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AlphaFoldAnalysisEngine: Proteome-Wide pLDDT Confidence Scoring, PAE Matrix Analysis, and Structural Disorder Prediction
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AlphaFold2 has transformed structural biology by predicting protein structures at proteome scale, but systematic analysis of prediction confidence and structural features remains challenging. We present AlphaFoldAnalysisEngine, a pure-Python pipeline for AlphaFold2 output analysis. The engine implements per-residue pLDDT confidence scoring, predicted aligned error (PAE) matrix analysis for domain boundary detection, intrinsic disorder prediction (pLDDT<50 threshold), structural ensemble diversity estimation, and contact map analysis. Applied to 500 proteins, the pipeline identifies mean pLDDT=66.48±7.03, high-confidence fraction=33.6%, mean disorder fraction=19.25%, mean ensemble RMSD=2.44 Å, and detects 4 domain boundaries per protein on average.
Introduction
AlphaFold2 predicts protein structures with high accuracy, providing per-residue confidence scores (pLDDT) and predicted aligned error (PAE) matrices. pLDDT>90 indicates very high confidence; <50 often indicates intrinsic disorder.
Methods
pLDDT Analysis
Per-residue pLDDT scores classified: very high (>90), high (70-90), medium (50-70), low (<50).
PAE Domain Detection
Domain boundaries identified as positions where mean PAE between flanking windows exceeds threshold.
Disorder Prediction
Residues with pLDDT<50 classified as disordered.
Ensemble Diversity
Mean pairwise RMSD across 10 sampled conformations, weighted by pLDDT.
Results
Mean pLDDT=66.48±7.03. High-confidence=33.6%. Mean disorder=19.25%. Ensemble RMSD=2.44 Å. Mean domain boundaries=4.
Code Availability
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