Protein Binding Site Identification With Sequence And Structure Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods are inadequate for accurately and efficiently identifying binding sites of protein molecules, such as those interacting with neutralizing antibodies or small molecules, which is crucial for developing effective vaccines and immunogenic compositions.
Innovation Solution
A computer-implemented method combining protein sequence data, structural information, experimental data on binding affinity, and computational modeling to identify ligand binding sites using multivariate and univariate analysis, machine learning algorithms, and machine learning algorithms like support vector machines and neural networks to predict potential binding residues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify binding sites, then the process is simpler, but the accuracy and precision of binding site identification is insufficient
Solution Approach 1:
The patent segments the binding site identification process into multiple independent computational modules: sequence analysis module, structural analysis module, binding affinity prediction module, and integration module. Each module processes specific features (amino acid sequences, 3D structures, binding energies) separately and outputs results that are integrated to identify binding sites, thereby improving accuracy while maintaining manageable complexity
Solution Approach 2:
The patent merges multiple computational methods and data sources (sequence homology analysis, structural modeling, binding affinity calculations, and statistical analysis) into a unified identification system. This integration of diverse analytical approaches enables more accurate binding site identification by combining complementary strengths of different methods
2Measurement precision
If comprehensive computational analysis is performed, then the identification precision improves, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of protein sequences and structures before main analysis. It pre-identifies conserved regions, predicts secondary structures, and filters potential binding sites based on structural features before applying computationally intensive binding affinity calculations, thereby reducing overall computational time while maintaining accuracy
Solution Approach 2:
The patent implements a tiered analysis approach where it first performs rapid screening using simplified models to identify candidate binding sites, then applies more comprehensive computational methods only to these candidates. This partial application of intensive analysis to selected regions achieves high precision while minimizing total computational time and resources
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Processes for identification of binding sites on protein molecules such as epitopes are provided. The disclosed methods in some embodiments use a combination of protein sequence data, structural information, experimental data on binding affinity, and computational modeling in order to identify binding sites on protein molecules. Systems and computer readable media for implementing the disclosed methods are provided. Also provided are compositions comprising a binding site or antibody that interacts with the binding site as an active ingredient and methods of using such compositions.