Network of hotspot interactions cluster tau amyloid folds

• Published in: Nature communications 2023-02, Vol.14 (1), p.895-19, Article 895
• Main Authors: Mullapudi, Vishruth, Vaquer-Alicea, Jaime, Bommareddy, Vaibhav, Vega, Anthony R., Ryder, Bryan D., White, Charles L., Diamond, Marc. I., Joachimiak, Lukasz A.
• Format: Article
• Language: English
• Subjects: 101/28; 119/118; 13; 13/31; 631/114/470; 631/45/460; 631/535/1267; 631/57/2266; 82/75; Alanine; Amino Acid Sequence; Amino acids; Amyloid; Amyloid - metabolism; Amyloid beta-Peptides - metabolism; Amyloidogenic Proteins; Control stability; Electron microscopy; Fibrils; Folding; Humanities and Social Sciences; Machine learning; Molecular Conformation; multidisciplinary; Neurodegenerative diseases; Protein Conformation, beta-Strand; Protein folding; Proteins; Science; Science (multidisciplinary); Tau protein; tau Proteins - metabolism
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-36572-3

Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformations of β-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We evaluate our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures. The authors developed a computational approach to probe the stability of amyloid fibrils and discover networks of hotspot interactions. Understanding the mechanisms of amyloid folding will help identify novel methods to treat protein (mis)folding diseases.