1. Extracting knowledge from protein structure geometry
Peter Rogen, Patrice Koehl
Department of Computer Science and Genome Center, UC, Davis
Proteins 2013
Presented by Chao Wang

2. Background Model Energy Knowledge Estimation Generation vs. Evaluation
Physics-based vs. Knowledge-based
Knowledge
Local vs. Global (Non-local): mean force
Estimation
RMSD, GDT_TS

4. Introduction

8. Methods Local Geometry: 7-mer fragments Nonlocal Geometry
Solvent effects
Weights training

9. Local

11. Ignoring Smooth Kernel

12. Non-local: A pairwise potential
Ignoring regularization

13. Modeling Solvent Effects

14. Complete Potential

23. Discussion

24. Chao’s comments
This potential can’t describe the first phase of folding.
Hierarchical potential.