ 1C70 1DMP 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 5 0 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. Residue Interface Propensity - 1DMP Propensity Binding Site 354.51686 1B6P 3 1.63 0 3.37 0 0 0 1.17 0 1.23 1.28 0 0 1.125 1.125 0 0.75 0 0 4.5 Betti Numbers 3.5 Ligand PI1 Discussion Residue propensity and Hydrophobicity results for protein pocket AL A AR G Dataset: Mutated and Wild Proteins Propensity 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 Propensity Description His References 1EC2 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 1. Laskowski, R. A., Luscombe, N. M., Swindells, M. B. & Thornton, J. M. (1996) Protein Sci. 5, 2438-2452 2. Dong, Q., Wang, X., Lin, L., Guan., Y. (2007) BMC Bioinformatics, 8, 1471-2105 3. Zhang, X. (2006) Volume Graphics 4. Campbell, S. J., Gold, N. D., Jackson, R. M. & Westhead, D. R. (2003) Curr. Opin. Struct. Biol. 13,389–395 5. Binkowski, A., Naghibzadeg, S., Liang, J. (2003) Nucleic Acid Research, 31:3352-3355 6. L.Young, R.L.Jernigan, D.G.Covell. (1994) Protein Sci. 3: 717-729 7. C.J.Tsai, S.L.Lin, H.J.Wolfson, R. Nussinov. (1997) Protein Sci. 6: 53-64