RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs

Published: 11 Feb 2025, Last Modified: 06 Mar 2025CPAL 2025 (Proceedings Track) PosterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: Protein Structural Prediction, Transformers, Patterson Maps
TL;DR: We use a hybrid convolution- and transformer-based model to predict electron density maps from corresponding Patterson maps and template maps, if available, via an iterative training process.
Abstract: Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce $\texttt{RecCrysFormer}$, a hybrid model that exploits the strengths of transformers with the aim of integrating experimental and ML approaches to protein structure determination from crystallographic data. $\texttt{RecCrysFormer}$ leverages Patterson maps and incorporates known standardized partial structures of amino acid residues to directly predict electron density maps, which are essential for constructing detailed atomic models through crystallographic refinement processes. $\texttt{RecCrysFormer}$ benefits from a ``recycling'' training regimen that iteratively incorporates results from crystallographic refinements and previous training runs as additional inputs in the form of template maps. Using a preliminary dataset of synthetic peptide fragments based on Protein Data Bank, $\texttt{RecCrysFormer}$ achieves good accuracy in structural predictions and shows robustness against variations in crystal parameters, such as unit cell dimensions and angles.
Submission Number: 43
Loading

OpenReview is a long-term project to advance science through improved peer review with legal nonprofit status. We gratefully acknowledge the support of the OpenReview Sponsors. © 2025 OpenReview