Target Analytics Part 1: Understanding EGFR Mutations through Thermodynamics and AlphaFold Structure Prediction

Check out the Demo here!

As discussed in previous articles, the Epidermal Growth Factor Receptor (EGFR) is a critical player in regulating cellular proliferation, survival, and differentiation. As a transmembrane tyrosine kinase, it is frequently mutated in non-small cell lung cancer (NSCLC) and other malignancies. These mutations often result in significant structural and functional alterations, leading to drug resistance and disease progression. In this demo, we’ll explore how Revilico’s robust computational tools can evaluate how such mutations affect EGFR’s thermodynamic stability and structural conformation using protein melting temperature (Tm) prediction and AlphaFold-powered 3D modeling.

To start the analysis, users can search directly for EGFR using Revilico’s built-in UniProt integration. By entering “EGFR” into the platform, researchers can quickly retrieve the canonical human sequence (UniProt ID: P00533) and begin analyzing either the wild type or customized mutant versions.

Protein Melting Temperature (Tm) Shifts in Mutant EGFR

Thermodynamic stability is a fundamental property of proteins that directly influences their function and behavior under physiological conditions. One important metric to assess this stability is the melting temperature (Tm) — the point at which a protein unfolds. Tm shifts caused by mutations can signal changes in protein folding, susceptibility to degradation, and sensitivity or resistance to drugs.

Revilico’s Tm prediction module leverages machine learning models trained on both biophysical properties and structural characteristics of proteins. This enables the estimation of ΔTm — the shift in melting temperature between a wild-type and mutant protein.

• Stabilizing mutations may increase Tm and are often associated with resistance to tyrosine kinase inhibitors (TKIs). For example, the T790M gatekeeper mutation in EGFR stabilizes the protein and prevents inhibitor binding.
  
  
  

• Destabilizing mutations, on the other hand, may reduce Tm and can result in loss of function or increased degradation, making the protein more vulnerable to therapeutic interventions.
  
  
  

Researchers can input point mutations, insertions, or deletions into Revilico’s interface to receive instantaneous thermodynamic assessments. This capability is especially valuable in early-stage drug discovery when evaluating candidate compounds against a dynamic mutational landscape.

EGFR Structure Prediction and Visualization

Revilico also offers seamless AlphaFold-based structural modeling, generating high-confidence 3D models of EGFR from its sequence. This is particularly useful for visualizing how mutations affect the overall protein fold and local structure.

Interactive Visualization Features

Once a structure is generated, Revilico provides an intuitive 3D environment with several powerful options:

• Toggle Sidechains: Users can switch between backbone-only views and full sidechain representations to assess residue-specific effects.

• Color Schemes: IDDT, rainbow, etc.

• Model Ranks: Multiple AlphaFold structure ranks are available for comparison, helping users evaluate structural uncertainty or conformational alternatives.

These visual tools allow researchers to:

• Inspect structural perturbations near mutation sites.

• Detect local clashes or changes in residue packing.

• Examine solvent exposure patterns and domain interactions.

Conclusion

Revilico offers an accessible and powerful framework for studying EGFR mutations through thermodynamic prediction and structural modeling. With built-in UniProt integration, users can go from sequence to insight in just a few clicks. Key features include:

• Rapid melting temperature predictions to assess stability impacts.
  
  
  

• High-quality AlphaFold models with flexible visualization options.
  
  
  

• Sidechain toggling, color scheme adjustments, and multi-rank structure exploration.
  
  
  

For cancer biologists, drug developers, and structural researchers, Revilico streamlines workflows that would otherwise require multiple tools and platforms. By making structural and biophysical analysis more intuitive, it supports deeper, faster understanding of EGFR mutant behavior in disease and therapy.

As oncology continues to move toward personalized, mutation-driven therapies, tools that provide early, mechanistic insight into the consequences of mutations are essential. Revilico’s integration of Tm prediction and AlphaFold structural modeling empowers researchers to bridge the gap between sequence changes and biological outcomes. By examining both thermodynamic stability and 3D structure, scientists can predict the behavior of mutant EGFR proteins with greater confidence — accelerating therapeutic design and precision treatment strategies.