Attentive Cross-Modal Paratope Prediction

Deac, Andreea; Veličković, Petar; Sormanni, Pietro

doi:10.1089/cmb.2018.0175

Cited by 59 publications

(50 citation statements)

References 10 publications

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“…Recently, computational and machine learning approaches for the sequence-based prediction of paratopes (Deac et al, 2018;Kunik et al, 2012b;Liberis et al, 2018), epitopes (Kringelum et al, 2012) or paratope-epitope (antibody-antigen) interaction (Baran et al, 2017;Deac et al, 2018;Jespersen et al, 2019;Kilambi and Gray, 2017;Krawczyk et al, 2013) are accumulating (for a more complete list of references see here: (Brown et al, 2019;EL-Manzalawy et al, 2017;Esmaielbeiki et al, 2016;Raybould et al, 2019a)). While the accuracy for the sequence-based prediction of paratopes seems generally higher than that for epitopes, overall, prediction accuracy has remained suboptimal -especially with regard to sequence-based prediction of epitope sites and paratope-epitope interaction (Brown et al, 2019;Greenbaum et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Akbar

Jeliazkov

Robert

et al. 2019

Preprint

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Antibody recognition of antigen relies on the specific interaction of amino acids at the paratopeepitope interface. A long-standing question in the fields of immunology and structural biology is whether paratope-epitope interaction is predictable. A fundamental premise for the predictability of paratope-epitope binding is the existence of structural units that are universally shared among antibody-antigen binding complexes. Here, we identified structural interaction motifs, which together compose a vocabulary of paratope-epitope binding that is shared among investigated antibody-antigen complexes. The vocabulary (i) is finite with less than 10 4 motifs, (ii) mediates specific and non-redundant interactions between paratope-epitope pairs, (iii) is immunity-specific (distinct from the motif vocabulary used by non-immune protein-protein interactions), and (iv) enables the machine learning prediction of paratope or epitope. The discovery of a vocabulary of paratope-epitope interaction demonstrates the learnability and predictability of paratope-epitope interaction.

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Section: Introductionmentioning

confidence: 99%

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Akbar

Jeliazkov

Robert

et al. 2019

Preprint

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“…Its predictions improve the speed and accuracy of a rigid docking algorithm. The AG-Fast-Parapred [23] is an outperform of Parapred, which for the first time, provides antigen information in an in-depth paratope predictor.…”

Section: Paratope Predictionmentioning

confidence: 99%

In silico Techniques for Prospecting and Characterizing Monoclonal Antibodies

Manieri¹,

Magalhães²,

Takata³

et al. 2021

Monoclonal Antibodies

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In the past few years, improvement in computational approaches provided faster and less expensive outcomes on the identification, development, and optimization of monoclonal antibodies (mAbs). In silico methods, such as homology modeling, to predict antibody structures, identification of epitope-paratope interactions, and molecular docking are useful to generate 3D structures of the antibody–antigen complexes. It helps identify the key residues involved in the antigen–antibody complex and enable modifications to enhance the antibody binding affinity. Recent advances in computational tools for redesigning antibodies are significant resources to improve antibody biophysical properties, such as binding affinity, solubility, stability, decreasing the timeframe and costs during antibody engineering. The immunobiological market grows continuously with new molecules, both natural and new molecular formats, such as bispecific antibodies, Fc-antibody fusion proteins, and mAb fragments, requiring novel methods for designing, screening, and analyzing. Algorithms and software set the in silico techniques on the innovation frontier.

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“…The antibody i-Patch [89] algorithm introduces a likelihood score for residue contact as a constraint on local docking to generate predicted paratope residues, and thus requires the structure of the antigen-antibody complex. AG-Fast-Parapred [88] , which is based on deep neural networks, utilizes antigen sequence information to predict paratope.…”

Section: Epitope Specificitymentioning

confidence: 99%