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Improving consensus structure by eliminating averaging artifacts.

¹Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, USA. dbahadur@uncc.edu

BMC Structural Biology

|March 10, 2009

Summary

This study introduces a Monte Carlo method to reduce artifacts in averaged molecular structures, improving geometric realism for proteins and RNA. The new approach significantly minimizes atomic clashes while maintaining structural accuracy.

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Area of Science:

Structural Biology
Computational Biology
Biophysics

Background:

Nuclear Magnetic Resonance (NMR) and molecular dynamics generate structural ensembles.
Averaging 3D coordinates yields consensus structures but can introduce artifacts like unphysical bond lengths and angles.
Existing methods for structure averaging in proteins and RNA may produce unrealistic local geometries.

Purpose of the Study:

To develop and assess a novel method for deriving representative molecular structures from ensembles.
To minimize artifacts, such as unrealistic local geometries and atomic clashes, resulting from structure averaging.
To improve the accuracy and reliability of consensus structures derived from structural biology data.

Main Methods:

A Monte Carlo simulation technique was employed to refine structures.
A harmonic pseudo energy function was used to drive structures towards an averaged conformation.
The method was tested on C-alpha models of 1364 proteins predicted by the TASSER algorithm.

Main Results:

The refined structures showed a minimal increase in Root Mean Square Deviation (RMSD) from native structures (0.08 Å difference).
Atomic clashes were drastically reduced from 63% to 1%, with most refined proteins having zero clashes.
The method produced representative structures comparable in RMSD quality to existing tools but with significantly fewer clashes.

Conclusions:

The developed approach effectively removes averaging artifacts, benefiting the structural biology community.
This method is applicable to various problems involving the averaging of 3D coordinates, including RNA secondary structure prediction.
The technique offers a valuable tool for generating more accurate and realistic consensus molecular structures.