IDENTIFICATION OF PROTEIN-PROTEIN BINDING SITES BY INCORPORATING THE PHYSICOCHEMICAL PROPERTIES AND STATIONARY WAVELET TRANSFORMS INTO PSEUDO AMINO ACID COMPOSITION
Keywords:
protein–protein binding sites; physicochemical property; stationary wavelet transform; pseudo amino acid composition; random forest; asymmetric bootstrap.Abstract
With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein–protein binding sites (PPBS) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBSPseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual “Random Forest” classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties.
References
Althaus, I. W., Gonzales, A. J., Diebel, M. R., Kezdy, F. J.,Aristoff, P. A., Tarpley, W. G., & Reusser, F. (1993). Kinetic studies with the nonucleoside HIV-1 reverse transcriptase inhibitor U-88204E.
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., … Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28, 235–242.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Cai, Y. D. & Chou, K. C. (2003). Nearest neighbour algorithm for predicting protein subcellular location by combining functional domain composition and pseudo-amino acid composition. Biochemical and Biophysical Research Communications, 305, 407–411.
Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K., & Sattar, A. (2015). Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC. Journal Theoretical Biology, 364, 284–294.
Eisenberg, D., Schwarz, E., Komaromy, M., & Wall, R. (1984). Analysis of membrane and surface protein sequences with the hydrophobic moment plot. Journal of Molecular Biology, 179, 125–142.
Gallet, X., Charloteaux, B., Thomas, A., & Brasseur, R. (2000). A fast method to predict protein interaction sites from sequences. Journal of Molecular Biology, 302, 917–926.
Kandaswamy, K. K., Martinetz, T., Moller, S., Sridharan, S., Pugalenthi (2011). AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. Journal of Theoretical Biology, 270, 56–62.
Ofran, Y., & Rost, B. (2003). Predicted protein–protein interaction sites from local sequence information. FEBS Letters, 544, 236–239.
Schnell, J. R., & Chou, J. J. (2008). Structure and mechanism of the M2 proton channel of influenza A virus. Nature, 451, 591–595.
Kobilova G.I. et al. INNOVATSION TEXNOLOGIYALARNI ISHLAB CHIQARISHGA JORIY ETISH //Educational Research in Universal Sciences. – 2023. – Т. 2. – №. 1 SPECIAL. – С. 108-111.
Кобилова Г.И. ЭФФЕКТИВНОЕ ИСПОЛЬЗОВАНИЕ ОТХОДОВ ПРИ ПРОИЗВОДСТВЕ КОНСЕРВИРОВАНИЯ. – 2023.
