Expanding AI-Driven Drug Discovery: Virtual Screening of Natural Compounds for Enhanced Cultured Meat Production
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초록
본 연구에서는 생물정보학과 인공지능 기반의 신약 개발 접근법을 활용하여 배양육 생산을 가속화하기 위한 천연물 첨가제를 도출하는 방법을 제안한다. 생물정보학적 접근으로 유방암 세포대비 정상 세포에서 높게 발현되는 차등 발현 유전자들을 선별하고, 이를 근육 조직 및 지방 조직 성장을 저해하는 유전자 후보로 정의하였다. 인공지능 기반의 신약 개발 접근법을 이용하여단백질과 화합물의 결합 예측 모델을 구성하였고, 두 개의 천연물 데이터베이스(COCONUT, 한국한의약진흥원 천연물 물질 데이터베이스)를 가상 스크리닝하여 근육 조직과 지방 조직의 성장을저해하는 단백질들과 잠재적으로 결합하는 천연물 후보 22종을 도출하였다.
Abstract
In this study, we propose a methodology for deriving natural additives that expedite cultured meat production through a combination of bioinformatics and artificial intelligence-based approaches in drug development. Leveraging a bioinformatics approach, we identify differentially expressed genes that are genetically associated with muscle and adipose tissue growth in breast cancer cells. Subsequently, employing an artificial intelligence-based drug development approach, we construct a model for predicting protein and compound interactions, and conduct virtual screenings of two natural product databases (COCONUT and the Korean Traditional Medicine Database). This enables us to identify 22 candidates of natural compounds that bind to proteins responsible for inhibiting muscle and adipose tissue growth.
참고문헌 (18)
- Shengyong Ng, "Integrating biomaterials and food biopolymers for cultured meat production", Acta Biomaterialia, 124, pp.108~129 (2021), Elsevier BV[DOI]
- Margherita Pallaoro, "Towards a More Realistic In Vitro Meat: The Cross Talk between Adipose and Muscle Cells", International Journal of Molecular Sciences, 24(7), pp.6630~ (2023), MDPI AG[DOI]
- Kwang‐Hwan Choi, "Muscle stem cell isolation and in vitro culture for meat production: A Methodological Review", Comprehensive Reviews in Food Science and Food Safety, 20(1), pp.429~457 (2020), Wiley[DOI]
- Mark J. Post, "Scientific, sustainability and regulatory challenges of cultured meat", Nature Food, 1(7), pp.403~415 (2020), Springer Science and Business Media LLC[DOI]
- Matthew E. Ritchie, "limma powers differential expression analyses for RNA-sequencing and microarray studies", Nucleic Acids Research, 43(7), pp.e47~e47 (2015), Oxford University Press (OUP)[DOI]
- Kexin Huang, "DeepPurpose: a deep learning library for drug–target interaction prediction", Bioinformatics, 36(22-23), pp.5545~5547 (2020), Oxford University Press (OUP)[DOI]
- Jaechang Lim, "Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation", Journal of Chemical Information and Modeling, 59(9), pp.3981~3988 (2019), American Chemical Society (ACS)[DOI]
- Hakime Öztürk, "Exploring chemical space using natural language processing methodologies for drug discovery", Drug Discovery Today, 25(4), pp.689~705 (2020), Elsevier BV[DOI]
- 공현승, "소규모 데이터세트에서 강건성을 유지하는 hERG 연관 심장독성 예측 딥러닝 아키텍처", 정보과학회 컴퓨팅의 실제 논문지, 28(6), pp.339~347 (2022), 한국정보과학회[DOI]
- Ashley Alex, "Anatomy and Physiology of the Breast during Pregnancy and Lactation", Advances in Experimental Medicine and Biology, pp.3~7 (2020), Springer International Publishing[DOI]
- Muhammad Akram, "Awareness and current knowledge of breast cancer", Biological Research, 50(1) (2017), Springer Science and Business Media LLC[DOI]
- Hakime Öztürk, "DeepDTA: deep drug–target binding affinity prediction", Bioinformatics, 34(17), pp.i821~i829 (2018), Oxford University Press (OUP)[DOI]
- Onat Kadioglu, "Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning", Computers in Biology and Medicine, 133, pp.104359~ (2021), Elsevier BV[DOI]
- Lucas A. Machado, "A machine learning-based virtual screening for natural compounds capable of inhibiting the HIV-1 integrase", Frontiers in Drug Discovery, 2 (2022), Frontiers Media SA[DOI]
- Ivyna Bong Pau Ni, "Gene expression patterns distinguish breast carcinomas from normal breast tissues: The Malaysian context", Pathology - Research and Practice, 206(4), pp.223~228 (2010), Elsevier BV[DOI]
- T. Liu, "BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities", Nucleic Acids Research, 35(Database), pp.D198~D201 (2007), Oxford University Press (OUP)[DOI]
- Maria Sorokina, "COCONUT online: Collection of Open Natural Products database", Journal of Cheminformatics, 13(1) (2021), Springer Science and Business Media LLC[DOI]
- "Korea Institute of Oriental Medicine Promotion Status of natural substance DB"[URL]