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A structural model of the E. coli PhoB dimer in the transcription initiation complex
Chang-Shung Tung1, Benjamin H McMahon
1Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. ct@lanl.gov
A new Motif Binding Geometries (MBG) approach models protein complexes by inferring interactions from homologous proteins. This method successfully predicted the PhoB/RNAP/σ-factor/DNA complex structure, advancing our understanding of cellular regulation.
Area of Science:
- Structural Biology
- Computational Biology
- Biochemistry
Background:
- Over 78,000 experimentally determined protein and nucleic acid structures exist, yet few represent protein complexes.
- Current homology modeling techniques for individual proteins do not extend to predicting protein complex structures.
- A gap exists in methods for understanding and predicting protein-protein interactions within complexes.
Purpose of the Study:
- To develop a novel computational method for inferring protein complex structures.
- To demonstrate the utility of the Motif Binding Geometries (MBG) approach for modeling biological complexes.
- To apply the MBG method to a significant regulatory complex involved in transcription.
Main Methods:
- Utilized a Motif Binding Geometries (MBG) approach.
- Inferred complex structures from databases of homologous protein complexes in different contexts.
- Applied the method to model the PhoB/RNAP/σ-factor/DNA transcription initiation complex.
Main Results:
- The modeled PhoB/RNAP/σ-factor/DNA complex exhibited stereo-chemical reasonableness.
- The complex demonstrated sufficient interfacial Solvent Excluded Surface Areas (SESAs) for adequate binding.
- The predicted structure was physically meaningful for transcription regulation and consistent with experimental data.
Conclusions:
- A structural model for the PhoB dimer in the transcription initiation complex was developed based on the principle that similar folding implies similar interactions.
- The MBG approach offers a scalable method for structural modeling and prediction of diverse biomolecular complexes.
- Models of protein complexes will enhance understanding of cellular regulation and signaling, complementing insights from individual protein models.
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