AnoChem: Prediction of chemical structural abnormalities based on machine learning models.

De novo drug design aims to rationally discover novel and potent compounds while reducing experimental costs during the drug development stage. Despite the numerous generative models that have been developed, few successful cases of drug design utilizing generative models have been reported. One of the most common challenges is designing compounds that are not synthesizable or realistic. Therefore, methods capable of accurately assessing the chemical structures proposed by generative models for drug design are needed. In this study, we present AnoChem, a computational framework based on deep learning designed to assess the likelihood of a generated molecule being real. AnoChem achieves an area under the receiver operating characteristic curve score of 0.900 for distinguishing between real and generated molecules. We utilized AnoChem to evaluate and compare the performances of several generative models, using other metrics, namely SAscore and Fréschet ChemNet distance (FCD). AnoChem demonstrates a strong correlation with these metrics, validating its effectiveness as a reliable tool for assessing generative models. The source code for AnoChem is available at https://github.com/CSB-L/AnoChem.

Altered expression of ADM and ADM2 by hypoxia regulates migration of trophoblast and HLA-G expression†.

Preeclampsia (PE) is a placental disorder caused by endothelial dysfunction via trophoblast inadequate invasion activity. Adrenomedullin (ADM) and ADM2 are multifunctional peptides that can support vascular activity and placental growth. However, correlation between ADMs and trophoblast functions is currently unclear. The objective of this study was to analyze changes in expression of ADMs in placenta and HTR-8/SVneo trophoblast cells under hypoxia and their effects on invasion activity of trophoblast cells and expression of HLA-G. In placental tissues of PE, expression levels of ADM and HLA-G were significantly increased (P < 0.05) whereas expression of ADM2 was decreased compared to that in normal term placenta. Under hypoxia, expression levels of ADM, ADM2, and HLA-G and invasion ability of trophoblast cells were increased in hypoxia-inducible factor-1 (HIF-1α)- dependent manner (P < 0.05). Treatment with ADMs agonists reduced HIF-1α activity whereas enhanced invasion ability under hypoxia. However, they were not changed after cotreatment of ADMs and HIF-1α inhibitor, YC-1, although expression levels of invasion-related genes MMP2, MMP9, and Rac1 were altered (P < 0.05). ADMs also increased HLA-G expression under normoxia whereasADM2 or cotreatment of ADMs under hypoxia attenuated HLA-G expression (P < 0.05). Our findings demonstrate that altered expression of ADMs plays a critical role in placental physiology, especially in trophoblast invasion and immune-modulation under hypoxia.

Current status of pan-genome analysis for pathogenic bacteria.

Biological knowledge accumulated over the decades and advances in computational methods have facilitated the implementation of pan-genome analysis that aims at better understanding of genotype-phenotype associations of a specific group of organisms. Pan-genome analysis has been shown to be an effective approach to better understand a clade of pathogenic bacteria because it helps developing various and tailored therapeutic strategies on the basis of their biological similarities and differences. Here, we review recent progress in the pan-genome analysis of pathogenic bacteria. In particular, we focus on computational tools that allow streamlined pan-genome analysis. Also, various applications of pan-genome analysis including those relevant to devising strategies for the prevention and treatment of pathogenic bacteria are reviewed.

Current status and applications of genome-scale metabolic models.

Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.

Expanded synthetic small regulatory RNA expression platforms for rapid and multiplex gene expression knockdown.

Synthetic small regulatory RNA (sRNA) can efficiently downregulate target gene expression at translational level in metabolic engineering, but cannot be used in engineered strain already having incompatible plasmid(s). To address this problem and make the sRNA gene expression modulation platform universally applicable, we report the development and applications of expanded synthetic sRNA expression platforms for rapid, multiplexed and genome-scale target gene knockdown in engineered Escherichia coli. As proof-of-concept, high performance strains capable of producing L-proline (54.1 g l-1) and L-threonine (22.9 g l-1) are rapidly developed by combinatorial knockdown of up to three genes via one-step co-transformation of sRNA expression vectors. Furthermore, a genome-scale sRNA library targeting 1,858 E. coli genes is employed to construct crude violacein (5.19 g l-1) and indigo (135 mg l-1) producers by high-throughput colorimetric screening. These examples demonstrate that the expanded sRNA expression vectors developed here enables rapid development of chemical overproducers regardless of plasmid compatibility.