Abstract
This paper presents the methods that have participated in the SHREC 2022 contest on protein–ligand binding site recognition. The prediction of protein- ligand binding regions is an active research domain in computational biophysics and structural biology and plays a relevant role for molecular docking and drug design. The goal of the contest is to assess the effectiveness of computational methods in recognizing ligand binding sites in a protein based on its geometrical structure. Performances of the segmentation algorithms are analyzed according to two evaluation scores describing the capacity of a putative pocket to contact a ligand and to pinpoint the correct binding region. Despite some methods perform remarkably, we show that simple non-machine-learning approaches remain very competitive against data-driven algorithms. In general, the task of pocket detection remains a challenging learning problem which suffers of intrinsic difficulties due to the lack of negative examples (data imbalance problem).
Original language | English (US) |
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Pages (from-to) | 20-31 |
Number of pages | 12 |
Journal | Computers and Graphics (Pergamon) |
Volume | 107 |
DOIs | |
State | Published - Oct 2022 |
Keywords
- 3D segmentation
- Binding site prediction
- Computational biology
- Molecular modeling
- SHREC
ASJC Scopus subject areas
- Software
- Signal Processing
- General Engineering
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design