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Minoux, H.

Publications and source records attributed to Minoux, H..

2 recordsLinked to original sources

NAStructuralDB : Structural database to facilitate computational studies of molecular modeling and recognition of proteins with special focus on antibody-antigen interactions.

Studying the interactions between antibodies and antigens is fundamental to the development of novel therapeutic biologics. Predictions of such interactions start with data collection. Though there exist reliable resources to identify antibody structures in the Protein Data Bank (PDB), such data still requires substantial processing to be usable in predictive tasks. Redundancy in sequences needs to be removed to avoid data leakages between train, test and validation sets. Descriptors such as surface accessibility, secondary structure and antibody region information need to be additionally annotated. Information on inter- and intra-molecular contacts, which is crucial to studying paratope/epitope information, needs to be collected. The specialized immunoglobulin format of Nanobodies(R) requires a separate dataset mirroring that of antibodies, given that their structure contains only a single VHH chain. Because antibody-antigen structures account for a small amount of all protein-protein contacts, having a molecular contact reference from other proteins is also desired. To address these issues, we introduce NAStructuralDB (https://naturalantibody.com/na-structural/), a dataset of processed structures of antibodies, Nanobodies(R), proteins and their complexes with molecular contact information and associated annotations. We use the opportunity of having collected the contact data to provide a reference of binding propensities of different residues across distinct contact types. We anticipate that this dataset will accelerate a broad range of predictive tasks by standardizing common, time-consuming data preparation steps in antibody and protein design.

bioinformatics↗

Finding Antibodies in Cryo-EM densities with CrAI

Therapeutic antibodies have emerged as a prominent class of new drugs due to their high specificity and their ability to bind to several protein targets. Once an initial antibody has been identified, an optimization of this hit compound follows based on the 3D structure, when available. Cryo-EM is currently the most efficient method to obtain such structures, supported by well-established methods that can transform raw data into a potentially noisy 3D map. These maps need to be further interpreted by inferring the number, position and structure of antibodies and other proteins that might be present. Unfortunately, existing automated methods addressing this last step have a limited accuracy and usually require additional inputs, high resolution maps, and exhibit long running times. We propose the first fully automatic and efficient method dedicated to finding antibodies in cryo-EM densities: CrAI. This machine learning approach leverages the conserved structure of antibodies and exploits a dedicated novel database that we built to solve this problem. Running a prediction takes only a few seconds, instead of hours, and requires nothing but the cryo-EM density, seamlessly integrating in automated analysis pipelines. Our method is able to find the location of both Fabs and VHHs, at resolutions up to 10[A] and is significantly more reliable than existing methods. It also provides an accurate estimation of the antibodies pose, even in challenging examples such as Fab binding to VHHs and vice-versa. We make our method available as a ChimeraX[44] bundle. 1

bioinformatics↗