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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & graphics Jg. 107; S. 20 - 31
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.10.2022
Schlagworte:
3D segmentation
Binding site prediction
Computational biology
Molecular modeling
SHREC
SHREC
Binding site prediction
3D segmentation
Molecular modeling
Computational biology
ISSN:0097-8493, 1873-7684
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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