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Updated: Jul 5, 2026
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
Published on: November 3, 2011
Identifying functional sites based on prediction of charged group behavior
1Northeastern University, Boston, Massachusetts, USA.
Current Protocols in Bioinformatics
|April 23, 2008
Summary
This study introduces THEMATICS, a computational tool that predicts protein function from structure. It identifies active sites by analyzing electrical potential and residue titration curves, pinpointing functionally important protein regions.
Area of Science:
- Biochemistry
- Computational Biology
- Structural Biology
Background:
- Predicting protein functional sites from 3D structure is crucial for understanding biological processes.
- Identifying active sites is key to protein function determination.
- Existing methods may not fully leverage biophysical properties for site prediction.
Purpose of the Study:
- To present THEMATICS, a computational method for predicting protein functional information using 3D structural data.
- To demonstrate the utility of electrical potential and titration curve analysis for active site identification.
- To provide a protocol for implementing and interpreting THEMATICS predictions.
Main Methods:
- Computation of the protein's electrical potential function.
- Calculation of predicted titration curves for all titratable protein groups.
- Analysis of deviations from Henderson-Hasselbalch behavior for ionizable residues.
Main Results:
- Most titratable residues exhibit predictable behavior according to the Henderson-Hasselbalch equation.
- Ionizable residues within active sites show significant deviations from typical titration behavior.
- A cluster of two or more deviating residues in close proximity reliably predicts active-site location.
Conclusions:
- THEMATICS offers a robust computational approach to predict protein functional sites.
- Analysis of residue titration curve deviations is a powerful indicator of active site residues.
- This method enhances the interpretation of protein structure for functional insights.
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