Analysis of surface structures of hydrogen bonding in protein–ligand interactions using the alpha shape model
Highlights
► Hydrogen bonds show consistent geometric patterns. ► Hydrogen atom–acceptor atom and donor atom–acceptor atom are spatially matched. ► Hydrogen bonding requires geometric complementarity.
Introduction
Interactions between a protein and another molecule such as another protein, DNA or a ligand take place in many biological processes. Among these interactions, protein–ligand interaction is of particular importance because it is related to the understanding of protein functions and drug development [1], [2]. In protein–ligand interactions, hydrogen bonding plays a significant role [3], [4]. Babine and Bender reported that hydrogen bonding contributed much to the stability in protein–ligand complexes [5]. Hydrogen bonding has already been used widely in the study of protein–ligand interactions. Gohlke et al. used hydrogen bonding to construct a scoring function for the prediction of protein–ligand interactions [6]. Luo et al. applied hydrogen bond matching to develop a fast protein–ligand docking algorithm [7]. Mancera developed a hydration penalty score for protein–ligand interactions using hydrogen bond formation [8]. Laurie and Jackson proposed to use the hydrogen bonding potential in the prediction of protein–ligand binding sites [9]. Although many studies show that surface properties play an important role in macromolecular interactions in general [10], [11], [12], [13], [14], few studies focus on the surface geometric properties of hydrogen bonding.
The alpha shape model can be applied to the study of 3D surface properties of biomolecules. This model was first used to study molecular volume computation and cavity detection in proteins by Liang et al. [15], [16]. Albou et al. used alpha shape to analyze the surface characteristics of proteins [17]. Recently, Zhou and Yan applied the alpha shape model to analyze the interface surface properties in protein-DNA and protein–protein interactions and achieved good results [10], [18]. These studies demonstrate that the alpha shape model is an effective means for the study of geometric properties of the interface in molecular interactions. In this Letter, we apply the 3D alpha shape model to represent the interface of a protein–ligand structure and use the solid angles at interface atoms to study the geometric properties of hydrogen bonds in the structure. Complementary geometric surface patterns are found in these hydrogen bonds.
Section snippets
Data selection
The experimental data used in this Letter were obtained from the protein–ligand structures in CCDC⧹Astex (http://www.ccdc.cam.ac.uk/products/life_sciences/gold/validation/astex/) [19]. The CCDC⧹Astex test set consists of 305 protein–ligand complexes with various proteins from different families and diverse ligand structures. These structures are already preprocessed and the coordinates of hydrogen atoms in the structures are added using SYBYL. The hydrogen atom coordinates are calculated only
Results and discussions
Potential hydrogen bonds are identified according to the atom types and spatial constraints. In order to evaluate the surface geometric properties of these bonds, we compute the alpha shapes of the protein and the ligand in each complex separately. After that, we search for the atoms of the hydrogen bonds in the alpha shape model to obtain the solid angles corresponding to the surface curvature of the atoms. In order to ensure the atoms on the surface of the alpha shape are solvent accessible,
Conclusions
In this Letter, we apply 3D alpha shape modeling to the study of the surface geometric properties of hydrogen bonding in protein–ligand complexes. The hydrogen bonds are classified into four types according their surface geometric properties. We use solid angles to evaluate the spatial match within the H–A and D–A pairs. The result demonstrates that hydrogen bonds show spatially matching patterns protein–ligand complexes. Our study here indicates that surface match play an important role in
Acknowledgement
This work is supported by the Hong Kong Research Grant Council (Project CityU 123809).
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