Deep Learning Antibody Library Construction Method
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Solution Overview
Problem
The traditional antibody library construction process is cumbersome, involving repeated adsorption, elution, and amplification steps, which are inefficient and time-consuming.
Innovation Solution
An antibody library construction method based on deep learning, utilizing a trained neural network model to predict antibody gene sequences and a temporal convolutional neural network for screening, combined with molecular docking and dynamics to establish a secondary antibody library with desired activities, stability, and specificity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional antibody library construction methods are used, then comprehensive antibody screening can be achieved, but the process becomes cumbersome and time-consuming due to repeated adsorption, elution, and amplification steps
Solution Approach 1:
The patent applies preliminary action by using a trained neural network model to predict antibody gene sequences before actual library construction. The model pre-screens potential antibody candidates by processing antigen epitope data and generating predicted coding gene sequences, allowing the most promising candidates to be selected in advance. This preliminary computational screening eliminates the need for repeated experimental adsorption, elution, and amplification cycles, significantly reducing construction time while maintaining comprehensive screening capability.
2Reliability
If traditional experimental screening methods are used, then antibody activity can be verified, but the process complexity increases due to multiple repeated steps
Solution Approach 1:
The patent replaces the mechanical and experimental screening system with a computational deep learning system. Instead of physically performing repeated adsorption, elution, and amplification steps in the lab, the invention uses a neural network model that processes antigen epitope data and predicts antibody gene sequences computationally. This substitution of mechanical experimental procedures with computational algorithms maintains the ability to verify antibody activity through molecular docking and dynamics simulations while dramatically reducing process complexity.
3Productivity
If deep learning prediction is used to reduce screening steps, then construction efficiency improves, but model accuracy and generalization ability must be maintained
Solution Approach 1:
The model undergoes extensive preliminary training action before deployment. The neural network is trained on a large dataset containing antigen epitopes, antigen recognition regions, and corresponding coding genes. The training process includes multiple epochs with optimization of hyperparameters, learning rates, and model architecture to ensure high prediction accuracy. This thorough preliminary training enables the model to generalize well to new antigens while maintaining high efficiency in predicting antibody gene sequences, thus resolving the contradiction between productivity and measurement precision.
Solution Approach 2:
The patent implements feedback mechanisms to continuously improve model accuracy. The training process uses loss functions to measure prediction errors and adjusts model parameters accordingly. Additionally, the system incorporates feedback from molecular docking and dynamics simulation results to refine predictions. This feedback loop ensures that the model maintains high prediction accuracy while operating efficiently, preventing the trade-off between speed and precision.
Data Source
AI summary
An antibody library construction method based on deep learning, comprising the steps of: obtaining a corresponding relation among antigen epitopes, antigen recognition regions and coding genes, and constructing a first database matching with the antigen epitopes, the antigen recognition regions and the coding genes; processing the antigen epitopes; carrying out clustering and characteristic extraction on the first database; and taking the multi-dimensional vector as the input of a temporal convolutional neural network, and stopping training until the error is lower than the threshold and tends to be stable to obtain the trained neural network model; and screening out antibody sequences having different activities, stability and specificity to the antigens in the coding gene sequence set X according to molecular docking, molecular dynamics and an existing gene sequence database Y so as to establish a secondary antibody library.