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

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 physicochemical 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,...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Juliana S. Bernardes, Alessandra Carbone, Gerson Zaverucha
• Keywords: Artificial Intelligence, BMCBI 2011, Frequent Patterns, Homology Detection, Proteins