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Interdiscip Sci ; 11(4): 691-697, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31119495

RESUMO

Monoclonal antibodies (mAbs) are one of the robust classes of therapeutic proteins. Their stability, specificity, and high solubility allow the successful development and commercialization of antibody-based drugs. Though with these characteristics, mAbs projects are often suspended due to self- or cross-interaction of monoclonal antibodies. This is one of the main reasons which causes the development of mAbs into drugs taking forever and expensive. CISI is short for cross-interaction or self-interaction of mAbs. It can be quantified by several assays. The assays such as poly-specificity reagent and cross-interaction chromatography can measure cross-interaction of mAbs. Self-interaction can be assayed through clone self-interaction by biolayer interferometry and affinity-capture self-interaction nanoparticle spectroscopy. To save time and money, we developed a model called CISI which can predict cross-interaction or self-interaction based on tripeptide composition. It showed 88.20% accuracy, 90.22% sensitivity, 86.05% specificity, 0.78 Mathew correlation coefficient, and 0.96 area under the receiver operating characteristic (ROC) curve (AUC) in the leave-one-out cross-validation. CISI is freely available at http://i.uestc.edu.cn/eli/cgi-bin/cisi.pl.


Assuntos
Anticorpos Monoclonais/química , Biologia Computacional/métodos , Algoritmos , Área Sob a Curva , Cromatografia , Mineração de Dados , Bases de Dados Factuais , Reações Falso-Positivas , Humanos , Interferometria , Modelos Estatísticos , Nanopartículas/química , Curva ROC , Reprodutibilidade dos Testes , Software , Solubilidade , Espectrofotometria , Máquina de Vetores de Suporte
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