AbLIFT: Antibody stability and affinity optimization by computational design of the variable light-heavy chain interface

Warszawski, Shira; Katz, Adriana; Khmelnitsky, Lev; Nissan, Gili Ben; Javitt, Gabriel; Dym, Orly; Unger, Tamar; Knop, Orli; Diskin, Ron; Albeck, Shira; Fass, Deborah; Sharon, Michal; Fleishman, Sarel J.

doi:10.2210/pdb6gc2/pdb

Cited by 8 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Biomolecular recognition of the glycan neoantigen CA19-9 by distinct antibodies

Borenstein-Katz

Warszawski

Amon

et al. 2021

Preprint

View full text Add to dashboard Cite

Glycans decorate cell surface, secreted glycoproteins and glycolipids. Altered glycans are often found in cancers. Despite their high diagnostic and therapeutic potentials, glycans are polar and flexible molecules that are quite challenging for the development and design of high-affinity binding antibodies. To understand the mechanisms by which glycan neoantigens are specifically recognized by antibodies, we analyze the biomolecular recognition of a single tumor-associated carbohydrate antigen CA19-9 by two distinct antibodies using X-ray crystallography. Despite the plasticity of glycans and the very different antigen-binding surfaces presented by the antibodies, both structures reveal an essentially identical extended CA19-9 conformer, suggesting that the stability of the conformer selects the antibodies. Starting from the bound structure of one of the antibodies, we use the AbLIFT computational method to design a variant with seven core mutations that exhibited tenfold improved affinity for CA19-9. The results reveal strategies used by antibodies to specifically recognize glycan antigens and show how automated antibody-optimization methods may be used to enhance the clinical potential of existing antibodies.

show abstract

Biomolecular recognition of the glycan neoantigen CA19-9 by distinct antibodies

Borenstein-Katz

Warszawski

Amon

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Antibody Library Design by Seeding Linear Programming with Inverse Folding and Protein Language Models

Hayes,

Magana-Zook,

Gonçalves

et al. 2024

Preprint

View full text Add to dashboard Cite

We propose a novel approach for antibody library design that combines deep learning and multi-objective linear programming with diversity constraints. Our method leverages recent advances in sequence and structure-based deep learning for protein engineering to predict the effects of mutations on antibody properties. These predictions are then used to seed a cascade of constrained integer linear programming problems, the solutions of which yield a diverse and high-performing antibody library. Operating in a cold-start setting, our approach creates designs without iterative feedback from wet laboratory experiments or computational simulations. We demonstrate the effectiveness of our method by designing antibody libraries for Trastuzumab in complex with the HER2 receptor, showing that it outperforms existing techniques in overall quality and diversity of the generated libraries.

show abstract

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

Kim,

McNaughton,

Kumar

2024

Bioengineering

View full text Add to dashboard Cite

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

show abstract

AbLIFT: Antibody stability and affinity optimization by computational design of the variable light-heavy chain interface

Cited by 8 publications

References 0 publications

Biomolecular recognition of the glycan neoantigen CA19-9 by distinct antibodies

Biomolecular recognition of the glycan neoantigen CA19-9 by distinct antibodies

Antibody Library Design by Seeding Linear Programming with Inverse Folding and Protein Language Models

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

Contact Info

Product

Resources

About