Beyond the Static Snapshot: Introducing Dynamics-Aware Evolutionary Profiling
- Taner Karagol
- Feb 2
- 2 min read
Static structures only tell half the story. To truly understand the impact of a mutation or the potential for a drug target, we need to understand the interplay between a protein's evolutionary history and its physical movement.
We are excited to introduce Dynamics-Aware Evolutionary Profiling, a new framework that mixes Molecular Dynamics (MD) trajectories with Evolutionary Data to provide a complete dynamic profile of your target.
The Science: Uncoupling Rigidity from Motion
Traditionally, "conservation" in proteins has been associated with "stability." If a residue is conserved, we assume it is a rigid structural anchor. But nature is more complex than that.
Dynamics-Aware Evolutionary Profiling challenges this assumption. By integrating MD trajectories with evolutionary alignments, we decompose conservation into three distinct, biologically meaningful layers:
DCS (Dynamic Conserved Score): Identifies residues that are evolutionarily conserved but remain mobile. These are the functional hinges,flexible loops, and gates that must move for the protein to work.
RCS (Rigid Conserved Score): Highlights the static structural anchors essential for folding and stability.
Co-evolutionary Coupled Scores: Maps the intricate relationship between these conserved regions.
Why This Matters: From Variants to Drugs
This multi-dimensional view allows for enhanced variant interpretation, helping researchers distinguish between benign flexibility and pathogenic loss of function in dynamic regions.
Furthermore, the identification of dynamic-conserved residues (DCS) fundamentally expands the boundaries of the druggable proteome. By highlighting "mobile" conserved sites, ADEPT (Automated Dynamics-Aware Evolutionary Profiling Tool) improves the resolution of genetic data and reveals potential cryptic pockets that static structures miss.
Open Access for the Community
We believe that advanced tools should be accessible to everyone. Whether you are a biologist without a coding background or a bioinformatician building a pipeline, we have a solution for you.
1. The Web Interface (No Coding Required)
You don’t need to be a simulation expert to use ADEPT. We have built an open-access web tool where you can apply this framework to your own proteins directly in the browser.
👉 Try it here: www.karagolresearch.com/adept
2. The Python Package (For Local & High-Throughput)
For researchers needing to process sensitive data locally, run high-throughput screens, or integrate ADEPT into existing bioinformatics pipelines, we offer the standalone adept-evo Python library.
Installation:
pip install adept-evo
CLI Usage: You can run the full analysis and mapping pipeline directly from your terminal:
adept --name MyProtein --rmsf data/rmsf.csv --data data/cons.csv --pdb structure.pdb
3. Cloud & Reproducibility (GitHub & Colab) ☁️
Our GitHub repository contains the full source code, alongside datasets for the large-scale statistical analysis presented in our manuscript.
> Note: For faster results on large datasets, we recommend using a high-performance (HPC) environment.
Citation
If you use this framework or the ADEPT tool in your research, please cite:
Karagöl, T., & Karagöl, A. (2026). Dynamics-aware Evolutionary Profiling Uncouples Structural Rigidity from Functional Motion to Enable Enhanced Variant Interpretation.
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