A method for partitioning the information contained in a protein sequence between its structure and function

TL;DR

This paper introduces a computational method to quantify the information in protein sequences related to their structure and function, revealing insights into protein behavior and potential applications in protein design and function prediction.

Contribution

A novel algorithm that evaluates the information needed for protein structure and function, linking information theory with protein folding and functional prediction.

Findings

The information gap in protein sequences closely matches the information needed for function.

The method can predict the information gap directly from sequences.

Potential to distinguish ordered from disordered proteins and identify unknown functions.

Abstract

Proteins employ the information stored in the genetic code and translated into their sequences to carry out well-defined functions in the cellular environment. The possibility to encode for such functions is controlled by the balance between the amount of information supplied by the sequence and that left after that the protein has folded into its structure. We developed a computational algorithm to evaluate the amount of information necessary to specify the protein structure, keeping into account the thermodynamic properties of protein folding. We thus show that the information remaining in the protein sequence after encoding for its structure (the 'information gap') is very close to what needed to encode for its function and interactions. Then, by predicting the information gap directly from the protein sequence, we show that it may be possible to use these insights from information theory to discriminate between ordered and disordered proteins, to identify unknown functions, and to optimize designed proteins sequences.