Peptide toxicity prediction.

Last decade has witnessed the revival of interest in peptides as potential therapeutics candidates. However, one of the bottlenecks in the success of therapeutic peptides in clinics is their toxicity towards eukaryotic cells. Therefore, considerable efforts have been made over the years both in wet and dry lab to overcome this limitation. With the advances in peptide synthesis, now it is possible to fine-tune the physicochemical properties of peptides by incorporating several chemical modifications and thus to optimize the peptide functionality in order to minimize the toxicity without compromising their therapeutic activity. Also various in silico tools for peptide toxicity prediction and peptide designing have been developed, which facilitates designing of therapeutic peptides with desired toxicity. In this chapter, we have discussed both wet lab and dry lab approaches used to optimize peptide toxicity. More emphasis has been given to describe the in silico method, ToxinPred, to predict the toxicity of peptide and about how to design a peptide or protein with desired toxicity by mutating minimum number of amino acids.

Identification and characterization of novel protein-derived arginine-rich cell-penetrating peptides.

Cell-penetrating peptides (CPPs) have proven their potential as an efficient delivery system due to their intrinsic ability to traverse biological membranes and transport various cargoes into the cells. In the present study, we have identified novel natural protein-derived CPPs using an integrated (in silico and experimental) approach. First, using bioinformatics approach, arginine-rich peptide segments were extracted from SwissProt proteins and their cell-penetrating properties were predicted. Finally, eight peptides were selected and their internalization was validated using experimental techniques. Laser scanning confocal microscopy and flow cytometry confirmed that seven out of eight peptides were internalized into live cells with varying efficiencies without significant cytotoxicity. Three peptides have shown higher internalization efficacy than TAT peptide, the most widely used CPP. Among these three peptides, one peptide (P8), derived from voltage-dependent L-type calcium channel subunit alpha-1D, was able to accumulate inside in a variety of cell types very efficiently through a rapid dose-dependent process. Further, experiments involving inhibition with various endocytic inhibitors along with co-localization studies indicate that the uptake mechanism of P8 is macropinocytosis, a fluid-phase endocytosis process. In addition, competitive inhibition with heparin revealed the involvement of cell-surface proteoglycans in P8 uptake. In summary, the present study provides evidence that an integrated in silico and experimental approach is an effective strategy for the identification of novel CPPs and CPPs identified in the present study have promising perspectives for future drug delivery applications.

In silico models for designing and discovering novel anticancer peptides.

Use of therapeutic peptides in cancer therapy has been receiving considerable attention in the recent years. Present study describes the development of computational models for predicting and discovering novel anticancer peptides. Preliminary analysis revealed that Cys, Gly, Ile, Lys, and Trp are dominated at various positions in anticancer peptides. Support vector machine models were developed using amino acid composition and binary profiles as input features on main dataset that contains experimentally validated anticancer peptides and random peptides derived from SwissProt database. In addition, models were developed on alternate dataset that contains antimicrobial peptides instead of random peptides. Binary profiles-based model achieved maximum accuracy 91.44% with MCC 0.83. We have developed a webserver, which would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides (http://crdd.osdd.net/raghava/anticp/).

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. 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/).

Computational approach for designing tumor homing peptides.

Tumor homing peptides are small peptides that home specifically to tumor and tumor associated microenvironment i.e. tumor vasculature, after systemic delivery. Keeping in mind the huge therapeutic importance of these peptides, we have made an attempt to analyze and predict tumor homing peptides. It was observed that certain types of residues are preferred in tumor homing peptides. Therefore, we developed support vector machine based models for predicting tumor homing peptides using amino acid composition and binary profiles of peptides. Amino acid composition, dipeptide composition and binary profile-based models achieved a maximum accuracy of 86.56%, 82.03%, and 84.19% respectively. These methods have been implemented in a user-friendly web server, TumorHPD. We anticipate that this method will be helpful to design novel tumor homing peptides. TumorHPD web server is freely accessible at http://crdd.osdd.net/raghava/tumorhpd/.

In silico approaches for designing highly effective cell penetrating peptides.

