Dataset Information

Modelling ligand depletion for simultaneous affinity and binding site quantification on cells and tissue.

ABSTRACT: The quantification of the number of targets in biological systems is an important parameter to assess the suitability of surface markers as targets for drugs, drug delivery and medical imaging. Likewise, quantifying the interaction with the target in terms of affinity and binding kinetics is essential during drug development. Commonly used approaches to quantify membrane antigens on live cells are based on manual saturation techniques that are labour-intensive, require careful calibration of the generated signal and do not quantify the binding rates. Here, we present how measuring interactions in real-time on live cells and tissue under ligand depletion conditions can be used to simultaneously quantify the kinetic binding parameters as well as the number of available binding sites in a biological system. Suitable assay design was explored with simulated data and feasibility of the method verified with experimental data for exemplary low molecular weight peptide and antibody radiotracers as well as fluorescent antibodies. In addition to revealing the number of accessible target sites and improving the accuracy of binding kinetics and affinities, the presented method does not require knowledge about the absolute signal generated per ligand molecule. This enables a simplified workflow for use with both radioligands and fluorescent binders.

SUBMITTER: Weber J

PROVIDER: S-EPMC10282064 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Modelling ligand depletion for simultaneous affinity and binding site quantification on cells and tissue.

Weber Judith J Djurberg Klara K Lundsten Salomonsson Sara S Kamprath Maria M Hoehne Aileen A Westin Hadis H Vergara Fernanda F Bondza Sina S

Scientific reports 20230620 1

The quantification of the number of targets in biological systems is an important parameter to assess the suitability of surface markers as targets for drugs, drug delivery and medical imaging. Likewise, quantifying the interaction with the target in terms of affinity and binding kinetics is essential during drug development. Commonly used approaches to quantify membrane antigens on live cells are based on manual saturation techniques that are labour-intensive, require careful calibration of the ...[more]

PMID: 37340068

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Project description:Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Dataset Information

Modelling ligand depletion for simultaneous affinity and binding site quantification on cells and tissue.

Publications

Modelling ligand depletion for simultaneous affinity and binding site quantification on cells and tissue.

Similar Datasets

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