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Protein Homologue Clustering and Molecular Modeling

Protein Homologue Clustering and Molecular Modeling. L. Wang. Background. Project done in Mt. Sinai School of Medicine, New York City. Lab of Dr. Roberto Sanchez. A major aim of this project is to answering: how accurate are the protein structure models from Comparative Modeling?.

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Protein Homologue Clustering and Molecular Modeling

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  1. Protein Homologue Clustering and Molecular Modeling L. Wang

  2. Background • Project done in Mt. Sinai School of Medicine, New York City. • Lab of Dr. Roberto Sanchez. • A major aim of this project is to answering: how accurate are the protein structure models from Comparative Modeling?

  3. Protein Structure and Function • Protein function is largely decided by its 3D structure • 3D structure is from 1D sequence folding • Protein structure can be determined by experimental methods, X-ray, NMR • Or predicted by modeling

  4. What is Comparative Modeling • Many protein structures are similar • Proteins sharing at least 30% sequence identity are generally similar to each other, and considered as homologues • Building target protein structure model based on a template protein structure already known

  5. How to Comparative Modeling • Find template • Get Alignment of target and template • Based on 3-D structure of template protein • Build model structure of target protein

  6. Accuracy of the Comparative Model • The structural difference between the target and templates • The alignment between target and templates

  7. Effect of Genome Project • Generated a lot of protein that we know the sequence but not the structure (need) • Generated more experimental structures (template) • By programs like psi-blast, more remote homologues can be determined (coverage) • Larger database helps more accurate alignment (accuracy)

  8. Modeller, an implementation of Comparative Modeling • Use scripts to control its behavior • Can perform a lot of tasks from aligning, to model building • Modeller can be easily automated.

  9. Project Overview • Building protein homologues using single linkage clustering algorithm • Automatically align target and template and building 3-D protein models • Comparing the models with the experimental structure

  10. Single Linkage Method vs Total Linkage

  11. Coverage Factor

  12. Building Protein Homologue Cluster • From PDB, run Blast • Build table • Get first cluster • Find representatives • Running PsiBlast on representatives • Build second table • Merge groups and get final cluster

  13. PDB Chains Unique Chains Blast Cluster (90% coverage,e-value<10-4., single linkage (1)) Find Representatives Psi-blast Re-cluster & Merge Groups (1) Refer to Duba, R.O., et al’s “Pattern Classification”, P566. Final Cluster Nice Group Keep the Representative Find Representative Pickup X-ray Data (<=2.5Å)

  14. Homologue cluster Using the good X-ray structures Building models (pair-wise align. & 3-D align.) Comparing the models with the experimental structures

  15. Error source:Alignment, plus structure difference between target and template

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