scholarly journals Relating structure to thermodynamics: The crystal structures and binding affinity of eight OppA-peptide complexes

1999 ◽  
Vol 8 (7) ◽  
pp. 1432-1444 ◽  
Author(s):  
Thomas G. Davies ◽  
Roderick E. Hubbard ◽  
JEREMY R.H. Tame
Keyword(s):  
Crystal Structures ◽  
Binding Affinity ◽  
Peptide Complexes

scholarly journals Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

2021 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett Morris
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Crystal Structures ◽  
Binding Affinity ◽  
Scoring Function ◽  
Scoring Functions ◽  
Data Set ◽  
Core Sets ◽  
Strong Performance

Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes.<br><br>We explore how the use of docked, rather than crystallographic, poses for both training and testing affects the performance of machine learning scoring functions. Using the PDBbind Core Sets as benchmarks, we show that the performance of a structure-based machine learning scoring function trained and tested on docked poses is lower than that of the same scoring function trained and tested on crystallographic poses. We construct a hybrid scoring function by combining both structure-based and ligand-based features, and show that its ability to predict binding affinity using docked poses is comparable to that of purely structure-based scoring functions trained and tested on crystal poses. Despite strong performance on docked poses of the PDBbind Core Sets, we find that our hybrid scoring function fails to generalise to anew data set, demonstrating the need for improved scoring functions and additional validation benchmarks. <br><br>Code and data to reproduce our results are available from https://github.com/oxpig/learning-from-docked-poses.

scholarly journals Crystal Structures of Two Closely Related but Antigenically Distinct HLA-A2/Melanocyte-Melanoma Tumor-Antigen Peptide Complexes

2001 ◽  
Vol 167 (6) ◽  
pp. 3276-3284 ◽  
Author(s):  
Piotr Sliz ◽  
Olivier Michielin ◽  
Jean-Charles Cerottini ◽  
Immanuel Luescher ◽  
Pedro Romero ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Tumor Antigen ◽  
Melanoma Tumor ◽  
Antigen Peptide ◽  
Peptide Complexes

scholarly journals Identification and characterization of two drug-like fragments that bind to the same cryptic binding pocket of Burkholderia pseudomallei DsbA

2021 ◽  
Author(s):  
Guillaume A. Petit ◽  
Biswarajan Mohanty ◽  
Róisín M. McMahon ◽  
Stefan Nebl ◽  
David H. Hilko ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Binding Affinity ◽  
Disulfide Bond ◽  
Burkholderia Pseudomallei ◽  
Protein A ◽  
Binding Pocket ◽  
New Drugs ◽  
Nmr Studies ◽  
Small Surface ◽  
Starting Point

AbstractDiSulfide Bond forming proteins (DSB) play a crucial role in the pathogenicity of many Gram-negative bacteria. Disulfide bond protein A (DsbA) catalyzes the formation of disulfide bonds necessary for the activity and stability of multiple substrate proteins, including many virulence factors. Hence, DsbA is an attractive target for the development of new drugs to combat bacterial infections. Here, we identified two fragments - 1 (bromophenoxy propanamide) and 2 (4-methoxy-N-phenylbenzenesulfonamide), that bind to the DsbA from the pathogenic bacterium Burkholderia pseudomallei, the causative agent of melioidosis. Crystal structures of the oxidized B. pseudomallei DsbA (termed BpsDsbA) co-crystallized with 1 or 2 suggests that both fragments bind to a hydrophobic pocket that is formed by a change in the side chain orientation of tyrosine 110. This conformational change opens a “cryptic” pocket that is not evident in the apo-protein structure. This binding location was supported by 2D-NMR studies which identified a chemical shift perturbation of the tyrosine 110 backbone amide resonance of more than 0.05 ppm upon addition of 2 mM of fragment 1 and over 0.04 ppm upon addition of 1 mM of fragment 2. Although binding was detected by both X-ray crystallography and NMR, the binding affinity (KD) for both fragments was low (above 2 mM), suggesting weak interactions with BpsDsbA. This conclusion is also supported by the modelled crystal structures which ascribe partial occupancy to the ligands in the cryptic binding pocket. Small fragments such as 1 and 2 are not expected to have high binding affinity due to their size and the relatively small surface area that can be involved in intermolecular interactions. However, their simplicity makes them ideal for functionalization and optimization. Identification of the binding sites of 1 and 2 to BpsDsbA could provide a starting point for the development of more potent novel antimicrobial compounds that target DsbA and bacterial virulence.SynopsisDescribes the binding properties of two drug-like fragments to a conformationally dynamic site in the disulfide-bond forming protein A from Burkholderia pseudomallei.

