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Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

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Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Yuhan Wang¹, Hywel D Williams², Duygu Dikicioglu¹

¹Department of Biochemical Engineering, University College London, London WC1E 6BT, U.K.

Molecular Pharmaceutics

|September 30, 2024

Summary

Computational methods predict protein aggregation kinetics by analyzing sequence and molecular dynamics. This approach streamlines therapeutic protein development by considering formulation conditions alongside protein properties.

Keywords:

antibody aggregation feature engineering formulation molecular dynamics monoclonal antibody protein stability regression analysis

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Area of Science:

Biochemistry and Biophysics
Computational Biology
Pharmaceutical Sciences

Background:

Computational methods like machine learning and molecular dynamics simulations offer potential for predicting protein properties.
Current methods often focus on sequence variations, neglecting formulation impacts on protein stability and aggregation.
Understanding protein aggregation is crucial for developing stable therapeutic protein drugs.

Purpose of the Study:

To develop predictive models for protein aggregation kinetics considering both molecular features and formulation conditions.
To gain deeper insights into the mechanisms governing protein stability and aggregation.
To validate existing findings and enhance the prediction accuracy for therapeutic protein formulations.

Main Methods:

Utilized molecular dynamics simulations and sequence-level molecular features for the Fab A33 protein.
Employed advanced statistical tools for data analysis and model development.
Investigated protein dynamics across 49 different solution conditions.

Main Results:

Identified key molecular features influencing protein stability and aggregation propensity.
Developed predictive models capable of forecasting aggregation kinetics under varied formulation conditions.
Validated previous findings on protein stability mechanisms.

Conclusions:

A combined approach of molecular features and simulation data enhances understanding of protein aggregation.
Predictive models incorporating formulation effects are vital for therapeutic protein development.
This work provides a framework for optimizing protein formulations to improve drug stability.