scholarly journals Mapping fatty acid binding to β-lactoglobulin: Ligand binding is restricted by modification of Cys 121

1998 ◽  
Vol 7 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Mahesh Narayan ◽  
Lawrence J. Berliner
Keyword(s):  
Fatty Acid ◽  
Ligand Binding ◽  
Fatty Acid Binding ◽  
Β Lactoglobulin

scholarly journals Effect on ligand binding of arginine mutations in recombinant rat liver fatty acid-binding protein

1994 ◽  
Vol 297 (1) ◽  
pp. 103-107 ◽  
Author(s):  
A E Thumser ◽  
C Evans ◽  
A F Worrall ◽  
D C Wilton
Keyword(s):  
Fatty Acid ◽  
Ligand Binding ◽  
Rat Liver ◽  
Fatty Acid Binding Protein ◽  
Binding Protein ◽  
Liver Fatty Acid ◽  
Fatty Acid Binding ◽  
Acid Binding Protein ◽  
Wide Range ◽  
Arginine Residues

Rat liver fatty acid-binding protein is able to accommodate a wide range of non-polar anions in addition to long-chain fatty acids. The two arginine residues of rat liver fatty acid-binding protein, Arg122 and Arg126, have been mutated and the effect of mutation on ligand binding investigated. No significant decrease in affinity for the fluorescent fatty acid analogue, 11-(5-dimethylaminonaphthalenesulphonyl amino)undecanoic acid, or oleate was observed. However, the apparent affinity for oleoyl-CoA was slightly increased with the mutations Ala122 and Gln122 such that oleoyl-CoA rather than oleate became the preferred ligand for these mutants. Small changes in protein stability were observed with the Arg122 mutations. The lack of notable ionic involvement of the conserved internal residue Arg122 in ligand binding is consistent with the hypothesis that the mode of ligand binding in liver fatty acid-binding protein is markedly different from that of other members of this lipid-binding protein family.

scholarly journals PickPocket : Pocket binding prediction for specific ligands family using neural networks.

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida
Keyword(s):  
Neural Networks ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Data ◽  
Binding Pocket ◽  
Fatty Acid Binding Proteins ◽  
Binding Prediction ◽  
Fatty Acid Binding ◽  
Binding Pockets

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .

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