Abstract
BackgroundProtein remote homology detection and fold recognition are central problems in bioinformatics. Currently, discriminative methods based on support vector machine (SVM) are the most effective and accurate methods for solving these problems. A key step to improve the performance of the SVM-based methods is to find a suitable representation of protein sequences.ResultsIn this paper, a novel building block of proteins called Top-n-grams is presented, which contains the evolutionary information extracted from the protein sequence frequency profiles. The protein sequence frequency profiles are calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into Top-n-grams. The protein sequences are transformed into fixed-dimension feature vectors by the occurrence times of each Top-n-gram. The training vectors are evaluated by SVM to train classifiers which are then used to classify the test protein sequences. We demonstrate that the prediction performance of remote homology detection and fold recognition can be improved by combining Top-n-grams and latent semantic analysis (LSA), which is an efficient feature extraction technique from natural language processing. When tested on superfamily and fold benchmarks, the method combining Top-n-grams and LSA gives significantly better results compared to related methods.ConclusionThe method based on Top-n-grams significantly outperforms the methods based on many other building blocks including N-grams, patterns, motifs and binary profiles. Therefore, Top-n-gram is a good building block of the protein sequences and can be widely used in many tasks of the computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the prediction of protein binding sites.
Highlights
Protein remote homology detection and fold recognition are central problems in bioinformatics
In this study we present a novel building block of proteins called Top-n-grams to use the evolutionary information of the protein sequence frequency profiles and apply this novel building block to remote homology detection and fold recognition
We present a novel representation of protein sequences based on Top-n-grams and apply the latent semantic analysis to improve the prediction performance of both protein remote homology detection and fold recognition
Summary
Protein remote homology detection and fold recognition are central problems in bioinformatics. Some heuristic algorithms, such as BLAST [3] and FASTA [4] trade reduced accuracy for improved efficiency These methods do not perform well for remote homology detection, because the alignment score falls into a twilight zone when the protein sequences similarity is below 35% at the amino acid level [5]. These methods such as profile hidden Markov model (HMM) [7] can be trained iteratively in a semi-supervised manner using both positively labeled and unlabeled samples of a particular family by pulling in close homology and adding them to the positive set [8] The discriminative algorithms such as Support Vector Machines (SVM) [9] provide state-of-theart performance. Another approach is the feature-space-based kernel, which chooses a proper feature space, represents each sequence as a vector in that space and inner product (or a function derived from it) between these vector-space representations is taken as a kernel for the sequences [10]
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