SARS-CoV-2 and its variants caused the COVID-19 pandemic. Vaccines that target conserved regions of SARS-CoV-2 and stimulate protective T-cell responses are important for reducing symptoms and limiting the infection. Seven cytotoxic (CTL) and five helper T-cells (HTL) epitopes from ORF1ab were identified using NetCTLpan and NetMHCIIpan algorithms, respectively. These epitopes were generated from ORF1ab regions that are evolutionary stable as reflected by zero Shannon’s entropy and are presented by 56 human leukocyte antigen (HLA) Class I and 22 HLA Class II, ensuring good coverage for the Indonesian and world population. Having fulfilled other criteria such as immunogenicity, IFNγ inducing ability, and non-homology to human and microbiome peptides, the epitopes were assembled into a vaccine construct (VC) together with β-defensin as adjuvant and appropriate linkers. The VC was shown to have good physicochemical characteristics and capability of inducing CTL as well as HTL responses, which stem from the engagement of the vaccine with toll-like receptor 4 (TLR4) as revealed by docking simulations. The most promiscuous peptide 899WSMATYYLF907 was shown via docking simulation to interact well with HLA-A*24:07, the most predominant allele in Indonesia. The data presented here will contribute to the in vitro study of T-cell epitope mapping and vaccine design in Indonesia.

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Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position’s reactivity and exhibits interesting behavior on neighboring bases in the sequence.

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Objective: The aim of this paper is to identify the list of microRNA (miRNA) which can regulate the aberrant expression of IQGAP in liver cancer formation. The aberrant expression of IQ motif-containing GTPase-activating protein (IQGAP) family which consists of IQGAP1, IQGAP2, and IQGAP3 has been linked to carcinogenesis in human cancers. The reciprocal expression of IQGAP family in human cancer has been studied to act as oncogenes or tumor suppressor genes. A growing number of studies suggest that upregulated or downregulated expression of IQGAP family triggers cancer development.Methods: A correlation study was performed to construct a pathway to inhibit or activate IQGAP family between miRNAs and IQGAPs. A pre-processing step was conducted to download, filter and process the dataset from TCGA. It yields miRNA and IQGAP gene expression matrix. Then, correlation computation was computed using MATLAB. Moreover, this study linked the results to the MiRTarBase to validate the prediction result with the wet lab experimental result.Results: This study identified significantly inversely correlation in 51 miRNAs-IQGAP1, 169 miRNAs-IQGAP2, and 33 miRNAs-IQGAP3, respectively, which may potentially play a role in a liver cancer formation. Some of the results also can be found in miRTarBase. It supports the precision of those miRNA and IQGAP interaction between dry lab and wet lab study. IQGAP1 and IQGAP2 mostly has been identified as an oncogene in cancer but IQGAP2 has been discovered as tumor suppressor gene. The list of miRNA in the result of this study can become a potential therapy to target the aberrant expression of IQGAP family.Conclusion: miRNA function is known as an oncogene or tumor suppressor gene in cancer development. Therefore, it can be one of the important molecular biology which may target the aberrant expression of IQGAP in liver cancer.