PDB File Conversion to Grayscale Arrays for Binding Affinity Prediction
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Solution Overview
Problem
Existing techniques fail to efficiently convert protein data bank (PDB) files into a format suitable for direct processing by artificial intelligence models, leading to inefficiencies in featurization and poor prediction of binding affinity.
Innovation Solution
A method and system that converts PDB files into a grayscale image array using modules for data extraction, file conversion, column selection, and image array generation, employing BioPandas, NumPy arrays, and min-max normalization to create a uniform format processable by AI models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing techniques (Onionnet, Prodigy) are used to generate features from PDB files, then some binding affinity prediction can be achieved, but the featurization efficiency is low and the prediction accuracy is poor due to using only contact-based features and simple linear regression models
Solution Approach 1:
The patent transforms PDB files into grayscale image arrays by converting atomic coordinates and structural parameters into pixel intensity values. This parameter transformation enables direct input to deep learning models, replacing the inefficient contact-based feature extraction with a comprehensive structural representation that captures spatial relationships, atomic types, and conformational information, thereby improving both featurization efficiency and prediction accuracy
Solution Approach 2:
The patent replaces the mechanical/algorithmic feature extraction processes (Onionnet shell-based technique, Prodigy contact counting) with a direct computational transformation to image arrays. This substitution eliminates the need for complex manual feature engineering and allows deep learning models to automatically learn relevant patterns from the structural data, significantly improving prediction performance
2Ease of operation
If PDB files are converted into grayscale image arrays using the proposed method, then the format becomes suitable for direct AI model processing, but the conversion process requires multiple modules and steps
Solution Approach 1:
The patent divides the complex conversion task into modular components: a data extraction module to retrieve PDB information, a conversion module to transform data into image array format, and a normalization module to scale values. This segmentation makes the system more manageable and easier to integrate with AI models, despite the multiple steps involved
Solution Approach 2:
The patent introduces grayscale image arrays as an intermediary representation between PDB files and AI models. This intermediate format simplifies the interface between structural biology data and machine learning algorithms, making the system easier to operate while the underlying complexity is encapsulated in the conversion modules
Data Source
AI summary
A system and a method for converting protein data bank (PDB) files into a grayscale image array is provided. The method includes extracting PDB files from a PDB, using a data extraction module. The method also includes converting, using a file conversion module, the PDB files into a data frame based on BioPandas. The method further includes selecting, using a column selection module, one or more columns from the data frame based on a pre-determined criteria. The method furthermore includes converting, using a column conversion module, the selected one or more columns into a NumPy array for rendering the data frame to resemble an image. The method furthermore includes resizing the NumPy array using an OpenCV for making size of the NumPy array uniform and for generating a grayscale image array.