Testing the Potential for Computational Chemistry to Quantify Biophysical Properties of the Non-Proteinaceous Amino Acids

Although most proteins of most living organisms are constructed from the same set of 20 amino acids, all indications are that this standard alphabet represents a mere subset of what was available to life during early evolution. However, we currently lack an appropriate quantitative framework with which to test the qualitative hypotheses that have been offered to date as explanations for nature's "choices." Specifically, although many indices have been developed to describe the 20 standard amino acids, few or no comparable data extend to prebiotically plausible alternatives because of the costly and time-consuming bench experiments that would be required. Computational chemistry (specifically quantitative structure property relationship methods) offers a potentially fast, cost-effective remedy for this knowledge gap by predicting such molecular properties in silico. Thus, we investigated the use of various freely accessible programs to predict three key amino acid properties (hydrophobicity, charge, and size). We assessed the accuracy of these predictions by comparisons with experimentally determined counterparts for appropriate test data sets. In light of these results, and factors of software accessibility and transparency, we suggest a method for further computational assessments of prebiotically plausible amino acids. The results serve as a starting point for future quantitative analysis of amino acid alphabet evolution.