scholarly journals Novel Method for Quantifying AhR-Ligand Binding Affinities Using Microscale Thermophoresis

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 60
Author(s):  
Anne Stinn ◽  
Jens Furkert ◽  
Stefan H. E. Kaufmann ◽  
Pedro Moura-Alves ◽  
Michael Kolbe
Keyword(s):  
Ligand Binding ◽  
Environmental Pollutants ◽  
Screening Strategy ◽  
Pharmacological Intervention ◽  
Binding Affinities ◽  
Free System ◽  
Microscale Thermophoresis ◽  
Aryl Hydrocarbon ◽  
Promising Target ◽  
A Cell

The aryl hydrocarbon receptor (AhR) is a highly conserved cellular sensor of a variety of environmental pollutants and dietary-, cell- and microbiota-derived metabolites with important roles in fundamental biological processes. Deregulation of the AhR pathway is implicated in several diseases, including autoimmune diseases and cancer, rendering AhR a promising target for drug development and host-directed therapy. The pharmacological intervention of AhR processes requires detailed information about the ligand binding properties to allow specific targeting of a particular signaling process without affecting the remaining. Here, we present a novel microscale thermophoresis-based approach to monitoring the binding of purified recombinant human AhR to its natural ligands in a cell-free system. This approach facilitates a precise identification and characterization of unknown AhR ligands and represents a screening strategy for the discovery of potential selective AhR modulators.

scholarly journals Novel method for quantifying AhR-ligand binding affinities using Microscale Thermophoresis

2021 ◽  
Author(s):  
Anne Stinn ◽  
Jens Furkert ◽  
Stefan H.E. Kaufmann ◽  
Pedro Moura-Alves ◽  
Michael Kolbe
Keyword(s):  
Ligand Binding ◽  
Environmental Pollutants ◽  
Screening Strategy ◽  
Pharmacological Intervention ◽  
Binding Affinities ◽  
Free System ◽  
Microscale Thermophoresis ◽  
Aryl Hydrocarbon ◽  
Promising Target ◽  
A Cell

AbstractThe aryl hydrocarbon receptor (AhR) is a highly conserved cellular sensor of a variety of environmental pollutants and dietary-, cell- and microbiota-derived metabolites with important roles in fundamental biological processes. Deregulation of the AhR pathway is implicated in several diseases, including autoimmune diseases and cancer, rendering AhR a promising target for drug development and host-directed therapy. The pharmacological intervention of AhR processes requires detailed information about the ligand binding properties to allow specific targeting of a particular signalling process without affecting the remaining. Here, we present a novel microscale thermophoresis-based approach to monitoring the binding of purified recombinant human AhR to its natural ligands in a cell-free system. This approach facilitates a precise identification and characterization of unknown AhR ligands and represents a screening strategy for the discovery of potential selective AhR modulators.

Combination of ribosome and phage display for fast selection of high affinity VHH antibody fragments

2019 ◽  
pp. 27-33
Author(s):  
YuE Kravchenko ◽  
SV Ivanov ◽  
DS Kravchenko ◽  
EI Frolova ◽  
SP Chumakov
Keyword(s):  
Phage Display ◽  
Selection Procedure ◽  
Free System ◽  
Phage Displays ◽  
High Affinity ◽  
Binding Domains ◽  
A Cell ◽  
Vhh Antibodies ◽  
Efficiency Of Selection ◽  
Selection Of

Selection of antibodies using phage display involves the preliminary cloning of the repertoire of sequences encoding antigen-binding domains into phagemid, which is considered the bottleneck of the method, limiting the resulting diversity of libraries and leading to the loss of poorly represented variants before the start of the selection procedure. Selection in cell-free conditions using a ribosomal display is devoid from this drawback, however is highly sensitive to PCR artifacts and the RNase contamination. The aim of the study was to test the efficiency of a combination of both methods, including pre-selection in a cell-free system to enrich the source library, followed by cloning and final selection using phage display. This approach may eliminate the shortcomings of each method and increase the efficiency of selection. For selection, alpaca VHH antibody sequences suitable for building an immune library were used due to the lack of VL domains. Analysis of immune libraries from the genes of the VH3, VHH3 and VH4 families showed that the VHH antibodies share in the VH3 and VH4 gene groups is insignificant, and selection from the combined library is less effective than from the VHH3 family of sequences. We found that the combination of ribosomal and phage displays leads to a higher enrichment of high-affinity fragments and avoids the loss of the original diversity during cloning. The combined method allowed us to obtain a greater number of different high-affinity sequences, and all the tested VHH fragments were able to specifically recognize the target, including the total protein extracts of cell cultures.

scholarly journals Effect of detergents on sterol synthesis in a cell-free system of yeast

1982 ◽  
Vol 23 (6) ◽  
pp. 803-810
Author(s):  
S Hata ◽  
T Nishino ◽  
N Ariga ◽  
H Katsuki
Keyword(s):  
Sterol Synthesis ◽  
Free System ◽  
Cell Free System ◽  
A Cell

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Development Project ◽  
Quantum Mechanical ◽  
Prediction Methods ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Quantum Mechanical Description ◽  
Optimization Stage

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

scholarly journals SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Surendra Kumar ◽  
Mi-hyun Kim
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Interaction ◽  
Fingerprint Pattern ◽  
Comparable Performance ◽  
Direct Interpretation ◽  
Benchmark Datasets ◽  
Complex Features

AbstractIn drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

scholarly journals Reconstitution of Endosomal Proteolysis in a Cell-free System

1989 ◽  
Vol 264 (10) ◽  
pp. 5392-5399
Author(s):  
L S Mayorga ◽  
R Diaz ◽  
P D Stahl
Keyword(s):  
Free System ◽  
Cell Free System ◽  
A Cell
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