X-ray Crystallography and Computational Docking for the Detection and Development of Protein–Ligand Interactions

2013 ◽  
Vol 20 (4) ◽  
pp. 569-575
Author(s):  
N.M. Kershaw ◽  
G.S.A. Wright ◽  
R. Sharma ◽  
S.V. Antonyuk ◽  
R.W. Strange ◽  
...  
Keyword(s):  
Computational Docking ◽  
X Ray ◽  
X Ray Crystallography ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

X-ray Crystallography and Computational Docking for the Detection and Development of Protein–Ligand Interactions

2013 ◽  
Vol 20 (4) ◽  
pp. 569-575 ◽  
Author(s):  
N.M. Kershaw ◽  
G.S.A. Wright ◽  
R. Sharma ◽  
S.V. Antonyuk ◽  
R.W. Strange ◽  
...  
Keyword(s):  
Computational Docking ◽  
X Ray ◽  
X Ray Crystallography ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Computational Investigation of Versatile Activity of Piperine

2019 ◽  
pp. 127-139
Author(s):  
Thenmozhi Marudhadurai ◽  
Navabshan Irfan
Keyword(s):  
Docking Study ◽  
Black Pepper ◽  
Docking Studies ◽  
Therapeutic Activity ◽  
Computational Docking ◽  
Target Proteins ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Anti Oxidant ◽  
Inhibition Property

Piperine is known for its versatile therapeutic activity. It has been used for various disease conditions (e.g., cold, cough, etc.). Piperine is an alkaloid found in black pepper. It possesses various pharmacological actions like anti-inflammatory, anti-oxidant, anti-cholinergic, and anti-cancerous. The above-mentioned properties will be studied by selecting target proteins COX-2 protein, angiotensin converting enzyme, acetylcholineesterases, and survivin using computational docking study. This chapter explains the inhibition property of piperine against selected target protein from the results of docking studies. Based on the docking scores and protein-ligand interactions, piperine was found to bind well in the active site of the selected target proteins. It ensures the binding efficacy of piperine against selected target proteins. Docking scores and protein-ligand interactions plays an important role in its therapeutic activity.

scholarly journals Hidden Bias in the DUD-E Dataset Leads to Misleading Performance of Deep Learning in Structure-Based Virtual Screening

2019 ◽  
Author(s):  
Lieyang Chen ◽  
Anthony Cruz ◽  
Steven Ramsey ◽  
Callum J. Dickson ◽  
José S. Duca ◽  
...  
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Molecular Recognition ◽  
Autodock Vina ◽  
Methodology Development ◽  
X Ray ◽  
Learning Biases ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

<p>Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUD-E). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of protein-ligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development. </p>

scholarly journals Hidden Bias in the DUD-E Dataset Leads to Misleading Performance of Deep Learning in Structure-Based Virtual Screening

2019 ◽  
Author(s):  
Lieyang Chen ◽  
Anthony Cruz ◽  
Steven Ramsey ◽  
Callum J. Dickson ◽  
José S. Duca ◽  
...  
Keyword(s):  
Deep Learning ◽  
Virtual Screening ◽  
Molecular Recognition ◽  
Autodock Vina ◽  
Methodology Development ◽  
X Ray ◽  
Learning Biases ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

<p>Recently much effort has been invested in using convolutional neural network (CNN) models trained on 3D structural images of protein-ligand complexes to distinguish binding from non-binding ligands for virtual screening. However, the dearth of reliable protein-ligand x-ray structures and binding affinity data has required the use of constructed datasets for the training and evaluation of CNN molecular recognition models. Here, we outline various sources of bias in one such widely-used dataset, the Directory of Useful Decoys: Enhanced (DUD-E). We have constructed and performed tests to investigate whether CNN models developed using DUD-E are properly learning the underlying physics of molecular recognition, as intended, or are instead learning biases inherent in the dataset itself. We find that superior enrichment efficiency in CNN models can be attributed to the analogue and decoy bias hidden in the DUD-E dataset rather than successful generalization of the pattern of protein-ligand interactions. Comparing additional deep learning models trained on PDBbind datasets, we found that their enrichment performances using DUD-E are not superior to the performance of the docking program AutoDock Vina. Together, these results suggest that biases that could be present in constructed datasets should be thoroughly evaluated before applying them to machine learning based methodology development. </p>

scholarly journals Estimation of the protein–ligand interaction energy for model building and validation

2017 ◽  
Vol 73 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Daria A. Beshnova ◽  
Joana Pereira ◽  
Victor S. Lamzin
Keyword(s):  
Model Building ◽  
Data Bank ◽  
Binding Mode ◽  
Automated Identification ◽  
Ligand Interaction ◽  
New Approach ◽  
X Ray Crystallography ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Protein Ligand Interaction

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein–ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein–ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site withARP/wARP. The applicability of the method to the validation of protein–ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.

scholarly journals Structure-based screening of binding affinities via small-angle X-ray scattering

IUCrJ ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 644-655
Author(s):  
Po-chia Chen ◽  
Pawel Masiewicz ◽  
Kathryn Perez ◽  
Janosch Hennig
Keyword(s):  
Conformational Changes ◽  
Small Angle ◽  
Titration Calorimetry ◽  
X Ray ◽  
Protein Ligand Interactions ◽  
X Ray Scattering ◽  
Ligand Interactions ◽  
Titration Protocol ◽  
Screen Performance ◽  
Ray Scattering

Protein–protein and protein–ligand interactions often involve conformational changes or structural rearrangements that can be quantified by solution small-angle X-ray scattering (SAXS). These scattering intensity measurements reveal structural details of the bound complex, the number of species involved and, additionally, the strength of interactions if carried out as a titration. Although a core part of structural biology workflows, SAXS-based titrations are not commonly used in drug discovery contexts. This is because prior knowledge of expected sample requirements, throughput and prediction accuracy is needed to develop reliable ligand screens. This study presents the use of the histidine-binding protein (26 kDa) and other periplasmic binding proteins to benchmark ligand screen performance. Sample concentrations and exposure times were varied across multiple screening trials at four beamlines to investigate the accuracy and precision of affinity prediction. The volatility ratio between titrated scattering curves and a common apo reference is found to most reliably capture the extent of structural and population changes. This obviates the need to explicitly model scattering intensities of bound complexes, which can be strongly ligand-dependent. Where the dissociation constant is within 102 of the protein concentration and the total exposure times exceed 20 s, the titration protocol presented at 0.5 mg ml−1 yields affinities comparable to isothermal titration calorimetry measurements. Estimated throughput ranges between 20 and 100 ligand titrations per day at current synchrotron beamlines, with the limiting step imposed by sample handling and cleaning procedures.

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