BackgroundOver 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.DescriptionWe 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.ConclusionToxinPred 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/).

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/).C ancer with leading cause of deaths remains the matter of health concern for both developed and developing countries 1 . Despite the advances in cancer treatments, mortality rate due to this deadly disease is still very high 1 . Owing to the development of resistance by cancer cells towards current anti-cancer chemotherapeutic drugs, there is an urgent need to add new weapons in the anti-cancer drug arsenal to fight with this deadly disease. In the last decade, small peptides having anticancer properties have emerged as a potential alternative approach for cancer therapy 2 . Peptide-based therapy has numerous advantages over small molecules that involve high specificity, low production cost, high tumor penetration, ease of synthesis and modification etc 3 .Anticancer peptides (ACPs) are small (5-30 amino acids) peptides, often derived from antimicrobial peptides (AMPs) and are cationic in nature 4 . Previous studies have demonstrated that many cationic AMPs, which are toxic to bacteria but not to normal cells, show a broad spectrum cytotoxicity against various cancer cells 5 . Although ACP is a rapidly emerging field, their mechanism of action remains elusive. However, few studies have suggested that there are few differences between the cell membranes of cancer and normal cells and selective killing of cancer cells by certain ACPs could be due to these differences 4,5 . In this context, electrostatic interactions between cationic amino acids of ACPs and anionic components of cancer cell membranes are suggested to be one of the major contributing factors in the selective killing of cancer cells by ACPs 4 . Also, high membrane fluidity and high cell-surface area 6,7 of cancer cells compared to untransformed cells lead to enhance the lytic activity of ACPs and binding of the increased number of ACPs, respectively. In addition, few ACPs induce apoptosis (program cell death) by disrupting mitochondrial membrane when d...

BackgroundCell 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.MethodsIn 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.ResultsIn 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.ConclusionThe 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/.

In response to T cell–dependent antigens, mature B cells are stimulated to form germinal centers (GCs), the sites of B cell affinity maturation and the cell of origin (COO) of most B cell lymphomas. To explore the dynamics of GC B cell development beyond the known dark zone and light zone compartments, we performed single-cell (sc) transcriptomic analysis on human GC B cells and identified multiple functionally linked subpopulations, including the distinct precursors of memory B cells and plasma cells. The gene expression signatures associated with these GC subpopulations were effective in providing a sc-COO for ∼80% of diffuse large B cell lymphomas (DLBCLs) and identified novel prognostic subgroups of DLBCL.