Computational Protein Designer
Designing proteins with computational precision for therapeutic applications
Using computational methods to understand, design, and engineer proteins for therapeutic applications
Rationally engineer protein variants with desired properties using computational modeling and molecular dynamics simulations.
Characterize protein systems and identify strategies for therapeutics design through advanced molecular dynamics.
Analyze protein-ligand interactions, binding free energies, and guide experimental validation through data-driven insights.
Advanced computational techniques for protein engineering and drug design
AMBER, Desmond simulations for studying dynamic behavior and interactions of biomolecules
AlphaFold-Multimer for modeling protein complexes and predicting binding configurations
HADDOCK, Schrödinger Glide for protein-ligand complex generation and analysis
MM/PBSA, MM/GBSA for binding free energy and per-residue decomposition analysis
Python, R for trajectory analysis, visualization, and statistical modeling
Predictive modeling for protein properties using XGBoost, neural networks, and ensemble methods
Accelerating therapeutic development through computational insights
Computational approaches enable rapid screening and optimization of protein variants, reducing experimental time and costs.
Identify key interaction hotspots and design mutations with precision, guided by molecular-level understanding.
Uncover molecular mechanisms and binding modes inaccessible through experimental methods alone.