Binding ligand prediction for proteins using partial matching of local surface patches.

Lee Sael,Daisuke Kihara

doi:10.3390/ijms11125009

Lee Sael, Daisuke Kihara

Open Access

https://doi.org/10.3390/ijms11125009

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Abstract

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

Highlights

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics [1,2,3,4]
This paper proposes a local surface patch method that analyzes the similarities between binding pockets by segmenting pocket region to smaller surface patches and comparing the pockets based on the shape of the patches
To make individual curves more visible, the ligand types are arbitrarily divided into two groups that show similar trends: The first group contains pockets that bind to ATP, flavin adenine dinucleotide (FAD), flavin mononucleotide (FMN), nicotinamide adenine dinucleotide (NAD), and STR

Summary

Introduction

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics [1,2,3,4]. Computational function prediction methods typically search for similar sequential/structural patterns taken from the protein of unknown function in known proteins. Functional characterization of proteins from their tertiary structures is becoming more important as an increasing number of protein structures of unknown function are being solved. As of October 2010, there are 3221 out of 68421 structures of unknown function in the Protein Data Bank (PDB) [5], most of which were solved by Structural Genomics projects [6]. They do not have homologous proteins of known function as they do not have even electronic annotations. Using structural information is a promising way for non-homology based function prediction

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Conclusion