End-to-end protein 3d density map deep reconstruction method based on particle image

Abstract

This invention relates to the fields of computer and biological technologies, specifically to an end-to-end protein 3D density map depth reconstruction method based on particle images. The method involves acquiring particle images, extracting spatial and structural features through convolution, classifying and filtering them, analyzing the rate of change of rotation quaternions and translation vectors to determine stability and correct posture, and further correcting posture by incorporating the direction of the distance between adjacent particles to reduce errors. The images are then mapped to a 3D voxel grid for reverse mapping to generate a spatial distribution density map. Based on the mapping rules, the 3D density map is decoded, reconstructed, and updated to obtain the protein 3D density map reconstruction result. In particle image processing, this invention extracts and precisely filters multi-dimensional features such as spatial distribution, shape, and structure. It combines the rate of change of rotation and translation errors to determine stability and iteratively corrects the image, gradually aligning postures. It utilizes the geometric relationships between adjacent particles to reduce deviations, maps features to a 3D voxel space to restore details, and continuously corrects errors during 3D decoding and optimization, improving the accuracy and reliability of 3D reconstruction.

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