scholarly journals FSH and TSH binding to their respective receptors: similarities, differences and implication for glycoprotein hormone specificity

2008 ◽  
Vol 41 (3) ◽  
pp. 145-164 ◽  
Author(s):  
R Núñez Miguel ◽  
J Sanders ◽  
D Y Chirgadze ◽  
T L Blundell ◽  
J Furmaniak ◽  
...  
Keyword(s):  
Crystal Structures ◽  
Binding Affinity ◽  
Binding Specificity ◽  
Receptor Activation ◽  
Activation Process ◽  
Leucine Rich Repeat ◽  
Glycoprotein Hormone ◽  
Repeat Domain ◽  
Leucine Rich Repeats ◽  
Hormone Specificity

The crystal structures of the leucine-rich repeat domain (LRD) of the FSH receptor (FSHR) in complex with FSH and the TSH receptor (TSHR) LRD in complex with the thyroid-stimulating autoantibody (M22) provide opportunities to assess the molecular basis of the specificity of glycoprotein hormone–receptor binding. A comparative model of the TSH–TSHR complex was built using the two solved crystal structures and verified using studies on receptor affinity and activation. Analysis of the FSH–FSHR and TSH–TSHR complexes allowed identification of receptor residues that may be important in hormone-binding specificity. These residues are in leucine-rich repeats at the two ends of the FSHR and the TSHR LRD structures but not in their central repeats. Interactions in the interfaces are consistent with a higher FSH-binding affinity for the FSHR compared with the binding affinity of TSH for the TSHR. The higher binding affinity of porcine (p)TSH and bovine (b)TSH for human (h)TSHR compared with hTSH appears not to be dependent on interactions with the TSHR LRD as none of the residues that differ among hTSH, pTSH or bTSH interact with the LRD. This suggests that TSHs are likely to interact with other parts of the receptors in addition to the LRD with these non-LRD interactions being responsible for affinity differences. Analysis of interactions in the FSH–FSHR and TSH–TSHR complexes suggests that the α-chains of both hormones tend to be involved in the receptor activation process while the β-chains are more involved in defining binding specificity.

scholarly journals High-resolution crystal structures leverage protein binding affinity predictions

2015 ◽  
Vol 84 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Simon Marillet ◽  
Pierre Boudinot ◽  
Frédéric Cazals
Keyword(s):  
High Resolution ◽  
Protein Binding ◽  
Crystal Structures ◽  
Binding Affinity

scholarly journals Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained On Docked Poses

2021 ◽  
Author(s):  
Fergus Boyles ◽  
Charlotte M Deane ◽  
Garrett Morris
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Crystal Structures ◽  
Binding Affinity ◽  
Scoring Function ◽  
Scoring Functions ◽  
Data Set ◽  
Core Sets ◽  
Strong Performance

Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear how these methods perform when applied to docked poses of complexes.<br><br>We explore how the use of docked, rather than crystallographic, poses for both training and testing affects the performance of machine learning scoring functions. Using the PDBbind Core Sets as benchmarks, we show that the performance of a structure-based machine learning scoring function trained and tested on docked poses is lower than that of the same scoring function trained and tested on crystallographic poses. We construct a hybrid scoring function by combining both structure-based and ligand-based features, and show that its ability to predict binding affinity using docked poses is comparable to that of purely structure-based scoring functions trained and tested on crystal poses. Despite strong performance on docked poses of the PDBbind Core Sets, we find that our hybrid scoring function fails to generalise to anew data set, demonstrating the need for improved scoring functions and additional validation benchmarks. <br><br>Code and data to reproduce our results are available from https://github.com/oxpig/learning-from-docked-poses.

Copper(ii) complexes with NNO ligands: synthesis, crystal structures, DNA cleavage, and anticancer activities

2019 ◽  
Vol 48 (48) ◽  
pp. 17925-17935 ◽  
Author(s):  
Ping Yang ◽  
Dan-Dan Zhang ◽  
Zi-Zhou Wang ◽  
Hui-Zhong Liu ◽  
Qing-Shan Shi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Crystal Structures ◽  
Binding Affinity ◽  
Dna Cleavage ◽  
Anticancer Activities ◽  
Dna Binding Affinity

The copper(ii) complexes of aroylhydrazone ligands exhibit strong DNA binding affinity and prominent ds DNA cleavage and cytotoxicity.

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