7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning

      1 , 1 , 2 , 3
      Bioinformatics
      Oxford University Press (OUP)

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation

          Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences.

          Results

          In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides.

          Availability and implementation

          The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite.

          Supplementary information

          Supplementary data are available at Bioinformatics online.

          Related collections

          Most cited references39

          • Record: found
          • Abstract: not found
          • Article: not found

          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            pROC: an open-source package for R and S+ to analyze and compare ROC curves

            Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Peptide therapeutics: current status and future directions.

              Peptides are recognized for being highly selective and efficacious and, at the same time, relatively safe and well tolerated. Consequently, there is an increased interest in peptides in pharmaceutical research and development (R&D), and approximately 140 peptide therapeutics are currently being evaluated in clinical trials. Given that the low-hanging fruits in the form of obvious peptide targets have already been picked, it has now become necessary to explore new routes beyond traditional peptide design. Examples of such approaches are multifunctional and cell penetrating peptides, as well as peptide drug conjugates. Here, we discuss the current status, strengths, and weaknesses of peptides as medicines and the emerging new opportunities in peptide drug design and development.
                Bookmark

                Author and article information

                Contributors
                Journal
                Bioinformatics
                Oxford University Press (OUP)
                1367-4803
                1460-2059
                November 01 2019
                November 01 2019
                April 17 2019
                November 01 2019
                November 01 2019
                April 17 2019
                : 35
                : 21
                : 4272-4280
                Affiliations
                [1 ]School of Computer Science and Technology, Tianjin University, Tianjin, China
                [2 ]School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China
                [3 ]Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
                Article
                10.1093/bioinformatics/btz246
                30994882
                16734362-2fa0-4e3d-9721-97f4899580de
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

                History

                Comments

                Comment on this article