A method for probing the mutational landscape of amyloid structure

Area of Science:

• Biophysics
• Computational Biology
• Structural Biology

Background:

• Proteins self-assemble into amyloid fibrils, which are biologically and clinically significant.
• Amyloid fibril structure varies with sequence and environment, and mutations can alter pathogenicity.
• Experimental determination of amyloid structures is challenging, necessitating computational approaches.

Purpose of the Study:

• To develop a computational method for predicting and analyzing wild-type and mutant amyloid structures.
• To energetically quantify the impact of sequence mutations on fibril conformation and stability.
• To provide a tool for discriminating between different amyloid conformations.

Main Methods:

• AmyloidMutants utilizes a statistical mechanics approach based on protein mutational landscapes.
• The method energetically quantifies mutation effects on fibril conformation and stability.
• It predicts complete super-secondary structures, enabling discrimination of dissimilar conformations.

Main Results:

• AmyloidMutants shows a 2-fold improvement in prediction accuracy compared to existing tools for wild-type structures.
• It accurately predicts conformational switches in mutants, such as the toxic 'Iowa' mutant of Aβ.
• The tool predicted reduced amyloid formation for a HET-s glutamine mutant, which was experimentally confirmed.

Conclusions:

• AmyloidMutants is a powerful computational tool for predicting amyloid structures and analyzing mutation effects.
• The method enhances understanding of amyloid fibril formation, stability, and pathogenicity.
• It offers a valuable resource for researchers studying amyloid-related diseases and protein self-assembly.