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A Protocol for Computer-Based Protein Structure and Function Prediction
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Published on: November 3, 2011

Geometry of protein shape and its evolutionary pattern for function prediction and characterization.

Jie Liang1

  • 1Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60612, USA. jliang@uic.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|December 8, 2009
PubMed
Summary

We analyzed protein structures to identify packing defects like voids and pockets. This analysis helps predict protein function and enzyme binding properties by understanding evolutionary pressures.

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Area of Science:

  • Structural biology
  • Computational biophysics
  • Protein bioinformatics

Background:

  • Proteins are large molecules with complex atomic structures.
  • Understanding protein structure is crucial for deciphering biological function.
  • Packing defects, such as voids and pockets, are inherent features of protein structures.

Purpose of the Study:

  • To compute and analyze protein packing defects (voids and pockets) from experimental structures.
  • To investigate the distribution, scaling, and origin of these defects.
  • To explore the use of defect analysis in predicting protein function and enzyme binding properties.

Main Methods:

  • Computational analysis of experimentally resolved protein structures.
  • Characterization of voids and pockets within protein interiors.
  • Analysis of defect distribution and scaling behavior.
  • Isolation and estimation of evolutionary selection pressures.

Main Results:

  • Identified and quantified protein packing defects (voids and pockets).
  • Characterized the distribution and scaling laws of these defects.
  • Developed methods to distinguish functional selection pressure from folding/stability constraints.
  • Demonstrated potential for predicting protein function and binding properties.

Conclusions:

  • Protein packing defects are quantifiable features with predictable scaling behaviors.
  • Analysis of these defects, combined with evolutionary pressure insights, can enhance protein function prediction.
  • This approach offers a novel way to characterize enzyme binding sites and properties.