Affiliations: Department of Bioinformatics & Biotechnology,
Jaypee University of Information Technology, Waknaghat, Solan, Himachal
Pradesh, India, 173215. Tel.: +91 9971241380; E-mail: kunal.jaiswal@yahoo.com
Abstract: Ubiquitin functions to regulate protein turnover in a cell by
closely regulating the degradation of specific proteins. Such a regulatory role
is very important, and thus I have analyzed the proteins that are
ubiquitin-like, using an artificial neural network, support vector machines and
a hidden Markov model (HMM). The methods were trained and tested on a set of
373 ubiquitin proteins and 373 non-ubiquitin proteins, obtained from Entrez
protein database. The artificial neural network and support vector machine are
trained and tested using both the physicochemical properties and PSSM matrices
generated from PSI-BLAST, while in the HMM based method direct sequences are
used for training-testing procedures. Further, the performance measures of the
methods are calculated for test sequences, i.e. accuracy, specificity,
sensitivity and Matthew's correlation coefficients of the methods are
calculated. The highest accuracy of 90.2%, specificity of 87.04% and
sensitivity of 94.08% was achieved using the support vector machine model
with PSSM matrices. While accuracies of 86.82%, 83.37%, 80.18% and
72.11% were obtained for the support vector machine with physicochemical
properties, neural network with PSSM matrices, neural networks with
physicochemical properties, and hidden Markov model, respectively. As the
accuracy for SVM model is better both using physicochemical properties and the
PSSM matrices, it is concluded that kernel methods such as SVM outperforms
neural networks and hidden Markov models.
