Improving model construction of profile HMMs for remote homology detection through structural alignment

• Tipo: Artigo em Revista Científica Internacional
• Data: 2007

Autores

Juliana S Bernardes (Autor) Outra; Alberto Mr Davila (Autor) Outra; Vitor S Costa (Autor) FCUP; Gerson Zaverucha (Autor) Outra

Revista

Título: BMC Bioinformatics

Vol. 8

Página Final: 435

ISSN: 1471-2105

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 19 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

Outras Informações

ID Authenticus: P-004-6AV

DOI: 10.1186/1471-2105-8-435

Abstract (EN): Background: Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the Twilight Zone, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance. Results: We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated through ROC curves and paired two tailed t-test. Conclusion: We observed that pHMMs derived from structural alignments performed significantly better than pHMMs derived from sequence alignment in low-identity regions, mainly below 20%. We believe this is because structural alignment tools are better at focusing on the important patterns that are more often conserved through evolution, resulting in higher quality pHMMs. On the other hand, sensitivity of these tools is still quite low for these low-identity regions. Our results suggest a number of possible directions for improvements in this area.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12