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VP Sang Yup Lee Honored with the Pony Chung Innovation Award
Vice President for Research Sang Yup Lee became the recipient of the Innovation Award by the Pony Chung Foundation that was established to honor the late Se-yung Chung, the former chairman of Hyundai Development Company. He will receive 200 million KRW in prize money. Chairman Chung developed Korea’s first domestically manufactured automobile, ‘Pony,’ in the mid-1970s that became the cornerstone of Korea’s auto industry today. Distinguished Professor Lee, from the Department of Chemical and Biomolecular Engineering, is a pioneering scholar in the field of systems metabolic engineering who developed various micro-organisms for producing a wide range of fuels, chemicals, materials, and natural compounds. He recently was elected as a foreign member of the Royal Society in the UK and is the first Korean ever elected into the National Academy of Inventors (NAI) in the US as well as one of 13 scholars elected as an International Member of both the National Academy of Sciences (NAS) and the National Academy of Engineering (NAE) in the US.
Prof. Sang Yup Lee Elected as a Foreign Member of the Royal Society
Vice President for Research Distinguished Professor Sang Yup Lee was elected as a foreign member of the Royal Society in the UK. On May 6, the Society announced the list of distinguished new 52 fellows and 10 foreign members who achieved exceptional contributions to science. Professor Lee and Professor V. Narry Kim from Seoul National University are the first foreign members ever elected from Korea. The Royal Society, established in 1660, is one of the most prestigious national science academies and a fellowship of 1,600 of the world’s most eminent scientists. From Newton to Darwin, Einstein, Hawking, and beyond, pioneers and paragons in their fields are elected by their peers. To date, there are 280 Nobel prize winners among the fellows. Distinguished Professor Lee from the Department of Chemical and Biomolecular Engineering at KAIST is one of the Highly Cited Researchers (HCRs) who pioneered systems metabolic engineering and developed various micro-organisms for producing a wide range of fuels, chemicals, materials, and natural compounds. His seminal scholarship and research career have already been recognized worldwide. He is the first Korean ever elected into the National Academy of Inventors (NAI) in the US and one of 13 scholars elected as an International Member of both the National Academy of Sciences (NAS) and the National Academy of Engineering (NAE) in the US. With this fellowship, he added one more accolade of being the first non-US and British Commonwealth scientist elected into the three most prestigious science academies: the NAS, the NAE, and the Royal Society.
Mystery Solved with Math: Cytoplasmic Traffic Jam Disrupts Sleep-Wake Cycles
KAIST mathematicians and their collaborators at Florida State University have identified the principle of how aging and diseases like dementia and obesity cause sleep disorders. A combination of mathematical modelling and experiments demonstrated that the cytoplasmic congestion caused by aging, dementia, and/or obesity disrupts the circadian rhythms in the human body and leads to irregular sleep-wake cycles. This finding suggests new treatment strategies for addressing unstable sleep-wake cycles. Human bodies adjust sleep schedules in accordance with the ‘circadian rhythms’, which are regulated by our time keeping system, the ‘circadian clock’. This clock tells our body when to rest by generating the 24-hour rhythms of a protein called PERIOD (PER) (See Figure 1). The amount of the PER protein increases for half of the day and then decreases for the remaining half. The principle is that the PER protein accumulating in the cytoplasm for several hours enters the cell nucleus all at once, hindering the transcription of PER genes and thereby reducing the amount of PER. However, it has remained a mystery how thousands of PER molecules can simultaneously enter into the nucleus in a complex cell environment where a variety of materials co-exist and can interfere with the motion of PER. This would be like finding a way for thousands of employees from all over New York City to enter an office building at the same time every day. A group of researchers led by Professor Jae Kyoung Kim from the KAIST Department of Mathematical Sciences solved the mystery by developing a spatiotemporal and probabilistic model that describes the motion of PER molecules in a cell environment. This study was conducted in collaboration with Professor Choogon Lee’s group from Florida State University, where the experiments were carried out, and the results were published in the Proceedings of the National Academy of Sciences (PNAS) last month. The joint research team’s spatial stochastic model (See Figure 2) described the motion of PER molecules in cells and demonstrated that the PER molecule should be sufficiently condensed around the cell nucleus to be phosphorylated simultaneously and enter the nucleus together (See Figure 3 Left). Thanks to this phosphorylation synchronization switch, thousands of PER molecules can enter the nucleus at the same time every day and maintain stable circadian rhythms. However, when aging and/or diseases including dementia and obesity cause the cytoplasm to become congested with increased cytoplasmic obstacles such as protein aggregates and fat vacuoles, it hinders the timely condensation of PER molecules around the cell nucleus (See Figure 3 Right). As a result, the phosphorylation synchronization switch does not work and PER proteins enter into the nucleus at irregular