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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Using Total Network Dissimilarity to Analyze Protein Structures Across Homologs
Vasam Manjveekar Prabantu1, Vasundhara Gadiyaram1, Saraswathi Vishveshwara2
1Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
Methods in Molecular Biology (Clifton, N.J.)
|November 1, 2025
Summary
This study introduces a new method using graph spectral analysis to compare protein structural networks (PSNs) of homologous proteins, revealing evolutionary patterns and structure-function relationships.
Area of Science:
- Structural bioinformatics
- Computational biology
- Biophysics
Background:
- Homologous proteins share evolutionary origins and often exhibit conserved structural features.
- Understanding structural variations is key to deciphering protein function and evolution.
- Existing methods may not fully capture subtle differences in protein structural networks.
Purpose of the Study:
- To develop and present a novel methodology for comparing protein structural networks (PSNs) of homologous proteins.
- To systematically explore similarities and differences in protein structures.
- To elucidate relationships between protein sequence, structure, and function.
Main Methods:
- Leveraging graph spectral analysis and quantitative comparison techniques.
- Employing network similarity score and total network dissimilarity.
- Utilizing Fiedler vector analysis for robust comparison.
Main Results:
- The methodology provides a robust framework for identifying subtle patterns in protein structure evolution.
- Quantitative comparison of PSNs reveals intricate relationships.
- Analysis highlights similarities and differences between homologous protein structures.
Conclusions:
- The developed approach offers significant insights into protein biology.
- This method enhances our understanding of protein structure evolution.
- It contributes to the systematic exploration of protein sequence-structure-function relationships.
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