Article

Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparison

EMBL-EBI, Cambridge, England, United Kingdom
Bioinformatics (Impact Factor: 4.62). 06/2005; 21(10):2347-55. DOI: 10.1093/bioinformatics/bti337
Source: PubMed

ABSTRACT An increasing number of protein structures are being determined for which no biochemical characterization is available. The analysis of protein structure and function assignment is becoming an unexpected challenge and a major bottleneck towards the goal of well-annotated genomes. As shape plays a crucial role in biomolecular recognition and function, the examination and development of shape description and comparison techniques is likely to be of prime importance for understanding protein structure-function relationships.
A novel technique is presented for the comparison of protein binding pockets. The method uses the coefficients of a real spherical harmonics expansion to describe the shape of a protein's binding pocket. Shape similarity is computed as the L2 distance in coefficient space. Such comparisons in several thousands per second can be carried out on a standard linux PC. Other properties such as the electrostatic potential fit seamlessly into the same framework. The method can also be used directly for describing the shape of proteins and other molecules.
A limited version of the software for the real spherical harmonics expansion of a set of points in PDB format is freely available upon request from the authors. Binding pocket comparisons and ligand prediction will be made available through the protein structure annotation pipeline Profunc (written by Roman Laskowski) which will be accessible from the EBI website shortly.

Download full-text

Full-text

Available from: Abdullah Kahraman, Sep 03, 2015
0 Followers
 · 
241 Views
 · 
109 Downloads
  • Source
    • "SPHARM therefore have global spatial support, and each coefficient describes the general conformation of the shape of interest at different spatial scales. Applications of SPHARM include molecular surface modeling [50], [51], medical-shape analysis [52], and cellshape analysis [15], [16], [19], [53]. In the latter case, the function basis is formed of wavelets (hence its name), and are constructed by analogy to wavelets in the plane via appropriate spherical projections [17], [20], [54]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Modern developments in light microscopy have allowed the observation of cell deformation with remarkable spatiotemporal resolution and reproducibility. Analyzing such phenomena is of particular interest for the signal processing and computer vision communities due to the numerous computational challenges involved, from image acquisition all the way to shape analysis and pattern recognition and interpretation. This article aims at providing an up-to-date overview of the problems, solutions, and remaining challenges in deciphering the morphology of living cells via computerized approaches, with a particular focus on shape description frameworks and their exploitation using machine-learning techniques. As a concrete illustration, we use our recently acquired data on amoeboid cell deformation, motivated by its direct implication in immune responses, bacterial invasion, and cancer metastasis.
    IEEE Signal Processing Magazine 01/2015; 32(1):30-40. DOI:10.1109/MSP.2014.2359131 · 4.48 Impact Factor
  • Source
    • "Moment-based methods can naturally control the resolution of the surface description , and physicochemical properties on the surface can be represented in the same way as surface shape. Thornton and her colleagues used spherical harmonics for describing binding pockets (Kahraman et al., 2007; Morris et al., 2005). In our earlier works, Pocket-Surfer, global pocket shape and the surface electrostatic potential are represented using 3D Zernike descriptors (3DZD; Chikhi et al., 2010). "
    [Show abstract] [Hide abstract]
    ABSTRACT: MOTIVATION: Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects. RESULTS: We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patches that characterize physicochemical properties of the local regions, the method can identify binding pockets of the same ligand even if they do not share globally similar shapes. Properties of local patches are represented by an efficient mathematical representation, 3D Zernike Descriptor. Patch-Surfer2.0 has significant technical improvements over our previous prototype, which includes a new feature that captures approximate patch position with a geodesic distance histogram. Moreover, we constructed a large comprehensive database of ligand binding pockets that will be searched against by a query. The benchmark shows better performance of Patch-Surfer2.0 over existing methods. Availability and implementation: http://kiharalab.org/patchsurfer2.0/.
    Bioinformatics 10/2014; 31(5). DOI:10.1093/bioinformatics/btu724 · 4.62 Impact Factor
  • Source
    • "Another method is to describe hydrophobic features of the protein using turns in main chain atoms. Yet another approach is to use a Fourier shape descriptor technique (5, 6). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Objective(s): A fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. Artificial neural networks (ANNs) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. We proposed a structure that obtains binding energy using physicochemical molecular descriptions of the selected drugs. Material and Methods: The set of 33 drugs with their binding energy to cyclooxygenase enzyme (COX2) in hand, from different structure groups, were considered. 27 physicochemical property descriptors were calculated by standard molecular modeling. Binding energy was calculated for each compound through docking and also ANN. A multi-layer perceptron neural network was used. Results: The proposed ANN model based on selected molecular descriptors showed a high degree of correlation between binding energy observed and calculated. The final model possessed a 27-4-1 architecture and correlation coefficients for learning, validating and testing sets equaled 0.973, 0.956 and 0.950, respectively. Conclusion: Results show that docking results and ANN data have a high correlation. It was shown that ANN is a strong tool for prediction of the binding energy and thus inhibition constants for different drugs in very short periods of time.
    Iranian Journal of Basic Medical Science 11/2013; 16(11):1196-202. · 0.60 Impact Factor
Show more