Gene Ontology-Based Protein Function Prediction by Using Sequence Composition Information

Abstract

The prediction of protein function is a difficult and important problem in computational biology. In this study, an efficient method is presented to predict protein function with sequence composition information. Four kinds of basic building blocks of protein sequences are investigated, including N-grams, binary profiles, PFAM domains and InterPro domains. The protein sequences are mapped into high-dimensional vectors by using the occurrence frequencies of each kind of building blocks. The resulting vectors are then taken as input to support vector machine to predict their function based on gene ontology. Experiments are conducted over the subset of GOA database. The experimental results show that the protein function can be predicted from primary sequence information. The method based on InterPro domains outperforms the other building blocks, and gets an overall accuracy of 0.87 and ROC score is 0.93. We also demonstrate that the use of feature extraction algorithms such as latent semantic analysis and nonnegative matrix factorization, can efficiently remove noise and improve the prediction efficiency without significantly degrading the performance. The results obtained here are helpful for the prediction of protein function by using only sequence information.

Sequence and structure based models of HIV-1 protease and reverse transcriptase drug resistance

“…For the structure-based models, mutant PR or RT proteins were represented as feature vectors whose input attributes, obtained via an in silico mutagenesis technique relying on a four-body statistical potential energy function, quantified environmental perturbations at positions in their respective targets upon mutation (Figure 4). Similarly, we developed sequence-based models by generating mutant attributes through two applications of n-grams, a technique previously used by other groups in a variety of studies on proteins [32-36] though not in this particular realm. Our models display performance measures that are generally competitive with those described by Rhee et al [24] in their seminal systematic study that utilizes a sequence-based approach.…”