In silico approach for predicting toxicity of peptides and proteins.
In silico approach for predicting toxicity of peptides and proteins.
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DOI：
10.1371/journal.pone.0073957
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Raghava GP
中科院分区：
文献类型：
--
作者：
Gupta S;Kapoor P;Chaudhary K;Gautam A;Kumar R;Open Source Drug Discovery Consortium;Raghava GP
在过去的几十年里，科学研究一直专注于开发基于肽/蛋白质的疗法来治疗各种疾病。多肽具有特异性强、穿透性强、易于制备等优点，是一种具有广阔应用前景的治疗药物。然而，基于肽/蛋白质的治疗的瓶颈之一是它们的毒性。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机模型。我们从各种数据库中获得了具有35个或更少残基的有毒肽，用于开发预测模型。从SwissProt和TrEMBL获得无毒或随机肽。观察到某些残基如Cys、His、Asn和Pro在毒性肽中的各个位置是丰富的并且是优选的。我们开发了基于机器学习技术和定量矩阵的模型，利用肽的各种性质来预测肽的毒性。基于二肽的模型在准确度方面的性能为94.50%，MCC为0.88。此外，从毒性肽中提取各种基序，并将这些信息与基于二肽的模型相结合，以开发杂交模型。为了评估基于二肽组成的最佳模型的过度优化，我们评估了其在独立数据集上的性能，并达到了90%左右的准确率。基于上述研究，开发了一个Web服务器ToxinPred，它将有助于预测（i）肽的毒性或非毒性，（ii）肽中增加或降低其毒性的最小突变，以及（iii）蛋白质中的毒性区域。ToxinPred是一种独特的计算机模拟方法，可用于预测肽/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的开发能够为基于肽/蛋白质的药物发现提供动力（http：//crdd.osdd.net/raghava/toxinpred/）。
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/).
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