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The presence of some amino acid mutations in the amino acid sequence that determines a protein’s structure can significantly affect that 3D structure and its biological function. However, the effects upon structural and functional changes differ for each displaced amino acid, and it is very difficult to predict these changes in advance. Although computer simulations are very effective at predicting conformational changes, they struggle to determine whether the amino acid mutation of interest induces sufficient conformational changes, unless the researcher is a specialist in molecular structure calculations. Therefore, we created a framework that efficiently utilizes molecular dynamics and persistent homology methods to identify amino acid mutations that induce structural changes. We show that this framework can be used not only to predict conformational changes produced by amino acid mutations but also to extract groups of mutations that significantly alter similar molecular interactions, by capturing the resultant protein–protein interaction changes.

Polymorphisms in immune-related proteins and viral spike proteins are high and complicate host-virus interactions. Therefore, diversity analysis of such protein structures is essential to understand the mechanism of the immune system. However, experimental methods, including X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy, have several problems: (i) they are conducted under different conditions from the actual cellular environment, (ii) they are laborious, time-consuming, and expensive, and (iii) they do not provide information on the thermodynamic behaviors. In this paper, we propose a computational method to solve these problems by using MD simulations, persistent homology, and a Bayesian statistical model. We apply our method to eight types of HLA-DR complexes to evaluate the structural diversity. The results show that our method can correctly discriminate the intrinsic structural variations caused by amino acid mutations from the random fluctuations caused by thermal vibrations. In the end, we discuss the applicability of our method in combination with existing deep learning-based methods for protein structure analysis.

Summary: The cellular interactions in the tumor microenvironment of colorectal cancer (CRC) are poorly understood, hindering patient treatment. In the current study, we investigate whether events occurring at the invasion front are of particular importance for CRC treatment strategies. To this end, we analyze CRC tissues by combining spatial transcriptomics from patients with a public single-cell transcriptomic atlas to determine cell-cell interactions at the invasion front. We show that CRC cells are localized specifically at the invasion front. These cells induce human leukocyte antigen G (HLA-G) to produce secreted phosphoprotein 1 (SPP1)+ macrophages while conferring CRC cells with anti-tumor immunity, as well as proliferative and invasive properties. Taken together, these findings highlight the signaling between CRC cell populations and stromal cell populations at the cellular level.

Many anticancer drugs have serious adverse effects; therefore, it is necessary to target features specific to cancer cells to minimize the effects on healthy cells. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) was reported to be specifically enhanced in cancer. We confirmed the validity of MTHFD2 as a drug discovery target using clinical data. In addition, we performed in silico screening to design an anticancer drug specifically targeting MTHFD2. Analysis of the clinical data indicated that MTHFD2 was enhanced in most cancers compared with normal tissues, and affected the prognosis in cancer patients. Candidate compounds for MTHFD2 inhibitors were identified using in silico drug discovery techniques, and the important interactions for MTHFD2 binding were determined. In addition, these candidate compounds decreased levels of MTHFD2 metabolites in cancer cells. The findings of the present study may help to develop anticancer drugs targeting MTHFD2, with a view to minimizing the adverse effects of anticancer drugs.

The M2 isoform of pyruvate kinase, the final rate-limiting enzyme of aerobic glycolysis, is expressed during embryonic development. In contrast, the M1 isoform is expressed in differentiated cells due to alternative splicing. Here we investigated murine embryonic stem cells (ESCs) with Pkm1 or Pkm2 knock-in alleles. Pkm1 allele knock-in resulted in excessive oxidative phosphorylation and induced the formation of cysteine-thiol disulfide-dependent complexes of forkhead box class-O (FOXO) transcription factors, which resulted in altered endoderm differentiation. In contrast, Pkm2 knock-in induced synthesis of a methylation-donor, S-adenosylmethionine, and increased unsaturated eicosanoid groups, which contributed to the redox control and maintenance of ESC undifferentiated status. Because PKM2 is also a critical enzyme for the cancer-specific Warburg effect, our results demonstrate an important role for the Pkm2 allele in establishing intracellular redox conditions and modulating PKM1-dependent oxidative phosphorylation events to achieve an appropriate ESC differentiation program.