Modelling the assembly and flexibility of antibody structures

Guo, Dongjun; De Sciscio, Maria Laura; Chi-Fung Ng, Joseph; Fraternali, Franca

doi:10.1016/j.sbi.2023.102757

Cited by 6 publications

(1 citation statement)

References 67 publications

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“…In general, the error level of these methods can be considered to be in the same order as the X-ray resolution, which has a mean value of 2 Å in the PDB ( https://www.rcsb.org/stats/distribution-resolution ). This can be regarded as an interesting result [ 119 ].…”

Section: For Protein and Antibody Designmentioning

confidence: 99%

Antibody design using deep learning: from sequence and structure design to affinity maturation

Joubbi,

Micheli,

Milazzo

et al. 2024

Briefings in Bioinformatics

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Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody–antigen interactions docking and affinity maturation.

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Section: For Protein and Antibody Designmentioning

confidence: 99%

Antibody design using deep learning: from sequence and structure design to affinity maturation

Joubbi,

Micheli,

Milazzo

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

show abstract

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

El Salamouni,

Cater,

Spenkelink

et al. 2024

FEBS Open Bio

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Nanobodies, the smallest functional antibody fragment derived from camelid heavy‐chain‐only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen‐binding domains, highlighting the critical role of complementarity‐determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen‐binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody‐based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

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Electrochemical cytosensors for non-invasive liquid biopsy: Detection procedures and technologies for circulating tumor cells

Wu,

He,

Fan

et al. 2025

Biosensors and Bioelectronics

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Modelling the assembly and flexibility of antibody structures

Cited by 6 publications

References 67 publications

Antibody design using deep learning: from sequence and structure design to affinity maturation

Antibody design using deep learning: from sequence and structure design to affinity maturation

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Electrochemical cytosensors for non-invasive liquid biopsy: Detection procedures and technologies for circulating tumor cells

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