times, making the circadian rhythms and sleep-wake cycles unstable, the study revealed. Professor Kim said, “As a mathematician, I am excited to help enable the advancement of new treatment strategies that can improve the lives of so many patients who suffer from irregular sleep-wake cycles. Taking these findings as an opportunity, I hope to see more active interchanges of ideas and collaboration between mathematical and biological sciences.” This work was supported by the National Institutes of Health and the National Science Foundation in the US, and the International Human Frontiers Science Program Organization and the National Research Foundation of Korea. Publication: Beesley, S. and Kim, D. W, et al. (2020) Wake-sleep cycles are severely disrupted by diseases affecting cytoplasmic homeostasis. Proceedings of the National Academy of Sciences (PNAS), Vol. 117, No. 45, 28402-28411. Available online at https://doi.org/10.1073/pnas.2003524117 Profile: Jae Kyoung Kim, Ph.D. Associate Professor email@example.com http://mathsci.kaist.ac.kr/~jaekkim @umichkim on Twitter Department of Mathematical Sciences Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea Profile: Choogon Lee, Ph.D. Associate Professor firstname.lastname@example.org https://med.fsu.edu/biosci/lee-lab Department of Biomedical Sciences Florida State University Florida, USA (END)
Drawing the Line to Answer Art’s Big Questions
- KAIST scientists show how statistical physics can reveal art trends across time and culture. - Algorithms have shown that the compositional structure of Western landscape paintings changed “suspiciously” smoothly between 1500 and 2000 AD, potentially indicating a selection bias by art curators or in art historical literature, physicists from the Korea Advanced Institute of Science and Technology (KAIST) and colleagues report in the Proceedings of the National Academy of Sciences (PNAS). KAIST statistical physicist Hawoong Jeong worked with statisticians, digital analysts and art historians in Korea, Estonia and the US to clarify whether computer algorithms could help resolve long-standing questions about design principles used in landscape paintings, such as the placement of the horizon and other primary features. “A foundational question among art historians is whether artwork contains organizing principles that transcend culture and time and, if yes, how these principles evolved over time,” explains Jeong. “We developed an information-theoretic approach that can capture compositional proportion in landscape paintings and found that the preferred compositional proportion systematically evolved over time.” Digital versions of almost 15,000 canonical landscape paintings from the Western renaissance in the 1500s to the more recent contemporary art period were run through a computer algorithm. The algorithm progressively divides artwork into horizontal and vertical lines depending on the amount of information in each subsequent partition. It allows scientists to evaluate how artists and various art styles compose landscape artwork, in terms of placement of a piece’s most important components, in addition to how high or low the landscape’s horizon is placed. The scientists started by analysing the first two partitioning lines identified by the algorithm in the paintings and found they could be categorized into four groups: an initial horizontal line followed by a second horizontal line (H-H); an initial horizontal line followed by a second vertical line (H-V); a vertical followed by horizontal line (V-H); or a vertical followed by a vertical line (V-V) (see image 1 and 2). They then looked at the categorizations over time. They found that before the mid-nineteenth century, H-V was the dominant composition type, followed by H-H, V-H, and V-V. The mid-nineteenth century then brought change, with the H-V composition style decreasing in popularity with a rise in the H-H composition style. The other two styles remained relatively stable. The scientists also looked at how the horizon line, which separates sky from land, changed over time. In the 16th century, the dominant horizon line of the painting was above the middle of the canvas, but it gradually descended to the lower middle of the canvas by the 17th century, where it remained until the mid-nineteenth century. After that, the horizon line began gradually rising again. Interestingly, the algorithm showed that these findings were similar across cultures and artistic periods, even through periods dominated by a diversity in art styles. This similarity may well be a function, then, of a bias in the dataset. “In recent decades, art historians have prioritized the argument that there is great diversity in the evolution of artistic expression rather than offering a relatively smoother consensus story in Western art,” Jeong says. “This study serves as a reminder that the available large-scale datasets might be perpetuating severe biases.” The scientists next aim to broaden their analyses to include more diverse artwork, as this particular dataset was ultimately Western and male biased. Future analyses should also consider diagonal compositions in paintings, they say. This work was supported by the National Research Foundation (NRF) of Korea. Publication: Lee, B, et al. (2020) Dissecting landscape art history with information theory. Proceedings of the National Academy of Sciences (PNAS), Vol. 117, No. 43, 26580-26590. Available online at https://doi.org/10.1073/pnas.2011927117 Profile: Hawoong Jeong, Ph.D. Professor email@example.com https://www.kaist.ac.kr Department of Physics Korea Advanced Institute of Science and Technology (KAIST) Daejeon, Republic of Korea (END)
X-ray Scattering Shines Light on Protein Folding
