RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs
By: Tom Pan , Evan Dramko , Mitchell D. Miller and more
Potential Business Impact:
Builds detailed protein models from crystal data.
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.
Similar Papers
Completion of partial structures using Patterson maps with the CrysFormer machine learning model
Biological Physics
Helps scientists see tiny protein shapes faster.
Protein Secondary Structure Prediction Using 3D Graphs and Relation-Aware Message Passing Transformers
Machine Learning (CS)
Helps predict how proteins fold to work.
ProFusion: 3D Reconstruction of Protein Complex Structures from Multi-view AFM Images
CV and Pattern Recognition
Builds 3D protein shapes from tiny height maps.