Structural Modeling of Nanobodies: A Benchmark of State-of-the-Art Artificial Intelligence Programs

Mario S. Valdés-Tresanco, Mario E. Valdés-Tresanco, Daiver E. Jiménez-Gutiérrez, Ernesto Moreno

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

4 Citas (Scopus)

Resumen

The number of applications for nanobodies is steadily expanding, positioning these molecules as fast-growing biologic products in the biotechnology market. Several of their applications require protein engineering, which in turn would greatly benefit from having a reliable structural model of the nanobody of interest. However, as with antibodies, the structural modeling of nanobodies is still a challenge. With the rise of artificial intelligence (AI), several methods have been developed in recent years that attempt to solve the problem of protein modeling. In this study, we have compared the performance in nanobody modeling of several state-of-the-art AI-based programs, either designed for general protein modeling, such as AlphaFold2, OmegaFold, ESMFold, and Yang-Server, or specifically designed for antibody modeling, such as IgFold, and Nanonet. While all these programs performed rather well in constructing the nanobody framework and CDRs 1 and 2, modeling CDR3 still represents a big challenge. Interestingly, tailoring an AI method for antibody modeling does not necessarily translate into better results for nanobodies.

Idioma originalInglés
Número de artículo3991
PublicaciónMolecules
Volumen28
N.º10
DOI
EstadoPublicada - may. 2023

Tipos de productos de Minciencias

  • Artículo A1 - Q1

Huella

Profundice en los temas de investigación de 'Structural Modeling of Nanobodies: A Benchmark of State-of-the-Art Artificial Intelligence Programs'. En conjunto forman una huella única.

Citar esto