Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods

2019 ◽  
Vol 15 (3) ◽  
pp. 205-215 ◽  
Author(s):  
Renxiang Yan ◽  
Xiaofeng Wang ◽  
Yarong Tian ◽  
Jing Xu ◽  
Xiaoli Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binding Site ◽  
Binding Sites ◽  
Zinc Binding ◽  
Biological Mechanisms ◽  
Learning Methods ◽  
Multiple Sequence ◽  
Machine Learning Methods ◽  
Zinc Binding Site ◽  
Sequence Profiles

The zinc (Zn2+) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins.

scholarly journals Histidine residues of zinc ligands in β-lactamase II

1978 ◽  
Vol 175 (2) ◽  
pp. 441-447 ◽  
Author(s):  
G S Baldwin ◽  
A Galdes ◽  
H A O Hill ◽  
B E Smith ◽  
S G Waley ◽  
...  
Keyword(s):  
Bacillus Cereus ◽  
Binding Site ◽  
Binding Sites ◽  
Zinc Binding ◽  
Histidine Residue ◽  
Beta Lactamase ◽  
Histidine Residues ◽  
Zinc Enzyme ◽  
Zinc Binding Site ◽  
Ph Range

1. The Zn(II)-requiring beta-lactamase from Bacillus cereus 569/H/9, which has two zinc-binding sites, was examined by 270 MHz 1H n.m.r. spectroscopy. Resonances were assigned to five histidine residues. 2. Resonances attributed to three of the histidine residues in the apoenzyme shift on the addition of one equivalent of Zn(II). 3. Although these three histidine residues are free to titrate in the apoenzyme, none of them titrates over the pH range 6.0–9.0 in the mono-zinc enzyme. 4. The ability of the C-2 protons of these three histidine residues to exchange with solvent (2H2O) is markedly decreased on Zn(II) binding. 5. It is proposed that these three histidine residues act as zinc ligands at the tighter zinc-binding site. 6. Resonances attributed to a fourth histidine residue shift on addition of further zinc to the mono-zinc enzyme. It is proposed that this histidine residue acts as a Zn(II) ligand at the second zinc-binding site.

scholarly journals Zincbindpredict—Prediction of Zinc Binding Sites in Proteins

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 966
Author(s):  
Sam M. Ireland ◽  
Andrew C. R. Martin
Keyword(s):  
Binding Site ◽  
Binding Sites ◽  
Computational Models ◽  
Significant Proportion ◽  
Zinc Binding ◽  
Web Interface ◽  
Zinc Binding Site ◽  
Experimental Approaches ◽  
Better Than ◽  
Novel Protein

Background: Zinc binding proteins make up a significant proportion of the proteomes of most organisms and, within those proteins, zinc performs rôles in catalysis and structure stabilisation. Identifying the ability to bind zinc in a novel protein can offer insights into its functions and the mechanism by which it carries out those functions. Computational means of doing so are faster than spectroscopic means, allowing for searching at much greater speeds and scales, and thereby guiding complimentary experimental approaches. Typically, computational models of zinc binding predict zinc binding for individual residues rather than as a single binding site, and typically do not distinguish between different classes of binding site—missing crucial properties indicative of zinc binding. Methods: Previously, we created ZincBindDB, a continuously updated database of known zinc binding sites, categorised by family (the set of liganding residues). Here, we use this dataset to create ZincBindPredict, a set of machine learning methods to predict the most common zinc binding site families for both structure and sequence. Results: The models all achieve an MCC ≥ 0.88, recall ≥ 0.93 and precision ≥ 0.91 for the structural models (mean MCC = 0.97), while the sequence models have MCC ≥ 0.64, recall ≥ 0.80 and precision ≥ 0.83 (mean MCC = 0.87), with the models for binding sites containing four liganding residues performing much better than this. Conclusions: The predictors outperform competing zinc binding site predictors and are available online via a web interface and a GraphQL API.

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