SHREC 2022: Protein–ligand binding site recognition

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 docki...

Full description

Saved in:
Bibliographic Details
Published inComputers & graphics Vol. 107; pp. 20 - 31
Main Authors 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, Axenopoulos, Apostolos, Mylonas, Stelios, Daras, Petros
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2022
Subjects
3D segmentation
Binding site prediction
Computational biology
Molecular modeling
SHREC
SHREC
Binding site prediction
3D segmentation
Molecular modeling
Computational biology
Online AccessGet full text

Cover

More Information
Summary: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). [Display omitted] •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.
ISSN:0097-8493
1873-7684
DOI:10.1016/j.cag.2022.07.005