Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Nat Comput Sci ; 2(12): 845-865, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-38177393

RESUMO

Machine learning (ML) is a key technology for accurate prediction of antibody-antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody-antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.


Assuntos
Anticorpos , Reações Antígeno-Anticorpo , Especificidade de Anticorpos , Epitopos/química , Aprendizado de Máquina
2.
Cell Rep ; 34(11): 108856, 2021 03 16.
Artigo em Inglês | MEDLINE | ID: mdl-33730590

RESUMO

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 104 motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.


Assuntos
Reações Antígeno-Anticorpo/imunologia , Sítios de Ligação de Anticorpos/imunologia , Epitopos/imunologia , Motivos de Aminoácidos , Sequência de Aminoácidos , Anticorpos/química , Anticorpos/imunologia , Regiões Determinantes de Complementaridade/química , Epitopos/química , Aprendizado de Máquina , Ligação Proteica
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...