A Python-based bioinformatics toolkit for automated physicochemical profiling and comparative analysis of protein sequences using Biopython.
This project enables rapid analysis of multiple protein sequences provided in FASTA format and computes important physicochemical descriptors commonly used in structural bioinformatics, protein engineering, recombinant protein expression, biomarker discovery, and computational biology workflows.
Physicochemical characterization of proteins is a fundamental step in computational biology and structural bioinformatics. Properties such as molecular weight, isoelectric point, hydrophobicity, and instability index provide valuable insight into protein behavior, solubility, structural stability, and biochemical functionality.
This toolkit automates batch analysis of protein sequences using the Biopython ProtParam module and exports the results in a structured CSV format for downstream analysis and visualization.
The project was designed to support scalable protein analysis workflows in computational biology and bioinformatics environments.
- Supports batch analysis of multiple protein sequences
- FASTA-based input workflow
- Automated physicochemical property calculation
- CSV export for downstream analysis
- Linux-compatible workflow
- Lightweight and reproducible Python implementation
- Uses Biopython ProtParam (industry-standard bioinformatics library)
The toolkit computes:
- Molecular Weight (MW)
- Isoelectric Point (pI)
- GRAVY (Grand Average of Hydropathicity)
- Instability Index
Additional metrics can be integrated in future versions:
- Aliphatic Index
- Amino Acid Composition
- Aromaticity
- Extinction Coefficient
- Secondary Structure Fraction
FASTA Input ↓ Sequence Validation ↓ Physicochemical Property Calculation (Biopython ProtParam) ↓ Comparative Protein Analysis ↓ CSV Export & Reporting
Physicochemical profiling is widely used in:
- Recombinant protein expression optimization
- Protein engineering and mutation analysis
- Biomarker discovery
- Structural bioinformatics workflows
- Therapeutic protein characterization
- Comparative sequence analysis
- Rational protein design
- Drug discovery and target characterization
Protein-Physicochemical-Analysis-Toolkit/
│
├── app.py
│
├── data/
│ └── input_proteins.fasta
│
├── results/
│ └── physicochemical_results.csv
│
├── scripts/
│ └── protein_physchem_analysis.py
│
├── docs/
│ └── dashboard.png
│
├── requirements.txt
├── LICENSE
└── README.mdThe input file must be a standard FASTA file containing one or more protein sequences.
Example:
>Protein_1
MKWVTFISLLFLFSSAYSRGVFRRDTHKSEIAHRFKDLGE
>Protein_2
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTK
-
Input file must be saved as plain text
-
Only valid amino acid characters (A–Z) should be used
-
Remove special symbols such as:
-
- $
- whitespace formatting artifacts
-
-
Sequences should be biologically valid protein sequences
git clone https://github.com/AbhignaNagaraj/Protein-Physicochemical-Analysis-Dashboard.git cd Protein-Physicochemical-Analysis-Dashboard
---
# Requirements
* Python 3
* Biopython
* Pandas
* Streamlit
* Plotly
* Linux/Unix
Install dependencies:
python3 -m pip install -r requirements.txt
---
# How to Run
## Run Streamlit Dashboard
```bash
streamlit run app.py
Analysis Results Protein ID Molecular Weight Isoelectric Point GRAVY Instability Index NP_000577.2 17627.53 7.67 -0.007 47.71 mutated 17629.46 6.82 -0.055 47.71
- Negative values indicate hydrophilic proteins
- Positive values indicate hydrophobic proteins
Hydrophilic proteins are generally more soluble in aqueous environments.
- Lower pI values often correlate with acidic proteins
- Higher pI values indicate basic proteins
pI is important for:
- protein purification
- buffer optimization
- solubility assessment
- Values < 40 indicate stable proteins
- Values > 40 suggest intrinsic instability
This project also includes a Streamlit-based interactive dashboard for automated physicochemical analysis of protein sequences.
- FASTA file upload
- Automated protein property analysis
- Interactive results table
- CSV export support
- Comparative protein analysis
streamlit run app.py
Useful for:
- recombinant protein design
- mutation impact studies
- therapeutic protein engineering
This toolkit can be used for:
- Protein engineering
- Mutation impact analysis
- Biomarker research
- Structural bioinformatics
- Comparative protein characterization
- Recombinant protein expression planning
- Bioinformatics teaching and training
- Computational biology technical assessments
- Python 3
- Streamlit
- Biopython
- Pandas
- Plotly
- Linux/Unix
Planned future developments include:
- AlphaFold structure integration
- Automated visualization dashboards
- REST API deployment
- Docker containerization
- Nextflow workflow integration
- Parallel batch processing support
- Interactive comparative protein analysis plots
Compare wild-type vs mutant proteins to assess:
- stability changes
- hydrophobicity changes
- biochemical behavior shifts
Identify proteins likely to exhibit:
- aggregation
- instability
- poor solubility
before experimental expression.
Generate physicochemical descriptors for:
- structural modeling
- docking studies
- therapeutic target analysis
Dr. Abhigna N U
PhD Bioinformatics | Computational Biology | Structural Bioinformatics | AI-Assisted Drug Discovery | Genomics & Workflow Automation
GitHub: https://github.com/AbhignaNagaraj
LinkedIn: https://www.linkedin.com/in/dr-abhigna-bioinformatics/
This project is licensed under the MIT License.
If you use this project in academic or research work, please cite appropriately or provide repository attribution.
- Biopython Development Team
- Open-source bioinformatics community
- Python scientific computing ecosystem academic, educational, and evaluation purposes.