Flexible fitting of AlphaFold2-predicted models to cryo-EM density maps using elastic network models: a methodical affirmation.

MOTIVATION

This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.

RESULTS

We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 to 0.64), cation diffusion facilitator YiiP (TM-score increased from 0.76 to 0.83), and pilus (TM-score increased from 0.77 to 0.85). We explored the effect of three different mode ranges (modes 1-9, 7-9, and 1-12), masked or box-cropped density maps, numerical optimization methods, and two similarity measures (Pearson correlation and inner product). The best results were achieved for the widest mode range (modes 1-12), masked maps, inner product, and local Powell optimization. These optimal parameters were implemented in the flexible fitting utility elforge.py in version 1.4 of our Python-based ModeHunter package.

AVAILABILITY AND IMPLEMENTATION

https://modehunter.biomachina.org.