BACKGROUND: Cell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis. METHODS: In the present study, support vector machine (SVM)-based models have been developed for predicting and designing highly effective cell penetrating peptides. Various features like amino acid composition, dipeptide composition, binary profile of patterns, and physicochemical properties have been used as input features. The main dataset used in this study consists of 708 peptides. In addition, we have identified various motifs in cell penetrating peptides, and used these motifs for developing a hybrid prediction model. Performance of our method was evaluated on an independent dataset and also compared with that of the existing methods. RESULTS: In cell penetrating peptides, certain residues (e.g. Arg, Lys, Pro, Trp, Leu, and Ala) are preferred at specific locations. Thus, it was possible to discriminate cell-penetrating peptides from non-cell penetrating peptides based on amino acid composition. All models were evaluated using five-fold cross-validation technique. We have achieved a maximum accuracy of 97.40% using the hybrid model that combines motif information and binary profile of the peptides. On independent dataset, we achieved maximum accuracy of 81.31% with MCC of 0.63. CONCLUSION: The present study demonstrates that features like amino acid composition, binary profile of patterns and motifs, can be used to train an SVM classifier that can predict cell penetrating peptides with higher accuracy. The hybrid model described in this study achieved more accuracy than the previous methods and thus may complement the existing methods. Based on the above study, a user-friendly web server CellPPD has been developed to help the biologists, where a user can predict and design CPPs with much ease. CellPPD web server is freely accessible at http://crdd.osdd.net/raghava/cellppd/.

CancerDR: cancer drug resistance database.

Cancer therapies are limited by the development of drug resistance, and mutations in drug targets is one of the main reasons for developing acquired resistance. The adequate knowledge of these mutations in drug targets would help to design effective personalized therapies. Keeping this in mind, we have developed a database "CancerDR", which provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance. CancerDR allows user to identify promiscuous drug molecules that can kill wide range of cancer cells. CancerDR is freely accessible at http://crdd.osdd.net/raghava/cancerdr/

TumorHoPe: a database of tumor homing peptides.

BACKGROUND: Cancer is responsible for millions of immature deaths every year and is an economical burden on developing countries. One of the major challenges in the present era is to design drugs that can specifically target tumor cells not normal cells. In this context, tumor homing peptides have drawn much attention. These peptides are playing a vital role in delivering drugs in tumor tissues with high specificity. In order to provide service to scientific community, we have developed a database of tumor homing peptides called TumorHoPe. DESCRIPTION: TumorHoPe is a manually curated database of experimentally validated tumor homing peptides that specifically recognize tumor cells and tumor associated microenvironment, i.e., angiogenesis. These peptides were collected and compiled from published papers, patents and databases. Current release of TumorHoPe contains 744 peptides. Each entry provides comprehensive information of a peptide that includes its sequence, target tumor, target cell, techniques of identification, peptide receptor, etc. In addition, we have derived various types of information from these peptide sequences that include secondary/tertiary structure, amino acid composition, and physicochemical properties of peptides. Peptides in this database have been found to target different types of tumors that include breast, lung, prostate, melanoma, colon, etc. These peptides have some common motifs including RGD (Arg-Gly-Asp) and NGR (Asn-Gly-Arg) motifs, which specifically recognize tumor angiogenic markers. TumorHoPe has been integrated with many web-based tools like simple/complex search, database browsing and peptide mapping. These tools allow a user to search tumor homing peptides based on their amino acid composition, charge, polarity, hydrophobicity, etc. CONCLUSION: TumorHoPe is a unique database of its kind, which provides comprehensive information about experimentally validated tumor homing peptides and their target cells. This database will be very useful in designing peptide-based drugs and drug-delivery system. It is freely available at http://crdd.osdd.net/raghava/tumorhope/.

CPPsite: a curated database of cell penetrating peptides.

Delivering drug molecules into the cell is one of the major challenges in the process of drug development. In past, cell penetrating peptides have been successfully used for delivering a wide variety of therapeutic molecules into various types of cells for the treatment of multiple diseases. These peptides have unique ability to gain access to the interior of almost any type of cell. Due to the huge therapeutic applications of CPPs, we have built a comprehensive database 'CPPsite', of cell penetrating peptides, where information is compiled from the literature and patents. CPPsite is a manually curated database of experimentally validated 843 CPPs. Each entry provides information of a peptide that includes ID, PubMed ID, peptide name, peptide sequence, chirality, origin, nature of peptide, sub-cellular localization, uptake efficiency, uptake mechanism, hydrophobicity, amino acid frequency and composition, etc. A wide range of user-friendly tools have been incorporated in this database like searching, browsing, analyzing, mapping tools. In addition, we have derived various types of information from these peptide sequences that include secondary/tertiary structure, amino acid composition and physicochemical properties of peptides. This database will be very useful for developing models for predicting effective cell penetrating peptides. Database URL: http://crdd.osdd.net/raghava/cppsite/.