Shift-Invariant Double Threading Model for MHC Binding Prediction
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Solution Overview
Problem
Predicting 3-D protein structure and protein-ligand binding, particularly for Major Histocompatibility Complex (MHC) molecules, remains challenging due to the complexity of protein-ligand interactions and the variability in peptide lengths and sequences, which complicates the training of predictors.
Innovation Solution
The Shift Invariant Double Threading (SIDT) model treats the relative position of peptides within the MHC groove as a hidden variable, allowing for the ensemble modeling of binding configurations and incorporating trainable parameters to refine predictions, enabling accurate and efficient prediction of binding energies and probabilities across various MHC class II alleles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the threading model is used to predict MHC-peptide binding, then the prediction process becomes computationally simpler by using known binding configurations, but the accuracy deteriorates due to the simplification assumptions about energy additivity and proximity patterns
Solution Approach 1:
The patent transforms the binding prediction problem from direct energy calculation to probability estimation using logistic regression. By changing the output parameter from binding energy (continuous physical quantity) to binding probability (statistical measure), the model achieves better accuracy while maintaining computational efficiency through the use of trained parameters instead of complex physics-based calculations
Solution Approach 2:
The patent replaces the physics-based threading model (which relies on mechanical assumptions about energy additivity and fixed proximity patterns) with a statistical learning approach. By substituting the deterministic physics model with a probabilistic model trained on experimental data, the system achieves higher accuracy without the restrictive assumptions of the original threading approach
2Device complexity
If the proximity pattern of the peptide in the MHC groove is assumed to be invariant, then the energy calculation becomes a simple sum of pairwise potentials, but the prediction accuracy deteriorates when the peptide's amino acid content significantly influences the binding configuration
Solution Approach 1:
The patent performs preliminary training of the logistic regression model using experimental binding data before making predictions. By pre-learning the relationships between peptide-MHC features and binding outcomes from real data, the model captures context-dependent effects without requiring complex real-time calculations, thus maintaining both simplicity and reliability
Solution Approach 2:
The patent introduces trained parameters (weights and biases) as intermediaries between the input features and the binding probability output. These intermediate parameters encapsulate the complex relationships between amino acid sequences and binding configurations, allowing the model to account for contextual effects without explicitly modeling the complex physical interactions
Data Source
AI summary
Shift invariant predictors are described herein. By way of example, a system for predicting binding information relating to a binding of a protein and a ligand can include a trained binding model and a prediction component. The trained binding model can include a hidden variable representing an unknown alignment of the ligand at a binding site of the protein. The prediction component can be configured to predict the binding information by employing information about the protein's sequence, the ligand's sequence and the trained binding model.