Pancreatic adenocarcinoma (PAAD) is one of the most challenging cancers with high morbidity and mortality. KRAS mutations could occur as an early event in PAAD. KRAS genes are highly mutated with recurrent mutations in various cancer types, including PAAD. We aim to depict the omics landscape of KRAS mutations and seek potential novel drugs for pancreatic cancer patients with KRAS mutations. This study used data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) for KRAS mutation analysis in a multi-omics manner. We found that the genomics and transcriptomics patterns of KRAS mutations are significantly different compared to the corresponding non-KRAS-mutated PAAD samples. Pancreatic cancer's prognosis is directly associated with a specific KRAS mutation and its protein's structure instability. Our analysis confirmed that Irinotecan could be a potential drug for PAAD patients with KRASG12Dmutation.

Drug-target interactions (DTIs) play a crucial role in target-based drug discovery and development. Computational prediction of DTIs can effectively complement experimental wet-lab techniques for the identification of DTIs, which are typically time- and resource-consuming. However, the performances of the current DTI prediction approaches suffer from a problem of low precision and high false-positive rate. In this study, we aim to develop a novel DTI prediction method for improving the prediction performance based on a cascade deep forest (CDF) model, named DTI-CDF, with multiple similarity-based features between drugs and the similarity-based features between target proteins extracted from the heterogeneous graph, which contains known DTIs. In the experiments, we built five replicates of 10-fold cross-validation under three different experimental settings of data sets, namely, corresponding DTI values of certain drugs (SD), targets (ST), or drug-target pairs (SP) in the training sets are missed but existed in the test sets. The experimental results demonstrate that our proposed approach DTI-CDF achieves a significantly higher performance than that of the traditional ensemble learning-based methods such as random forest and XGBoost, deep neural network, and the state-of-the-art methods such as DDR. Furthermore, there are 1352 newly predicted DTIs which are proved to be correct by KEGG and DrugBank databases. The data sets and source code are freely available at https://github.com//a96123155/DTI-CDF.

BACKGROUND: There are ever increasing researches implying that noncoded RNAs (ncRNAs) specifically circular RNAs (circRNAs) and microRNAs (miRNAs) in exosomes play vital roles in respiratory disease. However, the detailed mechanisms persist to be unclear in mycobacterial infection. METHODS: In order to detect circRNAs and miRNAs expression pattern and potential biological function in tuberculosis, we performed immense parallel sequencing for exosomal ncRNAs from THP-1-derived macrophages infected by Mycobacterium tuberculosis H37Ra, Mycobacterium bovis BCG and control Streptococcus pneumonia, respectively and uninfected normal cells. Besides, THP-1-derived macrophages were used to verify the validation of differential miRNAs, and monocytes from PBMCs and clinical plasma samples were used to further validate differentially expressed miR-185-5p. RESULTS: Many exosomal circRNAs and miRNAs associated with tuberculosis infection were recognized. Extensive enrichment analyses were performed to illustrate the major effects of altered ncRNAs expression. Moreover, the miRNA-mRNA and circRNA-miRNA networks were created and expected to reveal their interrelationship. Further, significant differentially expressed miRNAs based on Exo-BCG, Exo-Ra and Exo-Control, were evaluated, and the potential target mRNAs and function were analyzed. Eventually, miR-185-5p was collected as a promising potential biomarker for tuberculosis. CONCLUSION: Our findings provide a new vision for exploring biological functions of ncRNAs in mycobacterial infection and screening novel potential biomarkers. To sum up, exosomal ncRNAs might represent useful functional biomarkers in tuberculosis pathogenesis and diagnosis.

BACKGROUND: The most frequently mutated gene pairs in pancreatic adenocarcinoma (PAAD) are KRAS and TP53, and our goal is to illustrate the multiomics and molecular dynamics landscapes of KRAS/TP53 mutation and also to obtain prospective novel drugs for KRAS- and TP53-mutated PAAD patients. Moreover, we also made an attempt to discover the probable link amid KRAS and TP53 on the basis of the abovementioned multiomics data. METHOD: We utilized TCGA & Cancer Cell Line Encyclopedia data for the analysis of KRAS/TP53 mutation in a multiomics manner. In addition to that, we performed molecular dynamics analysis of KRAS and TP53 to produce mechanistic descriptions of particular mutations and carcinogenesis. RESULT: We discover that there is a significant difference in the genomics, transcriptomics, methylomics, and molecular dynamics pattern of KRAS and TP53 mutation from the matching wild type in PAAD, and the prognosis of pancreatic cancer is directly linked with a particular mutation of KRAS and protein stability. Screened drugs are potentially effective in PAAD patients. CONCLUSIONS: KRAS and TP53 prognosis of PAAD is directly associated with a specific mutation of KRAS. Irinotecan and vandetanib are prospective drugs for PAAD patients with KRASG12Dmutation and TP53 mutation.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research. The anti-coronavirus target protein sequences reported from the current outbreak are scanned against the anti-coronavirus target data-sets via CORONAPEP which provides precision-based anti-coronavirus peptides. This tool is specifically for the coronavirus data, which can predict peptides from the whole genome, or a gene or protein's list. Besides it is relatively fast, accurate, userfriendly and can generate maximum output from the limited information. The availability of tools like CORONAPEP will immeasurably perquisite researchers in the discipline of oncology and structure-based drug design.

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.