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A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

TitleA discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.
Publication TypeJournal Article
Year of Publication2011
AuthorsBernardes, JS, Carbone, A, Zaverucha, G
JournalBMC Bioinformatics
Volume12
Pagination83
Date Published2011
ISSN1471-2105
KeywordsAlgorithms, Amino Acid Motifs, Amino Acid Sequence, Computational Biology, Databases, Factual, Logic, Models, Genetic, Molecular Sequence Data, Proteins, Sequence Alignment, Sequence Analysis, Protein, Software
Abstract

BACKGROUND: Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM).

RESULTS: We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function.

CONCLUSIONS: The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

DOI10.1186/1471-2105-12-83
Alternate JournalBMC Bioinformatics
PubMed ID21429187
PubMed Central IDPMC3078102