Protein Structure Refinement via DeepTracer and AlphaFold2

Abstract

Understanding the structures of proteins has numerous applications, such as vaccine development. It is a slow and labor-intensive task to manually build protein structures from experimental electron density maps, therefore, machine learning approaches have been proposed to automate this process. However, most of the experimental maps are not atomic resolution, so they are insufficient for computer vision-based machine learning methods to precisely determine the protein structure. On the other hand, methods that utilize evolutionary information from protein sequences to predict structures, like AlphaFold2, have recently achieved groundbreaking accuracy but often require manual effort to refine the results. We propose DeepTracer-Refine, an automated method to refine AlphaFold structures by aligning them to DeepTracers predicted structure. We tested our method on 39 multi-domain proteins and we improved the average residue coverage from 78.2% to 90.0% and average lDDT score from 0.67 to 0.71. We also compared DeepTracer-Refine against another method, Phenixs AlphaFold refinement, to demonstrate that our method not only performs better when the initial AlphaFold model is less precise but also exceeds Phenix in run-time performance.