- Multiple forms of a non-functional, unfolded protein follow different pathways and timelines to reach its folded, functional state, a study reveals. - KAIST researchers have used an X-ray method to track how proteins fold, which could improve computer simulations of this process, with implications for understanding diseases and improving drug discovery. Their findings were reported in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 30. When proteins are translated from their DNA codes, they quickly transform from a non-functional, unfolded state into their folded, functional state. Problems in folding can lead to diseases like Alzheimer’s and Parkinson’s. “Protein folding is one of the most important biological processes, as it forms the functioning 3D protein structure,” explained the physical chemist Hyotcherl Ihee of the Department of Chemistry at KAIST. Dr. Tae Wu Kim, the lead author of this research from Ihee’s group, added, “Understanding the mechanisms of protein folding is important, and could pave the way for disease study and drug development.” Ihee’s team developed an approach using an X-ray scattering technique to uncover how the protein cytochrome c folds from its initial unfolded state. This protein is composed of a chain of 104 amino acids with an iron-containing heme molecule. It is often used for protein folding studies. The researchers placed the protein in a solution and shined ultraviolet light on it. This process provides electrons to cytochrome c, reducing the iron within it from the ferric to the ferrous form, which initiates folding. As this was happening, the researchers beamed X-rays at very short intervals onto the sample. The X-rays scattered off all the atomic pairs in the sample and a detector continuously recorded the X-ray scattering patterns. The X-ray scattering patterns provided direct information regarding the 3D protein structure and the changes made in these patterns over time showed real-time motion of the protein during the folding process. The team found cytochrome c proteins initially exist in a wide variety of unfolded states. Once the folding process is triggered, they stop by a group of intermediates within 31.6 microseconds, and then those intermediates follow different pathways with different folding times to reach an energetically stable folded state. “We don’t know if this diversity in folding paths can be generalized to other proteins,” Ihee confessed. He continued, “However, we believe that our approach can be used to study other protein folding systems.” Ihee hopes this approach can improve the accuracy of models that simulate protein interactions by including information on their unstructured states. These simulations are important as they can help identify barriers to proper folding and predict a protein’s folded state given its amino acid sequence. Ultimately, the models could help clarify how some diseases develop and how drugs interact with various protein structures. Ihee’s group collaborated with Professor Young Min Rhee at the KAIST Department of Chemistry, and this work was supported by the National Research Foundation of Korea (NRF) and the Institute for Basic Science (IBS). Figure. The scientists found that non-functional unfolded forms of the protein cytochrome c follow different pathways and timelines to reach a stable functional folded state. Publications: Kim, T. W., et al. (2020) ‘Protein folding from heterogeneous unfolded state revealed by time-resolved X-ray solution scattering’. PNAS. Volume 117. Issue 26. Page 14996-15005. Available online at https://doi.org/10.1073/pnas.1913442117 Profile: Hyotcherl Ihee, Ph.D. Professor firstname.lastname@example.org http://time.kaist.ac.kr/ Ihee Laboratory Department of Chemistry KAIST https://www.kaist.ac.kr Daejeon 34141, Korea Profile: Young Min Rhee, Ph.D. Professor email@example.com http://singlet.kaist.ac.kr Rhee Research Group Department of Chemistry KAIST https://www.kaist.ac.kr Daejeon 34141, Korea (END)
3D Hierarchically Porous Nanostructured Catalyst Helps Efficiently Reduce CO2
- This new catalyst will bring CO2 one step closer to serving as a sustainable energy source. - KAIST researchers developed a three-dimensional (3D) hierarchically porous nanostructured catalyst with carbon dioxide (CO2) to carbon monoxide (CO) conversion rate up to 3.96 times higher than that of conventional nanoporous gold catalysts. This new catalyst helps overcome the existing limitations of the mass transport that has been a major cause of decreases in the CO2 conversion rate, holding a strong promise for the large-scale and cost-effective electrochemical conversion of CO2 into useful chemicals. As CO2 emissions increase and fossil fuels deplete globally, reducing and converting CO2 to clean energy electrochemically has attracted a great deal of attention as a promising technology. Especially due to the fact that the CO2 reduction reaction occurs competitively with hydrogen evolution reactions (HER) at similar redox potentials, the development of an efficient electrocatalyst for selective and robust CO2 reduction reactions has remained a key technological issue. Gold (Au) is one of the most commonly used catalysts in CO2 reduction reactions, but the high cost and scarcity of Au pose obstacles for mass commercial applications. The development of nanostructures has been extensively studied as a potential approach to improving the selectivity for target products and maximizing the number of active stable sites, thus enhancing the energy efficiency. However, the nanopores of the previously reported complex nanostructures were easily blocked by gaseous CO bubbles during aqueous reactions. The CO bubbles hindered