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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Pseudocontact Shift-Driven Iterative Resampling for 3D Structure Determinations of Large Proteins.

Kala Bharath Pilla¹, Gottfried Otting¹, Thomas Huber¹

¹Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia.

Journal of Molecular Biology

|January 19, 2016

Summary

This study enhances protein structure prediction using pseudocontact shifts (PCSs) from lanthanides. The new algorithm accurately models larger proteins with sparse PCS data, improving 3D structure determination.

Keywords:

3D structure determination NMR spectroscopy conformational sampling membrane proteins pseudocontact shifts

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Area of Science:

Biochemistry
Structural Biology
Computational Chemistry

Background:

Paramagnetic lanthanides induce pseudocontact shifts (PCSs) in nuclear magnetic resonance (NMR) spectra.
PCSs provide valuable long-range structural restraints for protein modeling.
Sparse PCS data significantly improves 3D protein structure prediction using Rosetta.

Purpose of the Study:

To extend the use of PCS data for 3D structure prediction of larger proteins (>200 residues).
To develop a novel algorithm utilizing PCSs as the sole experimental restraint for protein modeling.
To enhance Rosetta's fragment assembly method for improved accuracy in protein structure determination.

Main Methods:

Utilized PCSs from paramagnetic lanthanide ions attached at four different sites.
Developed a PCS-driven iterative resampling algorithm for fragment assembly.
Applied sparse PCS data at multiple stages: identifying local structures, ranking models, and rebuilding fragment libraries.

Main Results:

The algorithm successfully generated accurate 3D models for eight diverse proteins (100-220 residues).
Demonstrated the effectiveness of using solely backbone amide proton PCSs.
Achieved accurate modeling for proteins previously difficult to model without extensive experimental restraints.

Conclusions:

PCS data, even when sparse, is highly effective for accurate protein structure prediction.
The developed iterative resampling algorithm enhances Rosetta's capabilities for modeling larger proteins.
This approach offers a powerful method for determining complex protein structures using minimal experimental data.