Annotated Stochastic Context Free Grammars for Analysis and Synthesis of Proteins

An important step to understand the main functions of a specific family of proteins is the detection of protein features that could reveal how protein chains are constituted. To achieve this aim we treated amino acid sequences of proteins as a formal language, building a Context-Free Grammar annotated using an n-gram Bayesian classifier. This formalism is able to analyze the connection between protein chains and protein functions. In order to design new protein chains with the properties of the considered family we performed a rule clustering of the grammar to build an Annotated Stochastic Context Free Grammar. Our methodology was applied to a class of Antimicrobial Peptides (AmPs): the Frog antimicrobial peptides family. Through this case study, our approach pointed out some important aspects regarding the relationship between sequences and functional domains of proteins and how protein domain motifs are preserved by natural evolution in to the amino acid sequences. Moreover our results suggest that the synthesis of new proteins with a given domain architecture can be one of the fields where application of Annotated Stochastic Context Free Grammars can be useful.