Automatic identification of highly conserved family regions and relationships in genome wide datasets including remote protein sequences

• Published in: PloS one Vol. 8; no. 9; p. e75458
• Main Authors: Dogan, Tunca, Karaçali, Bilge
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.09.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Alignment; Amino acid sequence; Amino acids; Automation; Bioengineering; Bioinformatics; Cluster Analysis; Clustering; Collection; Computational Biology - methods; Conservation; Conserved Sequence; Databases, Protein; Datasets; Evolution, Molecular; Gene sequencing; Genomes; Genomics; Genomics - methods; Humans; Informatics; Methods; Nucleotide sequence; Ontology; Protein families; Protein Interaction Domains and Motifs; Proteins; Proteins - chemistry; Proteins - genetics; R&D; Reproducibility of Results; Research & development; ROC Curve; Segments; Statistical methods; Structure-function relationships; Turkey
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0075458

Identifying shared sequence segments along amino acid sequences generally requires a collection of closely related proteins, most often curated manually from the sequence datasets to suit the purpose at hand. Currently developed statistical methods are strained, however, when the collection contains remote sequences with poor alignment to the rest, or sequences containing multiple domains. In this paper, we propose a completely unsupervised and automated method to identify the shared sequence segments observed in a diverse collection of protein sequences including those present in a smaller fraction of the sequences in the collection, using a combination of sequence alignment, residue conservation scoring and graph-theoretical approaches. Since shared sequence fragments often imply conserved functional or structural attributes, the method produces a table of associations between the sequences and the identified conserved regions that can reveal previously unknown protein families as well as new members to existing ones. We evaluated the biological relevance of the method by clustering the proteins in gold standard datasets and assessing the clustering performance in comparison with previous methods from the literature. We have then applied the proposed method to a genome wide dataset of 17793 human proteins and generated a global association map to each of the 4753 identified conserved regions. Investigations on the major conserved regions revealed that they corresponded strongly to annotated structural domains. This suggests that the method can be useful in predicting novel domains on protein sequences.