Dataset Information

A consistency-based feature selection method allied with linear SVMs for HIV-1 protease cleavage site prediction.

ABSTRACT:

Background

Predicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy.

Results

We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations.

Conclusion

Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper.

Software availability

The software is available at http://ozyer.etu.edu.tr/c-fs-svm.rar. The software can be downloaded at esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar; you will find a readme file which explains how to set the software in order to work.

SUBMITTER: Ozturk O

PROVIDER: S-EPMC3751940 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.

Project description:There are many toxic chemicals to contaminate the world and cause harm to human and other organisms. How to quickly discriminate these compounds and characterize their potential molecular mechanism and toxicity is essential. High through put transcriptomics profiles such as microarray have been proven useful to identify biomarkers for different classification and toxicity prediction purposes. Here we aim to investigate how to use microarray to predict chemical contaminants and their possible mechanisms. In this study, we divided 105 compounds plus vehicle control into 14 compound classes. On the basis of gene expression profiles of in vitro primary cultured hepatocytes, we comprehensively compared various normalization, feature selection and classification algorithms for the classification of these 14 class compounds. We found that normalization had little effect on the averaged classification accuracy. Two support vector machine methods LibSVM and SMO had better classification performance. When feature sizes were smaller, LibSVM outperformed other classification methods. Simple logistic algorithm also performed well. At the training stage, usually the feature selection method SVM-RFE performed the best, and PCA was the poorest feature selection algorithm. But overall, SVM-RFE had the highest overfitting rate when an independent dataset used for a prediction in this case. Therefore, we developed a new feature selection algorithm called gradient method which had a pretty high training classification as well as prediction accuracy with the lowest over-fitting rate. Through the analysis of biomarkers that distinguished 14 class compounds, we found a goup of genes that mainly invovled in cell cylce were significanly downregulated by the metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and PXR mediators. For in vitro experiment, primary cultured rat hepatocytes were treated one of 105 compounds with relative controls. At least three biological replicates were used for each unique condition. In total 531 arrays were used.

Project description:Mutations in amyloid ? precursor protein (APP) gene alter APP processing, either causing familial Alzheimer's disease (AD) or protecting against dementia. Under normal conditions, ?-site APP cleaving enzyme 1 (BACE1) cleaves APP at minor Asp1 site to generate C99 for amyloid ? protein (A?) production, and predominantly at major Glu11 site to generate C89, resulting in truncated A? production. We discovered that A673V mutation, the only recessive AD-associated APP mutation, shifted the preferential ?-cleavage site of BACE1 in APP from the Glu11 site to the Asp1 site both in male and female transgenic mice in vivo and in cell lines and primary neuronal culture derived from timed pregnant rats in vitro, resulting in a much higher C99 level and C99/C89 ratio. All other mutations at this site, including the protective Icelandic A673T mutation, reduced C99 generation, and decreased the C99/C89 ratio. Furthermore, A673V mutation caused stronger dimerization between mutant and wild-type APP, enhanced the lysosomal degradation of the mutant APP, and inhibited ?-secretase cleavage of the mutant C99 to generate A?, leading to recessively inherited AD. The results demonstrate that APP673 regulates APP processing and the BACE1 cleavage site selection is critical for amyloidogenesis in AD pathogenesis, and implicate a pharmaceutical potential for targeting the APP673 site for AD drug development.SIGNIFICANCE STATEMENT ?-site APP cleaving enzyme 1 (BACE1) is essential for amyloid ? protein production. We discovered that A673V mutation shifted the BACE1 cleavage site from the Glu11 to the Asp1 site, resulting in much higher C99 level and C99/C89 ratio. All other mutations at this site of amyloid ? precursor protein (APP) reduced C99 generation and decreased the C99/C89 ratio. Furthermore, A673V mutation resulted in stronger dimerization between mutant and wild-type APP, enhanced the lysosomal degradation of the mutant APP, and inhibited ?-secretase cleavage of the mutant C99 to generate amyloid ? protein, leading to recessively inherited Alzheimer's disease (AD). The results demonstrate that APP673 regulates APP processing, and the BACE1 cleavage site selection is critical for amyloidogenesis in AD pathogenesis, and implicate a pharmaceutical potential for targeting the APP673 site for AD drug development.