# Protein Structure Prediction protein motion movies and similar datasets have more than pure visual data - energy kinetics - cooperative binding ## Prediction Techniques | Name | Model Type | Description | | --------- | ----------------- | ------------------------------------------------------------- | | ESMFold | LLM | Can be run using a single input sequence | | AlphaFold | Homology Modeling | Relies on multiple sequence alignment and template structures | ### Homology Modeling 1. Model the framework regions 2. Model the CDR loops 3. Model the CDR-H3 loop - Potentially uses different approaches from steps 1 & 2 ## Determining Quality of Prediction ![[Pasted image 20240609074506.png]] Resolution Å experimental - crystallography not always under physio conditions computational - systematic/methodological errors --- ![[Pasted image 20240527225151.png|500]] **Protein Homology Modeling** - Do we have any SSM libraries with a closely homologous antibody that we have the structural information of? ![[Pasted image 20240601034610.png|500]] **From Sequence to 3D Structure** - PDB structures are only models of a *snapshot* - technically newer techniques like cryo-em try to circumvent this right? > [Accelerating Antibody Drug Discovery Through Computational Modeling (Youtube)](https://www.youtube.com/watch?v=4pMb0EJwnFg) ## Post-Prediction Analyses ### Protein Property Analysis **Scalar Based Properties:** Calculated on the entire structure - Ex: pI, net charge, hydrodynamic radius, dipole moment, hydrophobic moment, etc. **Sequence Based Properties** **Region-Based Properties:** Residue aware properties - Ex: Charge, Hydrogen Bond Donors/Acceptors, Surface Exposure, Energetic Contributions to Different Patches