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Antibody Structure and Classes

Antibody Structure and Classes

Antibodies, also known as immunoglobulins, are produced by B cells in response to foreign substances, such as bacteria and viruses. These proteins are critical for recognizing and neutralizing these substances, protecting the body from potential harm.
The basic structure of an antibody consists of four protein chains: two identical heavy chains and two identical light chains. These chains are held together by disulfide bonds and other non-covalent interactions, forming a Y-shaped structure.

Antibody Structure

Antibody Structure

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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Updated: Jun 24, 2025

Characterization of Glycoproteins with the Immunoglobulin Fold by X-Ray Crystallography and Biophysical Techniques

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Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and

Eliott Park¹, Saeed Izadi¹

¹Pharmaceutical Development, Genentech Inc, South San Francisco, CA, USA.

|June 10, 2024

Summary

This summary is machine-generated.

This study introduces novel molecular surface descriptors for predicting antibody developability in silico. These descriptors improve consistency across structure models, aiding early-stage antibody drug discovery.

Keywords:

Aggregation antibodies developability electrostatics hydrophobicity in silico prediction molecular dynamics pharmacokinetics polyspecificity surface descriptors viscosity

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Area of Science:

Biochemistry and Biophysics
Computational Chemistry
Drug Discovery

Background:

* In silico assessment of antibody developability is crucial for efficient lead candidate selection.
* Predictive accuracy relies on molecular descriptors, model parameters, structure accuracy, and conformational sampling.
* Current methods face challenges in reproducibility and predictive power.

Purpose of the Study:

* To develop and validate novel molecular surface descriptors for predicting antibody developability.
* To assess the impact of methodological choices on descriptor performance.
* To propose and evaluate in silico developability risk flags.

Main Methods:

* Design of specific molecular surface descriptors for antibody developability.
* Benchmarking descriptor correlations with experimental biophysical properties (viscosity, aggregation, etc.).
* Investigation of sensitivity to methodological variations (dielectric constant, hydrophobicity scales, structure prediction, conformational sampling).

Main Results:

* Systematic shifts in surface descriptors observed based on structure prediction methods.
* Averaging over molecular dynamics simulations mitigates shifts and improves cross-method consistency.
* Inconsistent improvements in correlations with biophysical data despite enhanced consistency.

Conclusions:

* Proposed six in silico developability risk flags based on benchmarking.
* Assessed the effectiveness of these flags in predicting developability issues.
* Highlighted the importance of conformational sampling and methodological choices for reliable in silico developability assessment.