Pocket to concavity: a tool for the refinement of protein–ligand binding site shape from alpha spheres

概要

Bioinformatics, 39(4), 2023, btad212
https://doi.org/10.1093/bioinformatics/btad212
Advance Access Publication Date: 22 April 2023
Applications Note

Structural bioinformatics

Genki Kudo 1,*, Takumi Hirao2,3, Ryunosuke Yoshino3,4, Yasuteru Shigeta5,
Takatsugu Hirokawa3,4,*
1

Physics Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
Master’s Program in Medical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki
305-8575, Japan
3
Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan
4
Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan
5
Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
2

*Corresponding author. Physics Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba,
Ibaraki 305-8571, Japan. E-mail: s2330052@u.tsukuba.ac.jp (G.K.); Division of Biomedical Science, Faculty of Medicine, University of Tsukuba,
1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan. E-mail: t-hirokawa@md.tsukuba.ac.jp (T.H.)
Associate Editor: Alfonso Valencia
Received 20 October 2022; revised 24 February 2023; accepted 13 April 2023

Abstract
Summary: Understanding the binding site of the target protein is essential for rational drug design. Pocket detection
software predicts the ligand binding site of the target protein; however, the predicted protein pockets are often excessively estimated in comparison with the actual volume of the bound ligands. This study proposes a refinement
tool for the pockets predicted by an alpha sphere-based approach, Pocket to Concavity (P2C). P2C is divided into two
modes: Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shape of the deep and druggable
concavity where the core scaffold can bind. The LB mode searches the deep concavity around the bound ligand.
Thus, P2C is useful for identifying and designing desirable compounds in Structure-Based Drug Design (SBDD).
Availability and implementation: Pocket to Concavity is freely available at https://github.com/genki-kudo/Pocket-toConcavity. This tool is implemented in Python3 and Fpocket2.

Graphical Abstract

C The Author(s) 2023. Published by Oxford University Press.
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Pocket to concavity: a tool for the refinement of
protein–ligand binding site shape from alpha spheres

2

Kudo et al.

1 Introduction

2 Features of the P2C process
Figure 1 shows the workflow for P2C. There are two main modes:
Ligand-Free (LF) and Ligand-Bound (LB) modes. The LF mode provides the shaped-up pocket using the 3D protein structure. Whereas
the LB mode searches unoccupied empty pockets around the bound
ligand using the 3D complex structure. Details of inputs and outputs
are provided in the Supplementary Information (Appendix S0). The
P2C process can be explained in the following four major steps (further information in Supplementary Appendix S1):
1. Alpha-spheres generation: The pockets and element, termed
alpha-spheres, were generated based on Voronoi tessellation
(Liang et al. 1998). Fpocket was used as the generator in the default P2C settings. The alpha-spheres coordinate file can be
specified in place of this default generator.
2. Pocket selection: In the LF mode, pockets predicted by fpocket
with high druggability were selected for P2C processing. In the
LB mode, pockets around the bound ligand in the complex structure were selected for P2C processing. The range of alphaspheres around the ligand can be specified.
3. Alpha-spheres elimination: Selected pockets were subjected to
alpha-spheres elimination, the main step of P2C processing, and
were refined for precise shape. In the elimination process, the
density of each alpha-sphere was calculated, and alpha-spheres
with low density were deleted (density parameter optimization is
provided in Supplementary Information Appendix S2).
4. Empty site identification (the LB mode only): The refined pocket
was aligned with the ligand, and the alpha spheres that overlapped the ligand were deleted. The remaining alpha spheres
were re-clustered and re-defined as empty sites.
P2C was applied to the test dataset and Hit-to-Lead complexes.
Approximately 80% of the complexes in the test dataset showed

Figure 1. Pocket to concavity process. The input and output are shown in blue and
red squares, respectively.

improved accuracy of the volume overlap between the refined
pocket shape by P2C processing and the actual ligand. Furthermore,
the alpha-spheres of complexes with hit compounds reproduced the
shape of the lead compounds after P2C processing (Supplementary
Appendixes S3 and S4). In addition, a case study for each mode is
included in the Supplementary Information (Supplementary
Appendix S5). The results suggest that P2C detects the deep concavity where the active ligand can bind. This tool is useful for identifying and designing desirable compounds in SBDD.

3 Conclusions
P2C can clearly and accurately refine pockets predicted by an alpha
sphere-based approach. Refined pockets reproduce the deep concavity where the active ligand can bind. The algorithm is simple but effective and can be applied to various protein structures with or
without the bound ligand. Using the 3D structure of the target protein, anyone can accurately predict the binding site and the shape of
the actual bound ligand.

Supplementary data
Supplementary data are available at Bioinformatics online.

Conflict of interest
None declared.

Funding
This work was supported by the Research Support Project for Life Science
and Drug Discovery (Basis for Supporting Innovative Drug Discovery and
Life Science Research; BINDS) from AMED [JP22ama121029j0001].

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Structure-based drug design (SBDD) is a method of designing drugs
based on the structural information of their target proteins (Lionta
et al. 2014). Recently, SBDD has been used often due to the
increased number of available three-dimensional (3D) protein structures owing to crystal structure analysis and Cryo-electron microscopy (Renaud et al. 2018). Furthermore, predicted protein
structures using AlphaFold2 accelerate SBDD (Jumper et al. 2021).
The first step in SBDD is identifying the binding sites in the target
protein structure. A pocket detection software is expected to provide
information on the pockets, such as druggable sites, docking space,
and desirable ligand size for drug discovery. Pocket detection software using an alpha sphere-based approach, such as Fpocket and
Sitefinder on MOE (Chemical Computing Group Inc.), generates
pseudo-atoms (alpha-spheres) based on Voronoi tessellation of the
protein surface and accurately predicts druggable sites and docking
spaces (Le Guilloux et al. 2009; Schmidtke et al. 2010). However, it
provides insufficient information on the desirable ligand size as the
predicted pockets by pocket detection software are often larger than
the volume of the actual bound ligands (Supplementary Table S1;
Gagliardi et al. 2022). The lack of information causes a hindrance
to accurate and rational drug design.
This study proposes a tool, Pocket to Concavity (P2C), that
refines the pocket shape overestimated by the alpha sphere-based
approach to follow the actual ligand shape. This tool helps understand binding sites more clearly to select deep concavity where the
core scaffold can bind. The algorithm of P2C is simple and easy to
implement, making it an effective and practical approach for accurate pocket refinement. P2C showed that 80% of the test dataset
improved in the volume overlap between the actual bound ligand
and the pocket.

The refinement of protein-ligand binding site shape

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