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A complex water network contributes to high-affinity binding in an antibody-antigen interface.

ABSTRACT: This data article presents an analysis of structural water molecules in the high affinity interaction between a potent tumor growth inhibiting antibody (fragment), J22.9-xi, and the tumor marker antigen CD269 (B cell maturation antigen, BCMA). The 1.89 Å X-ray crystal structure shows exquisite details of the binding interface between the two molecules, which comprises relatively few, mostly hydrophobic, direct contacts but many indirect interactions over solvent waters. These are partly or wholly buried in, and therefore part of, the interface. A partial description of the structure is included in an article on the tumor inhibiting effects of the antibody: "Potent anti-tumor response by targeting B cell maturation antigen (BCMA) in a mouse model of multiple myeloma", Mol. Oncol. 9 (7) (2015) pp. 1348-58.

SUBMITTER: Marino SF

PROVIDER: S-EPMC4707293 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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A complex water network contributes to high-affinity binding in an antibody-antigen interface.

Marino S F SF Olal D D Daumke O O

Data in brief 20151219

This data article presents an analysis of structural water molecules in the high affinity interaction between a potent tumor growth inhibiting antibody (fragment), J22.9-xi, and the tumor marker antigen CD269 (B cell maturation antigen, BCMA). The 1.89 Å X-ray crystal structure shows exquisite details of the binding interface between the two molecules, which comprises relatively few, mostly hydrophobic, direct contacts but many indirect interactions over solvent waters. These are partly or wholl ...[more]

PMID: 26862587

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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

A complex water network contributes to high-affinity binding in an antibody-antigen interface.

Publications

A complex water network contributes to high-affinity binding in an antibody-antigen interface.

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