Entropy-Weighted Collapse Likelihood
A Next-Generation Framework for Protein Disorder & Collapse Analysis
EWCL (Entropy-Weighted Collapse Likelihood) is a biophysics-informed computational model for protein disorder, structural collapse risk, and entropy-aware pathogenicity analysis—engineered for speed, interpretability, and biological insight.
EWCL quantifies, for every amino acid residue, the likelihood of entropic collapse using only static structure and sequence-derived data. No simulations, sequence alignments, or deep learning model training are required.
Core signals integrated:
Key advantage: Fully interpretable, computationally efficient, and grounded in entropy physics—not machine learning heuristics
EWCL undergoes extensive benchmarking across large, diverse protein datasets, capturing unique biophysical disorder and collapse signals—not simply mirroring existing predictors.
• >29,000 proteins validated
• X-ray, AlphaFold, DisProt datasets
• ClinVar pathogenic variants analysis
• Monthly expansion of coverage
Final performance metrics and validation results published continuously with transparent methodology
• No training data or sequence alignments required
• Ultra-fast analysis (<2 seconds per 300 residues)
• Residue-level interpretability with explicit entropy features
• Unified analysis of disorder, flexibility, and mutation risk in one step
• Physics-based output: captures ground-truth entropy, not proxy scores
Physics-based approach vs. machine learning heuristics or molecular simulations
PDB file, AlphaFold model, or sequence
Automated per-residue entropy/collapse scoring
3D structure, heatmaps, correlation plots
Benchmark against disorder/mutation databases
Typical runtime: <2 seconds per 300 residues
Validated on:
>29,000 proteins, with ongoing monthly expansion
Current Features:
In Progress:
EWCL Philosophy:
A living scientific tool—continuously improved, transparently validated, and open about both its strengths and remaining challenges.
Special Recognition:
Professor Vladimir Uversky, whose foundational work on protein disorder and intrinsically disordered proteins directly inspired EWCL's development and validation roadmap.
EWCL is actively expanding its capabilities to serve the scientific and clinical communities
DisProt Overlay:
Direct integration of DisProt disorder annotations, enabling real-time overlay and benchmarking of EWCL predictions against curated IDP datasets.
ClinVar Variant Mapping:
Automated mapping and visualization of ClinVar pathogenic variants on EWCL-predicted high-risk regions, supporting mutation risk interpretation and clinical variant screening.
Interactive Benchmark Panels:
Side-by-side comparison of EWCL results with B-factor, pLDDT, IUPred, and experimental annotations—facilitating in-depth structural and functional analysis.
User-Driven Validation:
Allowing users to upload custom protein sets and benchmark EWCL predictions against their own experimental or curated data.
Publication-Ready Exports:
Support for downloadable, publication-quality figures (plots, heatmaps) and summary statistics in standard formats.
API & Programmatic Access:
RESTful endpoints and a Docker CLI for automated, large-scale EWCL analysis and integration into lab workflows.
Tutorials & Documentation:
Step-by-step guides, use-case examples, and full technical documentation for academic, research, and clinical users.
EWCL is an open scientific initiative. We welcome collaborations, feedback, and contributions from the research community.