Data associated with "Employing Deep Mutational Scanning in the E. coli Periplasm to Decode the Thermodynamic Landscape For Amyloid Formation"

This dataset comprises the quantitative outputs and supporting measurements generated in our study of amyloid formation using a high-throughput deep mutational scan (DMS) in the E. coli periplasm using the tripartite β-lactamase assay. It includes:

· Variant fitness scores for Aβ₄₂: log₂‐normalized deep sequencing read counts across ~750 of the 798 possible single‐amino‐acid substitutions, measured under incrementally increasing ampicillin selection.

· Biological replicates: three independent DMS experiments for Aβ₄₂.

· Aggregation‐propensity profiles: per‐residue scores from four established algorithms (Camsol, TANGO, AmyloGram, AGGRESCAN) for comparison with the DMS data.

· Proteostat fluorescence and confocal images: per‐cell fluorescence intensities confirming amyloid deposition in bacteria expressing β‐lactamase–Aβ₄₂ versus controls.

· FoldX‐derived ΔG contributions: per‐residue thermodynamic stability calculations for Aβ₄₂/Aβ₄₀ fibril structures, averaged over a five‐residue window.

· Critical concentration measurements: in vitro Thioflavin T endpoint fluorescence assays for a panel of Aβ₄₂ variants, with extrapolated monomer concentrations required for fibril formation.

· Machine‐learning feature set: explicit sequence descriptors (β-sheet propensity, polarity, side-chain bulkiness, etc.) and model SHAP values, together with pre‐trained

DMS‐based random‐forest predictions of variant fitness for Aβ₄₂ and three additional amyloidogenic intrinsically disordered proteins (α-synuclein, hIAPP, TDP-43).