SHREC 2022: Protein-ligand binding site recognition
Luca Gagliardi, Andrea Raffo, Ulderico Fugacci, Silvia Biasotti, Walter Rocchia, Hao Huang, Boulbaba Ben Amor, Yi Fang, Yuanyuan Zhang, Xiao Wang, Charles Christoffer, Daisuke Kihara, Apostolos Axenopoulos, Stelios Mylonas, Petros Daras
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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).