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Prediction of LncRNA-encoded small peptides in glioma and oligomer channel functional analysis using in silico approaches.

ABSTRACT: Glioma is a lethal malignant brain cancer, and many reports have shown that abnormalities in the behavior of water and ion channels play an important role in regulating tumor proliferation, migration, apoptosis, and differentiation. Recently, new studies have suggested that some long noncoding RNAs containing small open reading frames can encode small peptides and form oligomers for water or ion regulation. However, because the peptides are difficult to identify, their functional mechanisms are far from being clearly understood. In this study, we used bioinformatics methods to identify and evaluate lncRNAs, which may encode small transmembrane peptides in gliomas. Combining ab initio homology modeling, molecular dynamics simulations, and free energy calculations, we constructed a predictive model and predicted the oligomer channel activity of peptides by identifying the lncRNA ORFs. We found that one key hub lncRNA, namely, DLEU1, which contains two smORFs (ORF1 and ORF8), encodes small peptides that form pentameric channels. The mechanics of water and ion (Na+ and Cl-) transport through this pentameric channel were simulated. The potential mean force of the H2O molecules along the two ORF-encoded peptide channels indicated that the energy barrier was different between ORF1 and ORF8. The ORF1-encoded peptide pentamer acted as a self-assembled water channel but not as an ion channel, and the ORF8 permeated neither ions nor water. This work provides new methods and theoretical support for further elucidation of the function of lncRNA-encoded small peptides and their role in cancer. Additionally, this study provides a theoretical basis for drug development.

SUBMITTER: Cao Y

PROVIDER: S-EPMC7971536 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Chemotherapy is the main treatment for most early-stage cancers; nevertheless, its efficacy is usually limited by drug resistance, toxicity, and tumor heterogeneity. Cell-penetrating peptides (CPPs) are small peptide sequences that can be used to increase the delivery rate of chemotherapeutic drugs to the tumor site, therefore contributing to overcoming these problems and enhancing the efficacy of chemotherapy. The drug combination is another promising strategy to overcome the aforementioned problems since the combined drugs can synergize through interconnected biological processes and target different pathways simultaneously. Here, we hypothesized that different peptides (P1-P4) could be used to enhance the delivery of chemotherapeutic agents into three different cancer cells (HT-29, MCF-7, and PC-3). In silico studies were performed to simulate the pharmacokinetic (PK) parameters of each peptide and antineoplastic agent to help predict synergistic interactions in vitro. These simulations predicted peptides P2-P4 to have higher bioavailability and lower Tmax, as well as the chemotherapeutic agent 5-fluorouracil (5-FU) to have enhanced permeability properties over other antineoplastic agents, with P3 having prominent accumulation in the colon. In vitro studies were then performed to evaluate the combination of each peptide with the chemotherapeutic agents as well as to assess the nature of drug interactions through the quantification of the Combination Index (CI). Our findings in MCF-7 and PC-3 cancer cells demonstrated that the combination of these peptides with paclitaxel (PTX) and doxorubicin (DOXO), respectively, is not advantageous over a single treatment with the chemotherapeutic agent. In the case of HT-29 colorectal cancer cells, the combination of P2-P4 with 5-FU resulted in synergistic cytotoxic effects, as predicted by the in silico simulations. Taken together, these findings demonstrate that these CPP6-conjugates can be used as adjuvant agents to increase the delivery of 5-FU into HT-29 colorectal cancer cells. Moreover, these results support the use of in silico approaches for the prediction of the interaction between drugs in combination therapy for cancer.

Project description: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/.

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Prediction of LncRNA-encoded small peptides in glioma and oligomer channel functional analysis using in silico approaches.

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