Mutual information analysis of the dynamic correlation between side chains in proteins

Area of Science:

• Biophysics
• Computational Biology
• Protein Science

Background:

• Protein dynamics are crucial for regulating protein function.
• Changes in protein fluctuations are linked to mutations, modifications, and ligand binding.
• Dynamic correlations between protein side chains remain understudied due to a lack of reliable analysis methods.

Purpose of the Study:

• To develop a novel method for evaluating dynamic correlations between protein side chains.
• To accurately extract intrinsic side chain fluctuation properties, overcoming limitations of conventional methods.
• To investigate the impact of mutations on protein dynamic correlations and stability.

Main Methods:

• Utilized mutual information and molecular dynamics (MD) simulations.
• Employed distance principal component analysis (distPCA) to eliminate structural superposition errors.
• Projected side chain motion onto the distPCA eigenvector space to calculate mutual information.

Main Results:

• The developed method was successfully applied to the protein eglin c and its mutants.
• Even single amino acid mutations were found to significantly alter dynamic correlations.
• The study suggests a deep relationship between changes in dynamic correlations and protein stability.

Conclusions:

• The novel method provides a reliable way to analyze dynamic correlations between protein side chains.
• The findings highlight the significant impact of mutations on protein dynamics and stability.
• This approach can be valuable for elucidating the molecular mechanisms of allosteric communication in proteins.