Dataset Information

Direct detection of antibody-antigen binding using an on-chip artificial pore.

ABSTRACT: We demonstrate a rapid and highly sensitive all-electronic technique based on the resistive pulse method of particle sizing with a pore to detect the binding of unlabeled antibodies to the surface of latex colloids. Here, we use an on-chip pore to sense colloids derivatized with streptavidin and measure accurately their diameter increase on specific binding to several different types of antibodies. We show the sensitivity of this technique to the concentration of free antibody and that it can be used to perform immunoassays in both inhibition and sandwich configurations. Overall, our technique does not require labeling of the reactants and is performed rapidly by using very little solution, and the pore itself is fabricated quickly and inexpensively by using soft lithography. Finally, because this method relies only on the volume of bound ligand, it can be generally applied to detecting a wide range of ligand-receptor binding reactions.

SUBMITTER: Saleh OA

PROVIDER: S-EPMC298685 | biostudies-literature | 2003 Feb

REPOSITORIES: biostudies-literature

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Publications

Direct detection of antibody-antigen binding using an on-chip artificial pore.

Saleh Omar A OA Sohn Lydia L LL

Proceedings of the National Academy of Sciences of the United States of America 20030127 3

We demonstrate a rapid and highly sensitive all-electronic technique based on the resistive pulse method of particle sizing with a pore to detect the binding of unlabeled antibodies to the surface of latex colloids. Here, we use an on-chip pore to sense colloids derivatized with streptavidin and measure accurately their diameter increase on specific binding to several different types of antibodies. We show the sensitivity of this technique to the concentration of free antibody and that it can be ...[more]

PMID: 12552089

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Project description:High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.

Dataset Information

Direct detection of antibody-antigen binding using an on-chip artificial pore.

Publications

Direct detection of antibody-antigen binding using an on-chip artificial pore.

Similar Datasets

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