Coevolutionary signals across protein lineages help capture multiple protein conformations

Area of Science:

• Molecular Biology
• Structural Biology
• Biophysics

Background:

• Determining the complete functional conformational landscape of proteins, including native and intermediate states, remains a challenge in molecular biology.
• Understanding protein dynamics is crucial for comprehending biological function and developing targeted therapeutics.

Purpose of the Study:

• To develop and validate a novel computational approach for uncovering the full spectrum of protein functional states.
• To identify a signature of functionally important protein states using sequence coevolution data.

Main Methods:

• Employed direct coupling analysis (DCA) to detect coevolving residue pairs within protein sequences.
• Developed a protein structure-based model leveraging the DCA signature to predict conformational diversity.
• Applied the method to medium to large proteins (200-450 amino acids) across various protein families.

Main Results:

• Successfully revealed distinct functional structural states for multiple protein families with high resolution (mean ~1.9 Å rmsd for nonapo structures).
• Identified several functionally important intermediate or hidden protein states.
• Demonstrated the broad applicability of the enhanced sampling method.

Conclusions:

• The combination of direct coupling analysis and structure-based modeling effectively predicts diverse protein conformational landscapes.
• This approach significantly advances protein structure determination and offers new avenues for designing drugs that target specific functional states, including intermediates.