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/).

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/.

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...

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.

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/.

Adenomyoepithelioma of the breast is a rare tumor characterized by epithelial−myoepithelial differentiation, whose genetic underpinning is largely unknown. Here we show through whole-exome and targeted massively parallel sequencing analysis that whilst estrogen receptor (ER)-positive adenomyoepitheliomas display PIK3CA or AKT1 activating mutations, ER-negative adenomyoepitheliomas harbor highly recurrent codon Q61 HRAS hotspot mutations, which co-occur with PIK3CA or PIK3R1 mutations. In two- and three-dimensional cell culture models, forced expression of HRASQ61R in non-malignant ER-negative breast epithelial cells with or without a PIK3CAH1047R somatic knock-in results in transformation and the acquisition of the cardinal features of adenomyoepitheliomas, including the expression of myoepithelial markers, a reduction in E-cadherin expression, and an increase in AKT signaling. Our results demonstrate that adenomyoepitheliomas are genetically heterogeneous, and qualify mutations in HRAS, a gene whose mutations are vanishingly rare in common-type breast cancers, as likely drivers of ER-negative adenomyoepitheliomas.

Granular cell tumors (GCTs) are rare tumors that can arise in multiple anatomical locations, and are characterized by abundant intracytoplasmic granules. The genetic drivers of GCTs are currently unknown. Here, we apply whole-exome sequencing and targeted sequencing analysis to reveal mutually exclusive, clonal, inactivating somatic mutations in the endosomal pH regulators ATP6AP1 or ATP6AP2 in 72% of GCTs. Silencing of these genes in vitro results in impaired vesicle acidification, redistribution of endosomal compartments, and accumulation of intracytoplasmic granules, recapitulating the cardinal phenotypic characteristics of GCTs and providing a novel genotypic–phenotypic correlation. In addition, depletion of ATP6AP1 or ATP6AP2 results in the acquisition of oncogenic properties. Our results demonstrate that inactivating mutations of ATP6AP1 and ATP6AP2 are likely oncogenic drivers of GCTs and underpin the genesis of the intracytoplasmic granules that characterize them, providing a genetic link between endosomal pH regulation and tumorigenesis.

Background:Elevated APOBEC3B expression in tumours correlates with a kataegic pattern of localised hypermutation. We assessed the cellular phenotypes associated with high-level APOBEC3B expression and the influence of p53 status on these phenotypes using an isogenic system.Methods:We used RNA interference of p53 in cells with inducible APOBEC3B and assessed DNA damage response (DDR) biomarkers. The mutational effects of APOBEC3B were assessed using whole-genome sequencing. In vitro small-molecule inhibitor sensitivity profiling was used to identify candidate therapeutic vulnerabilities.Results:Although APOBEC3B expression increased the incorporation of genomic uracil, invoked DDR biomarkers and caused cell cycle arrest, inactivation of p53 circumvented APOBEC3B-induced cell cycle arrest without reversing the increase in genomic uracil or DDR biomarkers. The continued expression of APOBEC3B in p53-defective cells not only caused a kataegic mutational signature but also caused hypersensitivity to small-molecule DDR inhibitors (ATR, CHEK1, CHEK2, PARP, WEE1 inhibitors) as well as cisplatin/ATR inhibitor and ATR/PARP inhibitor combinations.Conclusions:Although loss of p53 might allow tumour cells to tolerate elevated APOBEC3B expression, continued expression of this enzyme might impart a number of therapeutic vulnerabilities upon tumour cells.