Prediction of protein subcellular localization based on variable-length motifs detection and dissimilarity based classification

Understanding molecular mechanisms of disease through spatial proteomics

Mammalian cells are organized into different compartments that separate and facilitate physiological processes by providing specialized local environments and allowing different, otherwise incompatible biological processes to be carried out simultaneously. Proteins are targeted to these subcellular locations where they fulfill specialized, compartment-specific functions. Spatial proteomics aims to localize and quantify proteins within subcellular structures.

Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins

Predicting the localization of a protein has become a useful practice for inferring its function. Most of the reported methods to predict subcellular localizations in Gram-negative bacterial proteins make use of standard protein representations that generally do not take into account the distribution of the amino acids and the structural information of the proteins. Here, we propose a protein representation based on the structural information contained in the pairwise statistical contact potentials. The wavelet transform decodes the information contained in the primary structure of the proteins, allowing the identification of patterns along the proteins, which are used to characterize the subcellular localizations. Then, a support vector machine classifier is trained to categorize them. Cellular compartments like periplasm and extracellular medium are difficult to predict, having a high false negative rate. The wavelet-based method achieves an overall high performance while maintaining a low false negative rate, particularly, on "periplasm" and "extracellular medium". Our results suggest the proposed protein characterization is a useful alternative to representing and predicting protein sequences over the classical and cutting edge protein depictions.

Contact potentials via wavelet transform for prediction of subcellular localizations in gram negative bacterial proteins

Predicting the localization of a protein has become a useful practice for inferring its function. Most of the reported methods to predict subcellular localizations in Gram-negative bacterial proteins have shown a low false positive rate. However, some subcellular compartmens like "periplasm" and "extracellular medium" are difficult to predict and remain high false negative rates. In this paper, a method based on representation from statistical contact potentials and wavelet transform is presented. The wavelet-based method achieves an overall high performance holding low false and negative rates particularly on periplasm and extracellular medium. Results suggest the contact potentials as an useful alternative to characterize protein sequences.

Interrogating noise in protein sequences from the perspective of protein-protein interactions prediction

The past decades witnessed extensive efforts to study the relationship among proteins. Particularly, sequence-based protein-protein interactions (PPIs) prediction is fundamentally important in speeding up the process of mapping interactomes of organisms. High-throughput experimental methodologies make many model organism's PPIs known, which allows us to apply machine learning methods to learn understandable rules from the available PPIs. Under the machine learning framework, the composition vectors are usually applied to encode proteins as real-value vectors. However, the composition vector value might be highly correlated to the distribution of amino acids, i.e., amino acids which are frequently observed in nature tend to have a large value of composition vectors. Thus formulation to estimate the noise induced by the background distribution of amino acids may be needed during representations. Here, we introduce two kinds of denoising composition vectors, which were successfully used in construction of phylogenetic trees, to eliminate the noise. When validating these two denoising composition vectors on Escherichia coli (E. coli), Saccharomyces cerevisiae (S. cerevisiae) and human PPIs datasets, surprisingly, the predictive performance is not improved, and even worse than non-denoised prediction. These results suggest that the noise in phylogenetic tree construction may be valuable information in PPIs prediction.