Information theoretic measures for quantifying sequence-ensemble relationships of intrinsically disordered proteins

Area of Science:

• Biochemistry
• Computational Biology
• Protein Design

Background:

• Intrinsically disordered proteins (IDPs) perform diverse biological functions.
• Current de novo design strategies for IDPs often rely on compositional biases and sequence patterns.
• Existing methods struggle to fully capture the complex sequence-function relationships in IDPs.

Purpose of the Study:

• To develop novel information theoretic measures for characterizing sequence-ensemble relationships (SERs) in IDPs.
• To provide a framework for improving the de novo design of IDPs.
• To enhance understanding of how protein sequence dictates conformational ensemble and function.

Main Methods:

• Utilized all-atom simulations to generate statistically robust conformational ensembles for IDPs.
• Developed and applied information theoretic measures to quantify sequence-ensemble relationships (SERs).
• Evaluated the utility of SERs in comparing proteins with conserved and poorly conserved sequences.

Main Results:

• Individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs.
• The developed information theoretic measures for SERs are effective, even with poor sequence conservation.
• SERs provide a valuable metric for comparing different IDP sequences and their conformational ensembles.

Conclusions:

• Information theoretic measures of SERs offer a more comprehensive approach to understanding IDPs.
• De novo design of IDPs guided by SERs can lead to improved insights into sequence-ensemble-function linkages.
• This work provides a foundation for more sophisticated and predictive design of functional IDPs.