In silico approach for predicting toxicity of peptides and proteins.
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.


METHODS
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.


CONCLUSIONS
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/).
Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
Sudheer
S
Gupta
Pallavi
P
Kapoor
Kumardeep
K
Chaudhary
Ankur
A
Gautam
Rahul
R
Kumar
Gajendra P S
GP
Raghava
D020816:Amino Acid Motifs; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry* / Q000633:toxicity; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein; D012984:Software
D010455:Peptides