Gene expression analysis for understanding cancer cell development is a basic, but an important step, to further our knowledge in cancer research. We may also be interested in understanding gene interactions that may lead to cancer development. One of the most important interactions is a regulatory interaction that involves transcription factor genes. In this research, we are attempting to construct a new regulatory network that imitates the transcription and translation processes of mRNA. We construct this network from four different cancer types: bile-duct cancer (BDC), lung adenocarcinoma (LUAD), colorectal cancer (CRC), and hepatocyte carcinoma (HCC). We also integrate differential expression data to obtain the interactions among differentially expressed genes. We then try to find intersecting sub-networks that exist across all cancer types. We believe that the transcription factor genes found in intersection sub-networks may reveal an important mechanism that affects cancer cell growth. In this research, we found that genes, such as those in the TEAD4, IRX5, HMGA1, and E2F gene family and the SOX gene family, are found in the enrichment analysis of the intersection sub-network obtained from multiple cancer data-sets. These genes point us toward dysregulation of the cell cycle, cell division, and cell proliferation mechanisms in cancer cells. These genes may become new cancer drug targets for cancer treatment.

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High-throughput screening (HTS) is a common practice in drug discovery. Although the chemoinformatics community has proposed various approaches for HTS data analysis, medicinal chemists continue to long for intuitive tools for the structure-activity relationship (SAR) analysis of HTS hits. Here, the author propose SAR analysis tools that were designed to help medicinal chemists grasp the chemical space of interest with conventional SAR tables. These tools comprise an on-the-fly analysis environment and a series of computational protocols for data processing prior to the interactive analysis. The protocols are designed for the following processes: i) structural classification based on simple rules to mimic visual inspection by medicinal chemists; ii) exhaustive generation of promising SAR tables using Pharmacofragment (PHF), a novel substructure concept; and iii) comprehensive analogue search to identify compounds that correspond to blank cells in SAR tables from compounds at hand. A case study using data from a screen for ribosomal protein S6 phosphorylation inhibitors (PubChem AID:493208) suggests that these tools are useful for generating conventional SAR tables for practical application to large-scale data such as HTS.

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We developed an automated FMO calculation protocol (Auto-FMO protocol) to calculate huge numbers of protein and ligand complexes, such as drug discovery targets, by an ab initio FMO method. The protocol performs not only FMO calculations but also pre-processing of input structures by homology modeling of missing atoms and subsequent MM-based optimization, as well as post-processing of calculation results. In addition, QM/MM optimization of complex structures, conformational searches of ligand structures in solvent, and MM-PBSA/GBSA calculations can be optionally carried out. In this paper, FMO calculations for 149 X-ray complex structures of estrogen receptor α and p38 MAP kinase were performed at the K computer and in-house PC cluster server by using the Auto-FMO protocol. To demonstrate the usefulness of the Auto-FMO protocol, we compared the ligand binding interaction energies by the Auto-FMO protocol with those of manually prepared data. In most cases, the data calculated by the Auto-FMO protocol showed reasonable agreement with the manually prepared data. Further improvement of the protocol is necessary for the treatment of ionization and tautomerization at the structure preparation stage, because some outlier data were observed due to these issues. The Auto-FMO protocol provides a powerful tool to deal with huge numbers of complexes for drug design, as well as for the construction of the FMO database (http://drugdesign.riken.jp/FMODB/) released in 2019.

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An antibiotic flucloxacillin (FX) which is widely used for the treatment of staphylococcal infection, is known to cause liver injury. A genome-wide association study has shown that FX induced idiosyncratic drug toxicity (IDT) is associated with HLA-B*57:01. FX is processed in the human body to produce several metabolites. Molecular interactions of FX or its metabolites with HLA-B*57:01 should play a crucial role in the occurrence of the adverse drug reaction. In this study, we have undertaken docking simulations of interactions of FX and its metabolites with HLA-B*57:01 to understand molecular mechanisms leading to the onset of IDT.

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