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Hybridoma technology is used for the large-scale production of monoclonal antibodies. Monoclonal antibodies bind to only a single antigenic determinant or epitope. Such antibodies are used in research, diagnostics, and disease therapy. The hybridoma technology established in 1975 by Georges Köhler and Cesar Milstein was awarded the Nobel Prize in Medicine in 1984 for revolutionizing research and therapy.
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Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Analyzing Tumor and Tissue Distribution of Target Antigen Specific Therapeutic Antibody

Published on: May 16, 2020

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.

Lateefat Kalejaye¹, I-En Wu¹, Taylor Terry¹

¹Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken 07030, NJ, United States.

Computational and Structural Biotechnology Journal

|June 3, 2024

Summary

This summary is machine-generated.

Deep Spatial Properties (DeepSP) is a new deep learning model that predicts antibody spatial properties from sequences, reducing computational time. This accelerates therapeutic antibody development by predicting viscosity and aggregation without complex simulations.

Keywords:

Antibody stability Deep learning Molecular dynamics simulation Monoclonal antibody Spatial aggregation propensity Spatial charge map

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Area of Science:

Biotechnology and Pharmaceutical Sciences
Computational Biology and Cheminformatics

Background:

Therapeutic antibody development is hindered by high viscosity and aggregation issues.
Predictive computational methods like spatial charge map (SCM) and spatial aggregation propensity (SAP) rely on computationally intensive molecular dynamics (MD) simulations.
Previous deep learning models like DeepSCM have shown promise in predicting SCM from sequence data.

Purpose of the Study:

To develop a deep learning surrogate model, DeepSP, that predicts SCM and SAP directly from antibody sequences.
To assess the utility of DeepSP-derived features for predicting antibody aggregation rates.
To significantly reduce the computational burden associated with predicting antibody structural properties.

Main Methods:

Trained a convolutional neural network deep learning surrogate model, DeepSP, on a dataset of 20,530 antibody sequences.
DeepSP predicts SCM and SAP scores for antibody variable regions solely based on amino acid sequences.
Utilized DeepSP-derived descriptors as features in machine learning models to predict antibody aggregation rates.

Main Results:

DeepSP achieved high linear correlation coefficients (0.76-0.96, average 0.87) between predicted and MD-derived scores for 30 properties.
Machine learning models using DeepSP features showed performance comparable to MD-based methods in predicting antibody aggregation rates.
The DeepSP approach drastically reduces computational time compared to traditional MD simulations.

Conclusions:

DeepSP offers a rapid and accurate method for generating antibody structural properties from sequence data alone.
This accelerates the screening and development of therapeutic antibodies by bypassing lengthy MD simulations.
DeepSP provides valuable sequence-based features for machine learning models predicting antibody stability and other properties.