Soft disorder modulates the assembly path of protein complexes

Area of Science:

• Structural biology
• Biophysics
• Computational biology

Background:

• Protein interactions are fundamental to biological processes.
• Protein flexibility and disorder play roles in function, but their involvement in complex assembly is not fully understood.
• Existing models often focus on rigid interactions, potentially overlooking dynamic aspects.

Purpose of the Study:

• To provide statistical evidence for the role of mobile and disordered regions in protein complex assembly.
• To investigate how protein disorder influences the formation of interaction interfaces during assembly.
• To explore the relationship between disorder and the accuracy of protein structure prediction methods.

Main Methods:

• Analysis of 40,205 hierarchies of protein complexes from the Protein Data Bank (PDB) based on X-ray crystallography.
• Categorization of residues as softly disordered (flexible, amorphous, or missing) in preceding structures within assembly hierarchies.
• Correlation analysis of disordered regions with newly formed interfaces during oligomerization, including surface-specific analysis.
• Investigation of the link between AlphaFold2's pLDDT confidence metric and soft disorder.

Main Results:

• New interfaces in protein complexes predominantly form at residues previously identified as softly disordered in simpler precursor complexes.
• This correlation holds true even when restricting analysis to surface residues, ruling out a simple surface effect.
• The location of these disordered residues shifts within the protein sequence as the number of interacting partners increases.
• A strong correlation exists between low-confidence regions predicted by AlphaFold2 (low pLDDT) and the union of observed soft disorder regions.

Conclusions:

• Protein complex assembly follows a general mechanism involving soft disorder, which modulates interface formation.
• Predicting the structure of large protein complexes is challenging and requires integrating soft disorder prediction with structure prediction.
• The pLDDT metric from AlphaFold2 can serve as a valuable proxy for identifying protein interfaces and predicting assembly pathways.