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...
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| Vydané v: | Computers & graphics Ročník 107; s. 20 - 31 |
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| Jazyk: | English |
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Elsevier Ltd
01.10.2022
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| ISSN: | 0097-8493, 1873-7684 |
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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).
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•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. |
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| AbstractList | 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. |
| Author | Axenopoulos, Apostolos Zhang, Yuanyuan Kihara, Daisuke Biasotti, Silvia Raffo, Andrea Mylonas, Stelios Gagliardi, Luca Rocchia, Walter Amor, Boulbaba Ben Huang, Hao Daras, Petros Fugacci, Ulderico Fang, Yi Christoffer, Charles Wang, Xiao |
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| Keywords | SHREC Binding site prediction 3D segmentation Molecular modeling Computational biology |
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| SubjectTerms | 3D segmentation Binding site prediction Computational biology Molecular modeling SHREC |
| Title | SHREC 2022: Protein–ligand binding site recognition |
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