flexible multi-scale fitting of atomic structures into low- resolution electron density maps with...

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Flexible Multi-scale Fitting of Atomic Structures into Low-resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita, Brooks. 2004. 337. 985-999 Used NMA to fit high resolution xtal structures to low resolution EM maps. Mostly concerned with conformational change. Also employed flexible fitting method with Calpha only claiming this allowed larger systems to be computationally tractable. Show that this method works better than Situs package on the targets they tested.

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Page 1: Flexible Multi-scale Fitting of Atomic Structures into Low- resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita,

Flexible Multi-scale Fitting of Atomic Structures into Low-resolution Electron Density Maps with Elastic Network

Normal Mode AnalysisTama, Miyashita, Brooks. 2004. 337. 985-999

Used NMA to fit high resolution xtal structures to low resolution EM maps. Mostly concerned with conformational change.

Also employed flexible fitting method with Calpha only claiming this allowed larger systems to be computationally tractable.

Show that this method works better than Situs package on the targets they tested.

Page 2: Flexible Multi-scale Fitting of Atomic Structures into Low- resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita,

ProMate: A Structure Based Prediction Program to Identify the Localization of Protein-Protein Binding Sites

Neuvirth, Raz, Schreiber. 2004. 338. 181-199

Binding surfaces share common properties that distinguish them from non-binding surfaces

Interesting review of binding surface properties, as compiled through many investigations

I think they draw some questionable conclusions.

The algorithm correctly predicts the binding site in about 50% of cases, random would be 13%.

Clear preference for binding interface to exist in region of loops. Mean loop length 11.17

Page 3: Flexible Multi-scale Fitting of Atomic Structures into Low- resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita,

Testing a Flexible-receptor Docking Algorithm in a Model Binding Site

Wei, Weaver, Ferrari, Matthews, Shoichet. 2004. 337. 1161-1182

Flexible receptor obtained by ensemble of xtal structures

Linear scaling opposed to exponential scaling

Recommend reading, wont say much more.

Page 4: Flexible Multi-scale Fitting of Atomic Structures into Low- resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita,

Can Contact Potentials Reliably Predict Stability of Proteins?

Khatun, Khare, Dokholyan. 2004. 336. 1223-1238

Contact potential describes the relative free energy of a given protein conformation by evaluating the interaction among residues

Evaluate a set of mutants and try to predict the ΔΔG for a single amino acid mutation

- contact potentials parameterized from experimental data (experimental error ~ kt)

They show that contact potentials are neither accurate nor transferable and claim that it is impossible to develop a set of transferable contact parameters to assess changes in stability of multiple proteins.

Page 5: Flexible Multi-scale Fitting of Atomic Structures into Low- resolution Electron Density Maps with Elastic Network Normal Mode Analysis Tama, Miyashita,

Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases

Cavasotto, Abagyan. 2004. 337. 209-225

ICM-flexible receptor docking algorithm (IFREDA)

-generates a discrete set of receptor conformations

Claims:

-both side chain rearrangements and backbone movements, including loop movements are taken into account

-this method helps when no holo structure available, they dock ligand into pocket, rotate and translate, reoptimize pocket, use this ensemble for docking