Signal-BNF: a Bayesian network fusing approach to predict signal peptides

Abstract

A signal peptide is a short peptide chain that directs the transport of a protein and has become the crucial vehicle in finding new drugs or reprogramming cells for gene therapy. As the avalanche of new protein sequences generated in the postgenomic era, the challenge of identifying new signal sequences has become even more urgent and critical in biomedical engineering. In this paper, we propose a novel predictor called Signal-BNF to predict the N-terminal signal peptide as well as its cleavage site based on Bayesian reasoning network. Signal-BNF is formed by fusing the results of different Bayesian classifiers which used different feature datasets as its input through weighted voting system. Experiment results show that Signal-BNF is superior to the popular online predictors such as Signal-3L and PrediSi. Signal-BNF is featured by high prediction accuracy that may serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the zip code protein-sorting system in cells.

Figure 1

A schematic drawing shows the signal sequence of a protein and how it is cleaved by the signal peptidase. An amino acid in the signal part is depicted as a white circle with a black number to indicate its sequential position, while in the mature protein depicted as a black circle with a white number. The signal sequence contains Ls residues and the mature protein Lm residues. The cleavage site is at the position (−1, +1), that is, between the last residue of the signal peptide sequence and the first residue of the mature protein.

Figure 2

A histogram to show the distribution of signal peptides with their length in the 4,184 secretory proteins constructed in this study. Of the 4,184 proteins, 894 humans, 338 plants, 1,435 animals, 635 eukaryotic, 269 Gram-positives, and 613 gram-negatives.

Figure 3

Illustration to show the sequence segments highlighted by sliding the scaled window [−ξ₁, +ξ₂] along a protein sequence. During the sliding process, the scales on the window are aligned with different amino acids so as to define different peptide segments. When the scale −1 is aligned with the tail residue of the signal sequence and scale +1 aligned with the head residue of the mature protein as shown in (a), the peptide segment is seen within the window regarded as secretion cleavable. Peptide segments seen within the window for all the other cases, such as those shown in ((b) and (c)), are regarded as nonsecretion cleavable.

Figure 4

Structures of multiple classifier systems. (a) Cascading, (b) parallel, and (c) hierarchy.

Figure 5

Integrated classification model used in this paper.