Shannon information entropy in the canonical genetic code

Area of Science:

• Information theory
• Biochemistry
• Genetics

Background:

• Shannon entropy measures information, analogous to thermodynamic entropy.
• Amino acid alphabets group amino acids by chemical/physical similarities.
• The canonical genetic code exhibits inherent redundancy, offering fault tolerance.

Purpose of the Study:

• To apply Shannon entropy to amino acid alphabets for objective evaluation.
• To introduce a quantitative method for assessing alphabet schemas.
• To identify amino acid groupings that best utilize the genetic code's fault tolerance.

Main Methods:

• Partitioning 64 RNA codons (microstates) into families (macrostates) based on amino acid alphabets.
• Calculating normalized mutual information to measure Shannon entropy reduction.
• Quantifying the information value of nucleotide positions.

Main Results:

• Identified amino acid groupings that effectively leverage the genetic code's fault tolerance.
• Estimated the relative importance of properties like hydropathy, size, and side-chain acidity.
• Quantified the average information value of nucleotide positions.

Conclusions:

• The study provides a novel quantitative approach to evaluating amino acid alphabets.
• Findings shed light on the coevolution of the genetic code and the tRNA-protein translation mechanism.
• This method can inform future studies on protein structure, function, and evolution.