Detail In Silico Approach for Predicting Toxicity of Peptides and Proteins (from Plos One 2013, 8 (9)) Bibliografi Author: Gupta, Sudheer ; Kapoor, Pallavi ; Chaudhary, Kumardeep ; Gautam, Ankur ; Kumar, Rahul ; Open Source Drug Discovery Consortium ; Raghava, Gajendra P. S. Topik: Peptides; Proteins; Toxicity; Seminar - Thesis lit Bahasa: (EN )     Penerbit: open access     Tahun Terbit: 2013     Jenis: Article - diterbitkan di jurnal ilmiah internasional Fulltext: journal.pone.0073957.PDF (3.72MB; 0 download) [Informasi yang berkaitan dengan koleksi ini di internet] Abstract Background: 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. Description: We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins. Conclusion: ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/). [seminar - thesis lit] Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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