Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model
Φόρτωση...
Ημερομηνία
Συγγραφείς
Lampros, C.
Papaloukas, C.
Exarchos, K.
Fotiadis, D. I.
Tsalikakis, D.
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Elsevier
Περίληψη
Τύπος
Είδος δημοσίευσης σε συνέδριο
Είδος περιοδικού
peer reviewed
Είδος εκπαιδευτικού υλικού
Όνομα συνεδρίου
Όνομα περιοδικού
Comput Biol Med
Όνομα βιβλίου
Σειρά βιβλίου
Έκδοση βιβλίου
Συμπληρωματικός/δευτερεύων τίτλος
Περιγραφή
Fold recognition is a challenging field strongly associated with protein function determination, which is crucial for biologists and the pharmaceutical industry. Hidden Markov models (HMMs) have been widely used for this purpose. In this paper we demonstrate how the fold recognition performance of a recently introduced HMM with a reduced state-space topology can be improved. Our method employs an efficient architecture and a low complexity training algorithm based on likelihood maximization. The fold recognition performance of the model is further improved in two steps. In the first step we use a smaller model architecture based on the {E,H,L} alphabet instead of the DSSP secondary structure alphabet. In the second step secondary structure information (predicted or true) is additionally used in scoring the test set sequences. The Protein Data Bank and the annotation of the SCOP database are used for the training and evaluation of the proposed methodology. The results show that the fold recognition accuracy is substantially improved in both steps. Specifically, it is increased by 2.9% in the first step to 22%. In the second step it further increases and reaches up to 30% when predicted secondary structure information is additionally used and it increases even more and reaches up to 34.7% when we use the true secondary structure. The major advantage of the proposed improvements is that the fold recognition performance is substantially increased while the size of the model and the computational complexity of scoring are decreased. (C) 2009 Elsevier Ltd. All rights reserved.
Περιγραφή
Λέξεις-κλειδιά
fold recognition, hidden markov models, protein classification, secondary structure prediction, secondary structure alphabet, support vector machines, sequence, superfamily
Θεματική κατηγορία
Παραπομπή
Σύνδεσμος
<Go to ISI>://000270375400007
http://ac.els-cdn.com/S0010482509001310/1-s2.0-S0010482509001310-main.pdf?_tid=c008fe9f6eadc8eefbfce41231e692f4&acdnat=1339758257_f9d041a19426f95229a7ccecf246cc86
http://ac.els-cdn.com/S0010482509001310/1-s2.0-S0010482509001310-main.pdf?_tid=c008fe9f6eadc8eefbfce41231e692f4&acdnat=1339758257_f9d041a19426f95229a7ccecf246cc86
Γλώσσα
en
Εκδίδον τμήμα/τομέας
Όνομα επιβλέποντος
Εξεταστική επιτροπή
Γενική Περιγραφή / Σχόλια
Ίδρυμα και Σχολή/Τμήμα του υποβάλλοντος
Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών