  Cryo-electron microscopy (cryo-EM) is a technique for reconstructing the
3-dimensional (3D) structure of biomolecules (especially large protein
complexes and molecular assemblies). As the resolution increases to the
near-atomic scale, building protein structures de novo from cryo-EM maps
becomes possible. Recently, recognition-based de novo building methods have
shown the potential to streamline this process. However, it cannot build a
complete structure due to the low signal-to-noise ratio (SNR) problem. At the
same time, AlphaFold has led to a great breakthrough in predicting protein
structures. This has inspired us to combine fragment recognition and structure
prediction methods to build a complete structure. In this paper, we propose a
new method named FFF that bridges protein structure prediction and protein
structure recognition with flexible fitting. First, a multi-level recognition
network is used to capture various structural features from the input 3D
cryo-EM map. Next, protein structural fragments are generated using pseudo
peptide vectors and a protein sequence alignment method based on these
extracted features. Finally, a complete structural model is constructed using
the predicted protein fragments via flexible fitting. Based on our benchmark
tests, FFF outperforms the baseline methods for building complete protein
structures.
