Choosing the Right AI Algorithm for Your Protein Prediction Project

AI-powered protein structure prediction now extends beyond individual protein structures to modeling protein complexes, ligand interactions, protein-protein docking, and antibody-antigen binding. But different algorithms have different strengths and limitations.

This webinar takes a technical look at the AI-powered structure prediction tools available in Lasergene Protein: Boltz-1 and Boltz-2, ESMFold2, OpenDDE, ColabFold, and ColabDock. Presenters Thomas Leary, Protein Product Specialist, and Erik Edlund, Protein Application Developer, discuss the pros and cons of each approach and demonstrate their uses with real data. 

• Visualize, compare, and evaluate protein structure models
• Interpret prediction confidence metrics and assess model quality
• Analyze sequence variation and its structural impact
• Generate predictions using Boltz and AlphaFold-Multimer
• Leverage new batch prediction workflows to efficiently process larger sets of protein sequences
• Use the new guided protein structure prediction workflow to simplify setup and streamline modeling