Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
PLoS One. 2013 Sep 13;8(9):e73957. doi: 10.1371/journal.pone.0073957. eCollection 2013.
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.
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.
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/).
在过去的几十年中，科学研究一直致力于开发基于肽/蛋白质的疗法来治疗各种疾病。与小分子相比，肽具有高度特异性、高穿透性、易于制造等优势，因此已成为治疗许多疾病的有前途的治疗分子。然而，基于肽/蛋白质的治疗方法的一个瓶颈是它们的毒性。因此，在本研究中，我们开发了用于预测肽和蛋白质毒性的计算模型。
我们从各种数据库中获得了含有 35 个或更少残基的毒性肽，用于开发预测模型。非毒性或随机肽从 SwissProt 和 TrEMBL 中获得。结果表明，某些残基（如 Cys、His、Asn 和 Pro）在毒性肽中丰富且偏好出现在各种位置。我们使用肽的各种性质基于机器学习技术和定量矩阵开发了用于预测肽毒性的模型。基于二肽的模型在准确性方面的性能为 94.50%，MCC 为 0.88。此外，从毒性肽中提取了各种基序，并将这些信息与基于二肽的模型结合起来，开发了一个混合模型。为了评估基于二肽组成的最佳模型的过优化，我们在独立数据集上评估了其性能，准确率约为 90%。基于上述研究，开发了一个名为 ToxinPred 的网络服务器，它将有助于预测（i）肽的毒性或非毒性，（ii）增加或减少其毒性所需的肽的最小突变，以及（iii）蛋白质中的毒性区域。
ToxinPred 是一种独特的同类计算方法，将有助于预测肽/蛋白质的毒性。此外，它将有助于设计毒性最小的肽和发现蛋白质中的毒性区域。我们希望 ToxinPred 的开发将为基于肽/蛋白质的药物发现提供动力（http://crdd.osdd.net/raghava/toxinpred/）。
