Annotated stochastic context free grammars for analysis and synthesis of proteins

Eva Sciacca, Salvatore Spinella, Dino Ienco, Paola Giannini

Risultato della ricerca: Capitolo in libro/report/atti di convegnoContributo a conferenzapeer review

Abstract

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.

Lingua originaleInglese
Titolo della pubblicazione ospiteEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 9th European Conference, EvoBIO 2011, Proceedings
Pagine77-88
Numero di pagine12
DOI
Stato di pubblicazionePubblicato - 2011
Evento9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011 - Torino, Italy
Durata: 27 apr 201129 apr 2011

Serie di pubblicazioni

NomeLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6623 LNCS
ISSN (stampa)0302-9743
ISSN (elettronico)1611-3349

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???event.eventtypes.event.conference???9th European Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics, EvoBIO 2011
Paese/TerritorioItaly
CittàTorino
Periodo27/04/1129/04/11

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