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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Predicting loop conformational ensembles.

Claire Marks¹, Jiye Shi², Charlotte M Deane¹

¹Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.

Bioinformatics (Oxford, England)

|November 15, 2017

Summary

This summary is machine-generated.

Current protein loop prediction algorithms struggle to model conformational diversity. These methods accurately predict single-conformation loops but fail to capture multiple native states, hindering accurate protein structure ensemble generation.

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Area of Science:

Computational biology
Structural bioinformatics
Protein structure prediction

Background:

Protein function relies on multiple stable conformations.
Accurate modeling of conformational diversity is crucial for structure prediction algorithms.
Current algorithms aim to produce ensembles representing conformational diversity, not just single states.

Purpose of the Study:

To assess the capability of current loop prediction algorithms in modeling protein conformational diversity.
To evaluate if these algorithms can generate and select decoys close to experimentally determined conformations.
To investigate the performance on loops with single versus multiple known conformations.

Main Methods:

Utilized existing loop prediction algorithms.
Predicted structures for loops with single and multiple experimentally determined conformations.
Assessed the ability of algorithms to generate decoys representing observed structures.

Main Results:

Loops with a single known conformation were predicted accurately.
Conformationally diverse loops were poorly modeled.
Predicted decoys often did not resemble any of the known native conformers for diverse loops.

Conclusions:

Current loop prediction methodologies are not well-suited for conformationally diverse protein regions.
The assumption that decoy sets contain multiple native conformations is often incorrect.
Accurate prediction of functionally important, conformationally diverse protein regions remains a challenge.