research
- The limit in measurement and time resolution of immunoprecipitation technique improved by a hundred thousand fold
Professor Yoon’s research team developed a fluorescent microscope that can observe a single molecule. The team grafted the immunoprecipitation technique, traditionally used in protein interaction analysis, to the microscope to develop a “live molecular level immunoprecipitation technique”. The team successfully and accurately measured the reaction between two proteins by repeated momentary interactions in the unit of tens of milliseconds.
The existing immunoprecipitation technique required at least one day to detect interaction between two proteins. There were limitations in detecting momentary or weak interactions. Also, quantitative analysis of the results was difficult since the image was measured by protein-band strength. The technique could not be used for live observation.
The team aimed to drastically improve the existing technique and to develop accurate method of measurement on molecular level. The newly developed technology can enable observation of protein interaction within one hour. Also, the interaction can be measured live, thus the protein interaction phenomenon can be measured in depth.
Moreover, every programme used in the experiment was developed and distributed by the research team so source energy is secured and created the foundation for global infra.
Professor Tae Young Yoon said, “The newly developed technology does not require additional protein expression or purification. Hence, a very small sample of protein is enough to accurately analyse protein interaction on a kinetic level.” He continued, “Even cancerous protein from the tissue of a cancer patient can be analysed. Thus a platform for customised anti-cancer medicine in the future has been prepared, as well.”
Figure 1. Mimetic diagram comparing the existing immunoprecipitation technique and the newly developed live molecular level immunoprecipitation technique
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research KAIST leads AI-based analysis on drug-drug interactions involving Paxlovid
KAIST (President Kwang Hyung Lee) announced on the 16th that an advanced AI-based drug interaction prediction technology developed by the Distinguished Professor Sang Yup Lee's research team in the Department of Biochemical Engineering that analyzed the interaction between the PaxlovidTM ingredients that are used as COVID-19 treatment and other prescription drugs was published as a thesis. This paper was published in the online edition of 「Proceedings of the National Academy of Sciences of Ameri
2023-03-16 -
people Professor Juho Kim’s Team Wins Best Paper Award at ACM CHI 2022
The research team led by Professor Juho Kim from the KAIST School of Computing won a Best Paper Award and an Honorable Mention Award at the Association for Computing Machinery Conference on Human Factors in Computing Systems (ACM CHI) held between April 30 and May 6. ACM CHI is the world’s most recognized conference in the field of human computer interactions (HCI), and is ranked number one out of all HCI-related journals and conferences based on Google Scholar’s h-5 index. Best pap
2022-06-13 -
event Professor Sung-Ju Lee’s Team Wins the Best Paper and the Methods Recognition Awards at the ACM CSCW
A research team led by Professor Sung-Ju Lee at the School of Electrical Engineering won the Best Paper Award and the Methods Recognition Award from ACM CSCW (International Conference on Computer-Supported Cooperative Work and Social Computing) 2021 for their paper “Reflect, not Regret: Understanding Regretful Smartphone Use with App Feature-Level Analysis”. Founded in 1986, CSCW has been a premier conference on HCI (Human Computer Interaction) and Social Computing. This year, 340
2021-11-22 -
research Defining the Hund Physics Landscape of Two-Orbital Systems
Researchers identify exotic metals in unexpected quantum systems Electrons are ubiquitous among atoms, subatomic tokens of energy that can independently change how a system behaves—but they also can change each other. An international research collaboration found that collectively measuring electrons revealed unique and unanticipated findings. The researchers published their results on May 17 in Physical Review Letters. “It is not feasible to obtain the solution just by tracing
2021-06-17 -
research Deep Learning Predicts Drug-Drug and Drug-Food Interactions
A Korean research team from KAIST developed a computational framework, DeepDDI, that accurately predicts and generates 86 types of drug-drug and drug-food interactions as outputs of human-readable sentences, which allows in-depth understanding of the drug-drug and drug-food interactions. Drug interactions, including drug-drug interactions (DDIs) and drug-food constituent interactions (DFIs), can trigger unexpected pharmacological effects, including adverse drug events (ADEs), wi
2018-04-18