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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Inferring protein-DNA dependencies using motif alignments and mutual information
Shaun Mahony1, Philip E Auron, Panayiotis V Benos
1Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, USA. shaun.mahony@ccbb.pitt.edu
Bioinformatics (Oxford, England)
|July 25, 2007
Summary
Mutual information analysis reveals coevolving amino acids in transcription factors and nucleotides in DNA targets. This method identifies potential protein-DNA contacts, aiding structural insights and guiding experimental studies.
Area of Science:
- Bioinformatics
- Structural Biology
- Genomics
Background:
- Mutual information (MI) is a powerful tool for analyzing covarying positions in biological sequences.
- MI has been previously applied to infer RNA secondary structures from multiple sequence alignments.
Purpose of the Study:
- To investigate the application of mutual information for discovering coevolving amino acids in transcription factors and nucleotides in their DNA-binding targets.
- To demonstrate that MI can identify potential protein-DNA contacts from sequence data alone.
Main Methods:
- Utilizing mutual information analysis on aligned transcription factor binding domains and their corresponding DNA target motifs.
- Comparing MI-derived predictions with known contacting positions in various DNA-binding motif families.
Main Results:
- Demonstrated that mutually covarying base-amino acid positions can indicate potential protein-DNA contacts.
- Identified known contacting positions within C2H2 zinc finger, homeodomain, and bHLH DNA-binding motif families.
- Showcased the potential of MI to predict base-amino acid contacting pairs.
Conclusions:
- Mutual information analysis offers a sequence-based approach to infer protein-DNA contacts.
- This method provides valuable structural insights into DNA recognition mechanisms.
- The findings can guide future experimental investigations into transcription factor-DNA interactions.
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