SHREC 2022: Protein–ligand binding site recognition. (October 2022) - Gagliardi, Luca; Raffo, Andrea; Fugacci, Ulderico; Biasotti, Silvia; Rocchia, Walter; Huang, Hao; Amor, Boulbaba Ben; Fang, Yi; Zhang, Yuanyuan; Wang, Xiao; Christoffer, Charles; Kihara, Daisuke; et al.
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). Graphical abstract: Highlights: A new contest for binding site detection in a protein. Proteins are provided both as molecular surfaces and in anonymized PQR format. Analysis of computational methods for recognizing ligand binding sites in a protein. Analysis of the performances of the methods that participated in SHREC 2022.