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IsAb: a computational protocol for antibody design.

Tianjian Liang¹, Hui Chen¹, Jiayi Yuan¹

¹School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA.

Briefings in Bioinformatics

|April 20, 2021

Summary

This summary is machine-generated.

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This study introduces a computational protocol for designing therapeutic antibodies, streamlining the process. The in silico method aids in antibody structure generation, binding prediction, and affinity maturation for improved drug development.

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Therapeutic antibodies are crucial for disease treatment, offering high efficacy and safety.
Experimental antibody design is costly and time-consuming.
Computational antibody design faces challenges like antigen flexibility and data scarcity.

Purpose of the Study:

To present a user-friendly, in silico protocol for computer-aided antibody design.
To address limitations in current computational antibody design methods.
To provide a standardized protocol for antibody design and optimization.

Main Methods:

Utilized Rosetta web server for antibody 3D structure generation.
Employed a two-step docking approach (ClusPro and SnugDock) for antibody-antigen complex analysis.

Keywords:

antibody design computer-aided antibody protocol protein engineering protein–protein docking

Related Experiment Videos

Applied in silico alanine scanning for hotspot identification and computational affinity maturation for antibody optimization.

Main Results:

Successfully developed and validated the 'IsAb' protocol using antibody D44.1 redesign.
Demonstrated the protocol's utility with a step-by-step tutorial using the cemiplimab antibody (PD-1 inhibitor).
The protocol integrates structure prediction, docking, and affinity maturation for comprehensive antibody design.

Conclusions:

The developed in silico protocol offers an efficient and accessible approach to therapeutic antibody design.
This computational strategy can accelerate the discovery and optimization of antibodies for various diseases.
Future work will involve experimental validation of the computationally enhanced antibody designs.