Biomedical subjects

Michael R Sawaya

Publications and source records attributed to Michael R Sawaya.

3 recordsLinked to original sources

PubMed2026

Cryo-EM structure of TGFBIp fibrils driven by a corneal dystrophy-linked mutation enables design of peptide inhibitors of aggregation.

Corneal dystrophy is a heterogeneous group of diseases which manifests clinically by progressive corneal opacity and diminishing visual acuity. A group of corneal dystrophies are linked to autosomal dominant mutations in transforming growth factor β-induced protein (TGFBIp) and characterized by extracellular amyloid-positive deposits of unknown molecular structure. Here, we determined the cryogenic-electron microscopy (cryo-EM) structure of amyloid fibrils formed by the TGFBIp FAS1-4 domain with corneal dystrophy-linked mutation V624M. The L569 to N609 fibril core, which includes the Y571-R588 segment enriched in patient corneal deposits, forms symmetrical protofilaments with internal solvent channels. Leveraging this structure, we designed peptide inhibitors intended to bind onto fibril ends to block elongation, targeting the unequal growth of symmetrical protofilaments. Our G1 and H4 inhibitors exhibit concentration-dependent reduction of TGFBIp FAS1-4 aggregation as assessed by Thioflavin T, solubility fractionation, and electron microscopy. Our work illustrates how fibril structures can guide rational inhibitor design and suggests the targeting of protein aggregates as a therapeutic approach for corneal and ocular diseases.

betaIG-H3 Protein↗

PubMed2025

Leveraging bioorthogonal conjugation for alpha synuclein fibril surveillance.

Alpha synuclein (α-syn) amyloid fibrils are associated with various neurodegenerative diseases. To better understand the molecular and cellular basis for α-syn fibril persistence and spread, we implemented a fluorophore labeling strategy to surveil pre-formed α-syn fibrils in solution and in cells. We leveraged amber codon mediated incorporation of a tetrazine-based artificial amino acid (TetV2.0) to install a cyclooctene-conjugated Janeliaflour, JF549, at four sites on human α-syn: residues 4, 60, 96 and 136. Fast coupling occurred under mild buffer conditions and in the presence of the disease-associated cofactor and cytotoxic lipid, psychosine. Labeled fibrils retained their polymorphic features, seeded the growth of new fibrils in vitro, and induced the seeding of positive puncta in α-syn FRET biosensor HEK293T cells. This allowed simultaneous tracking of exogenous and endogenous α-syn aggregates in biosensor cells, and their localization within the cells. In doing so, our approach facilitates more detailed mechanistic investigation of α-syn aggregates.

Synuclein↗

PubMed2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes.

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Machine Learning↗