Extracting knowledge from protein structure geometry

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Presentation transcript:

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

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

Introduction

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

Local

Ignoring Smooth Kernel

Non-local: A pairwise potential Ignoring regularization

Modeling Solvent Effects

Complete Potential

Discussion

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

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