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Related Concept Videos

Antibody Structure01:10

Antibody Structure

51.8K
Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
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Scalable High Throughput Selection From Phage-displayed Synthetic Antibody Libraries
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AI-based IsAb2.0 for antibody design.

Tianjian Liang1,2,3,4,5, Ze-Yu Sun1,2,3,4,5, Margaret G Hines6

  • 1Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, Pharmacometrics & System Pharmacology PharmacoAnalytics, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, Pittsburgh, PA 15261, United States.

Briefings in Bioinformatics
|September 16, 2024
PubMed
Summary
This summary is machine-generated.

We developed IsAb2.0, an AI-powered computational tool for designing therapeutic antibodies, including nanobodies and humanized antibodies. This improved protocol enhances binding affinity and accelerates immunotherapy development.

Keywords:
AlphaFold-MultimerHIV-1 nanobodyIsAbantibody designdeep learninghumanized antibody

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Area of Science:

  • Biotechnology and Bioinformatics
  • Immunology
  • Computational Biology

Background:

  • Therapeutic antibody design is crucial but faces challenges with costly experimental methods.
  • Existing computational tools like IsAb1.0 have limitations in accuracy, complexity, and scope, particularly for nanobodies and humanized antibodies.
  • Insufficient structural data and lack of standardized protocols hinder efficient antibody engineering.

Purpose of the Study:

  • To develop an advanced computational protocol, IsAb2.0, for accurate and efficient in silico antibody design.
  • To overcome the limitations of previous methods by integrating artificial intelligence and advanced modeling techniques.
  • To specifically enable the design of nanobodies and humanized antibodies for therapeutic applications.

Main Methods:

  • IsAb2.0 integrates artificial intelligence with AlphaFold-Multimer (versions 2.3/3.0) for template-free modeling and complex construction.
  • The FlexddG method is employed for precise in silico antibody optimization.
  • The protocol was validated using a humanized nanobody (HuJ3) targeting HIV-1 gp120.

Main Results:

  • IsAb2.0 accurately predicted five mutations to enhance the binding affinity of the humanized nanobody HuJ3 to HIV-1 gp120.
  • These predictions were corroborated by commercial software and experimentally validated through binding and neutralization assays.
  • The study demonstrated IsAb2.0's capability to streamline antibody design and improve binding affinity.

Conclusions:

  • IsAb2.0 represents a significant advancement in computational antibody design, offering improved accuracy and efficiency.
  • The AI-driven protocol facilitates the development of therapeutic antibodies, including nanobodies and humanized antibodies.
  • IsAb2.0 provides a foundation for accelerating future immunotherapy development through streamlined design processes.