12
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Prediction of Cell-Penetrating Potential of Modified Peptides Containing Natural and Chemically Modified Residues

      research-article

      Read this article at

      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

          Designing drug delivery vehicles using cell-penetrating peptides is a hot area of research in the field of medicine. In the past, number of in silico methods have been developed for predicting cell-penetrating property of peptides containing natural residues. In this study, first time attempt has been made to predict cell-penetrating property of peptides containing natural and modified residues. The dataset used to develop prediction models, include structure and sequence of 732 chemically modified cell-penetrating peptides and an equal number of non-cell penetrating peptides. We analyzed the structure of both class of peptides and observed that positive charge groups, atoms, and residues are preferred in cell-penetrating peptides. In this study, models were developed to predict cell-penetrating peptides from its tertiary structure using a wide range of descriptors (2D, 3D descriptors, and fingerprints). Random Forest model developed by using PaDEL descriptors (combination of 2D, 3D, and fingerprints) achieved maximum accuracy of 95.10%, MCC of 0.90 and AUROC of 0.99 on the main dataset. The performance of model was also evaluated on validation/independent dataset which achieved AUROC of 0.98. In order to assist the scientific community, we have developed a web server “CellPPDMod” for predicting the cell-penetrating property of modified peptides ( http://webs.iiitd.edu.in/raghava/cellppdmod/).

          Related collections

          Most cited references56

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

          Cell-Penetrating Peptides: From Basic Research to Clinics.

          The presence of cell and tissue barriers together with the low biomembrane permeability of various therapeutics often hampers systemic drug distribution; thus, most of the available molecules are of limited therapeutic value. Opportunities to increase medicament concentrations in areas that are difficult to access now exist with the advent of cell-penetrating peptides (CPPs), which can transport into the cell a wide variety of biologically active conjugates (cargoes). Numerous preclinical evaluations with CPP-derived therapeutics have provided promising results in various disease models that, in some cases, prompted clinical trials. The outcome of these investigations has thus opened new perspectives for CPP application in the development of unprecedented human therapies that are well tolerated and directed to intracellular targets.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field.

            Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Copyright © 2012 Wiley Periodicals, Inc.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Cell-penetrating peptides: 20 years later, where do we stand?

              Twenty years ago, the discovery of peptides able to cross cellular membranes launched a novel field in molecular delivery based on these non-invasive vectors, most commonly called cell-penetrating peptides (CPPs) or protein transduction domains (PTDs). These peptides were shown to efficiently transport various biologically active molecules inside living cells, and thus are considered promising devices for medical and biotechnological developments. Moreover, CPPs emerged as potential tools to study the prime mechanisms of cellular entry across the plasma membrane. This review is dedicated to CPP fundamentals, with an emphasis on the molecular requirements and mechanism of their entry into eukaryotic cells. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                12 April 2018
                2018
                : 9
                : 725
                Affiliations
                [1] 1Center for Computational Biology, Indraprastha Institute of Information Technology , Okhla, India
                [2] 2Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector-39A , Chandigarh, India
                [3] 3Cell Biology and Immunology, CSIR-Institute of Microbial Technology, Sector-39A , Chandigarh, India
                Author notes

                Edited by: Noton Kumar Dutta, Johns Hopkins University, United States

                Reviewed by: Tikam Chand Dakal, Hospital Maisonneuve-Rosemont and University of Montreal, Canada; William Farias Porto, Universidade Católica Dom Bosco, Brazil

                *Correspondence: Gajendra P. S. Raghava raghava@ 123456iiitd.ac.in

                This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

                †These authors have contributed equally to this work.

                Article
                10.3389/fmicb.2018.00725
                5906597
                29706944
                aba3f831-4a3d-49d7-9c6b-23dfc6c8e536
                Copyright © 2018 Kumar, Agrawal, Kumar, Bhalla, Usmani, Varshney and Raghava.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 January 2018
                : 28 March 2018
                Page count
                Figures: 4, Tables: 6, Equations: 8, References: 61, Pages: 10, Words: 7118
                Funding
                Funded by: Department of Science and Technology, Ministry of Science and Technology 10.13039/501100001409
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                modified cell-penetrating peptides,machine learning,random forest,svm,in silico method,chemical descriptors,antimicrobial peptide

                Comments

                Comment on this article