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Size-Exclusion Chromatography

Size-Exclusion Chromatography

In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
Silica particles offer advantages such as rigidity,...

Affinity Chromatography

Affinity Chromatography

Affinity chromatography is a powerful technique extensively utilized for separating and purifying specific biomolecules from complex mixtures. It capitalizes on the highly selective binding between an analyte and its counterpart, such as antibody-antigen interactions. The counterpart is immobilized on the stationary phase, forming an affinity column. The stationary phase typically consists of solid support, such as agarose or porous glass beads, immobilizing the affinity ligand. The mobile...

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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Related Experiment Video

Updated: Jan 6, 2026

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

Published on: June 20, 2019

Accelerating antibody development: sequence and structure-based models for predicting developability properties via

A N M Nafiz Abeer^1,2, Mehdi Boroumand¹, Isabelle Sermadiras³

¹Data Science and Modelling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA.

|September 26, 2025

Summary

This summary is machine-generated.

This study shows in silico models can speed up biopharmaceutical developability screening. Machine learning approaches, including protein language models and graph neural networks, effectively predict antibody aggregation for size exclusion chromatography assays.

Keywords:

Antibody structure developability properties graph neural network protein language model size exclusion chromatography therapeutic antibodies

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Last Updated: Jan 6, 2026

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Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Published on: August 16, 2019

Area of Science:

Biopharmaceutical development
Computational biology
Protein engineering

Background:

Experimental screening for biopharmaceutical developability, such as size exclusion chromatography (SEC), is resource-intensive and time-consuming.
Accelerating the antibody development process requires efficient screening methods for critical developability properties.

Purpose of the Study:

To explore and compare in silico models for accelerating the screening of biopharmaceutical developability properties.
To identify the most effective computational approach for predicting protein aggregation propensity in antibodies for SEC assays.

Main Methods:

Comparison of surrogate models using pre-computed sequence and predicted structure features.
Evaluation of sequence-based approaches utilizing protein language models (PLMs) like ESM-2 with various fine-tuning strategies.
Integration of antibody structural information via graph neural networks (GNN) into prediction pipelines.
Application of these diverse in silico methods to predict aggregation propensity for a dataset of approximately 1200 Immunoglobulin G (IgG1) molecules.

Main Results:

Empirical evaluation identified the most effective in silico approach for predicting developability properties relevant to SEC assays.
Demonstrated the potential of integrating structural information through GNNs alongside PLMs.
Quantified the performance differences between feature-based and end-to-end PLM approaches.

Conclusions:

In silico models, particularly those leveraging sequence and structural data, can significantly accelerate antibody developability screening.
The study provides valuable insights into selecting optimal computational strategies for predicting protein aggregation, aiding in faster antibody development.
This research contributes to optimizing early-stage biopharmaceutical screening processes, reducing experimental burdens.