• Microbiology and Biotechnology Letters
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• v.50 no.1
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• pp.102-109
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• 2022

Bile acids are essential molecules produced by vertebrates that are involved in several physiological roles, including the uptake of nutrients. Bacterial isolates capable of producing bile acids de novo have been identified and characterized. Such isolates may provide access to novel biochemical pathways suitable for the design of microbial cell factories. Here, we further characterized the ability of Maribacter dokdonensis, Dokdonia donghaensis, and Myroides pelagicus to produce bile acids. Contrary to previous reports, we did not observe de novo production of bile acids by these isolates. Instead, we found that these isolates deconjugated the amino acid moiety of bile acids present in the growth medium used in previous reports. Through genomic analysis, we identified putative bile salt hydrolases, which could be responsible for the different bile acid modifications observed. Our results challenge the hypothesis of de novo microbial bile acid production, while further demonstrating the diverse capacity of bacteria to modify bile acids.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• BMB Reports
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• v.37 no.4
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• pp.460-465
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• 2004

A 16-residue polypeptide model with the sequence acetyl-YALSLAATLLKEAASL-OH was derived by rational de novo peptide design. The designed sequence consists of amino acid residues with high propensity to adopt an alpha helical conformation, and sequential order was arranged to produce an amphipathic surface. The designed sequence was chemically synthesized using a solid-phase method and the polypeptide was purified by reverse-phase liquid chromatography. Molecular mass analysis by electro-spray ionization mass spectroscopy confirmed the correct designed sequence. Structural characterization by circular dichroism spectroscopy demonstrated that the peptide adopts the expected alpha helical conformation in 50% acetonitrile solution. Liposome binding assay using Small Unilamellar Vesicle (SUV) showed a marked release of entrapped glucose by interaction between the lipid membrane and the tested peptide. The channel-forming activity of the peptide was revealed by a planar lipid bilayer experiment. An analysis of the conducting current at various applied potentials suggested that the peptide forms a cationic ion channel with an intrinsic conductance of 188 pS. These results demonstrate that a simple rational de novo design can be successfully employed to create short peptides with desired structures and functions.

• Bulletin of the Korean Chemical Society
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• v.30 no.6
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• pp.1313-1316
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• 2009

Cdc25 phosphatases have been considered as attractive drug targets for anticancer therapy due to the correlation of their overexpression with a wide variety of cancers. We have been able to identify five novel Cdc25 phosphatase inhibitors with micromolar activity by means of a structure-based de novo design method with a known inhibitor scaffold. Because the newly discovered inhibitors are structurally diverse and have desirable physicochemical properties as a drug candidate, they deserve further investigation as anticancer drugs. The differences in binding modes of the identified inhibitors in the active sites of Cdc25A and B are addressed in detail.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.38-46
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• 2023

The value of living a long and healthy life without suffering has increased owing to aging populations, transition to welfare societies, and global interest in health deriving from the novel coronavirus disease pandemic. New drug development has gained attention as both a tool to improve the quality of life and high-value market, with blockbuster drugs potentially generating over 10 billion dollars in annual revenue. However, for newly discovered substances to be used as drugs, various properties must be verified over a long period in a time-consuming and costly process. Recently, the development of artificial intelligence technologies, such as deep and reinforcement learning, has led to significant changes in drug development by enabling the effective identification of drug candidates that satisfy desired properties. We explore and discuss trends in artificial intelligence for de novo drug design.

• Journal of Microbiology and Biotechnology
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• v.7 no.2
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• pp.138-143
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• 1997

We have designed and constructed a gene encoding novel high essential amino acid encoding protein(HEAAE). The resultant DNA fragment was tested for in vitro and in vivo expression and then cloned into plant expression vector pBI121, under the control of the cauliflower mosaic virus 35S promoter. Agrobacterium tumefaciens, strain LBA4404, was subsequently transformed with this new construct and Nicotiana tabacum var. Xanthi transgenic plants were obtained. DNA analysis by Southern procedure confirmed the presence of the multi-copy number of genes in the transformed plants. Analysis of RNA and protein synthesized in these transgenic plants demonstrated the stable expression of this gene.

• The Plant Pathology Journal
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• v.33 no.6
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• pp.529-542
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• 2017

Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3377-3380
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• 2014

• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3632-3636
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• 2014

In this study we designed and synthesized several heptapeptides that are enforced to form an amphipathic helix using all-hydrocarbon stapling system and evaluated their antimicrobial and hemolytic activities. The antimicrobial activity showed clear structure-activity relationships, confirming the importance of helicity and amphipathicity. Some stapled heptapeptides displayed a moderate antimicrobial activity along with a low hemolytic activity. To our best knowledge, although not highly potent, these stapled peptides represent the shortest helical amphipathic antimicrobial peptides reported to date. The preliminary data obtained in this work would serve as a good starting point for further developing short analogs of amphipathic helical antimicrobial peptides.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.343-346
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• 2021

약물 디자인이란 단백질과 같은 생물학적 표적에 작용할 수 있는 새로운 약물을 개발하는 과정이다. 전통적인 방법은 탐색과 개발 단계로 구성되어 있으나, 하나의 신약 개발을 위해서는 10 년 이상의 장시간이 요구되기 때문에, 이러한 기간을 단축하기 위한 인공지능 기반의 약물 디자인 방법들이 개발되고 있다. 하지만 많은 심층학습 기반의 약물 디자인 모델들은 RNN 기법을 활용하고 있고, RNN 은 훈련속도가 느리다는 단점이 있기 때문에 개선의 여지가 남아있다. 이런 단점을 극복하기 위해 본 연구는 self-attention 과 variational autoencoder 를 활용한 SMILES 생성 모델을 제안한다. 제안된 모델은 최신 약물 디자인 모델 대비 훈련 시간을 1/36 단축하고, 뿐만 아니라 유효한 SMILES 를 더 많이 생성하는 것을 확인하였다.