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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Disentangling evolutionary signals: conservation, specificity determining positions and coevolution. Implication for
Elin Teppa1, Angela D Wilkins, Morten Nielsen
1Fundación Instituto Leloir, Avda, Patricias Argentinas 435, CABA, C1405BWE, Argentina.
BMC Bioinformatics
|September 18, 2012
Summary
Identifying critical protein residues is key. This study benchmarks evolutionary methods, finding that integrating conservation with evolutionary trace (ET) and mutual information (MI) scores improves catalytic residue (CR) prediction.
Area of Science:
- Protein bioinformatics
- Computational biology
- Evolutionary analysis
Background:
- Numerous methods exist to identify functionally critical protein residues using evolutionary, sequence, and structural data.
- The overlap and comparative predictive power of these methods, especially for catalytic residues (CR), remain unclear.
Purpose of the Study:
- To assess the predictive potential and overlap of different evolutionary signal-based methods for identifying CR.
- To investigate the complementary information provided by various evolutionary signals and structural proximity for CR prediction.
Main Methods:
- Benchmarking diverse methods on enzymatic protein families using evolutionary signals.
- Analyzing information content within multiple sequence alignments.
- Integrating structural proximity scores with conservation, real-value Evolutionary Trace (rvET), and cumulative mutual information (cMI) scores.
Main Results:
- Methods clustered into three groups: rvET/conservation, mutual information (MI), and specificity determining position (SDP) predictors.
- Only rvET/conservation and MI methods reliably predicted CR when structural information was integrated.
- Integrating conservation with rvET and cMI proximity scores yielded the highest CR prediction performance.
- rvET and cMI scores provided complementary information for prediction systems.
Conclusions:
- Understanding evolutionary signals aids in identifying critical protein residues.
- Combining structural information with higher-order evolutionary signals and sequence conservation enhances catalytic residue detection.
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