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      In silico approach for predicting toxicity of peptides and proteins.

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          Abstract

          Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.

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          Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

          Sudipto Saha, G. P. S. Raghava (2006)
          B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/. Proteins 2006. (c) 2006 Wiley-Liss, Inc.
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            Two Sample Logo: a graphical representation of the differences between two sets of sequence alignments.

            Vladimir Vacic, Lilia Iakoucheva, Predrag Radivojac (2006)
            Two Sample Logo is a web-based tool that detects and displays statistically significant differences in position-specific symbol compositions between two sets of multiple sequence alignments. In a typical scenario, two groups of aligned sequences will share a common motif but will differ in their functional annotation. The inclusion of the background alignment provides an appropriate underlying amino acid or nucleotide distribution and addresses intersite symbol correlations. In addition, the difference detection process is sensitive to the sizes of the aligned groups. Two Sample Logo extends WebLogo, a widely-used sequence logo generator. The source code is distributed under the MIT Open Source license agreement and is available for download free of charge.
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              Cell-penetrating peptides: classes, origin, and current landscape.

              Francesca Milletti (2012)
              With more than ten new FDA approvals since 2001, peptides are emerging as an important therapeutic alternative to small molecules. However, unlike small molecules, peptides on the market today are limited to extracellular targets. By contrast, cell-penetrating peptides (CPPs) can target intracellular proteins and also carry other cargoes (e.g. other peptides, small molecules or proteins) into the cell, thus offering great potential as future therapeutics. In this review I present a classification scheme for CPPs based on their physical-chemical properties and origin, and I provide a general framework for understanding and discovering new CPPs. Copyright © 2012 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): PLoS One
                Title: PloS one
                Publisher: Public Library of Science (PLoS)
                ISSN (Electronic): 1932-6203
                ISSN (Print): 1932-6203
                Publication date (Electronic): 2013
                Volume: 8
                Issue: 9
                Affiliations
                [1 ] Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
                Article
                Publisher Item ID: PONE-D-13-21458
                DOI: 10.1371/journal.pone.0073957
                PMC ID: 3772798
                PubMed ID: 24058508
                SO-VID: a8c283fe-ad55-4a00-966c-39490f3f34f7
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