mass transport of the reactants through the electrolyte, resulting in low CO2 conversion rates. In the study published in the Proceedings of the National Academy of Sciences of the USA (PNAS) on March 4, a research group at KAIST led by Professor Seokwoo Jeon and Professor Jihun Oh from the Department of Materials Science and Engineering designed a 3D hierarchically porous Au nanostructure with two different sizes of macropores and nanopores. The team used proximity-field nanopatterning (PnP) and electroplating techniques that are effective for fabricating the 3D well-ordered nanostructures. The proposed nanostructure, comprised of interconnected macroporous channels 200 to 300 nanometers (nm) wide and 10 nm nanopores, induces efficient mass transport through the interconnected macroporous channels as well as high selectivity by producing highly active stable sites from numerous nanopores. As a result, its electrodes show a high CO selectivity of 85.8% at a low overpotential of 0.264 V and efficient mass activity that is up to 3.96 times higher than that of de-alloyed nanoporous Au electrodes. “These results are expected to solve the problem of mass transfer in the field of similar electrochemical reactions and can be applied to a wide range of green energy applications for the efficient utilization of electrocatalysts,” said the researchers. This work was supported by the National Research Foundation (NRF) of Korea. Image credit: Professor Seokwoo Jeon and Professor Jihun Oh, KAIST Image usage restrictions: News organizations may use or redistribute this image, with proper attribution, as part of news coverage of this paper only. Publication: Hyun et al. (2020) Hierarchically porous Au nanostructures with interconnected channels for efficient mass transport in electrocatalytic CO2 reduction. Proceedings of the National Academy of Sciences of the USA (PNAS). Available online at https://doi.org/10.1073/pnas.1918837117 Profile: Seokwoo Jeon, PhD Professor firstname.lastname@example.org http://fdml.kaist.ac.kr Department of Materials Science and Engineering (MSE) https://www.kaist.ac.kr Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea Profile: Jihun Oh, PhD Associate Professor email@example.com http://les.kaist.ac.kr Department of Materials Science and Engineering (MSE) Department of Energy, Environment, Water and Sustainability (EEWS) KAIST Profile: Gayea Hyun PhD Candidate firstname.lastname@example.org http://fdml.kaist.ac.kr Flexible Devices and Metamaterials Laboratory (FDML) Department of Materials Science and Engineering (MSE) KAIST Profile: Jun Tae Song, PhD Assistant Professor email@example.com http://www.cstf.kyushu-u.ac.jp/~ishihara-lab/ Department of Applied Chemistry https://www.kyushu-u.ac.jp Kyushu UniversityFukuoka, Japan (END)
Engineered Microbial Production of Grape Flavoring
(Image 1: Engineered bacteria that produce grape flavoring.) Researchers report a microbial method for producing an artificial grape flavor. Methyl anthranilate (MANT) is a common grape flavoring and odorant compound currently produced through a petroleum-based process that uses large volumes of toxic acid catalysts. Professor Sang-Yup Lee’s team at the Department of Chemical and Biomolecular Engineering demonstrated production of MANT, a naturally occurring compound, via engineered bacteria. The authors engineered strains of Escherichia coli and Corynebacetrium glutamicum to produce MANT through a plant-based engineered metabolic pathway. The authors tuned the bacterial metabolic pathway by optimizing the levels of AAMT1, the key enzyme in the process. To maximize production of MANT, the authors tested six strategies, including increasing the supply of a precursor compound and enhancing the availability of a co-substrate. The most productive strategy proved to be a two-phase extractive culture, in which MANT was extracted into a solvent. This strategy produced MANT on the scale of 4.47 to 5.74 grams per liter, a significant amount, considering that engineered microbes produce most natural products at a scale of milligrams or micrograms per liter. According to the authors, the results suggest that MANT and other related molecules produced through industrial processes can be produced at scale by engineered microbes in a manner that would allow them to be marketed as natural one, instead of artificial one. This study, featured at the Proceeding of the National Academy of Sciences of the USA on May 13, was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries from the Ministry of Science and ICT. (Image 2. Overview of the strategies applied for the microbial production of grape flavoring.)
A Novel Biosensor to Advance Diverse High-Level Production of Microbial Cell Factories
A research group at KAIST presented a novel biosensor which can produce diverse, high-level microbial cell factories. The biosensor monitors the concentration of products and even intermediates when new strains are being developed. This strategy provides a new platform for manufacturing diverse natural products from renewable resources. The team succeeded in creating four natural products of high-level pharmaceutical importance with this strategy. Malonyl-CoA is a major building block for many value-added chemicals including diverse natural products with pharmaceutical importance. However, due to the low availability of malonyl-CoA in bacteria, many malonyl-CoA-derived natural products have been produced by chemical synthesis or extraction from natural resources that are harmful to the environment and are unsustainable. For the sustainable biological production of malonyl-CoA-derived natural products, increasing the intracellular malonyl-CoA pool is necessary. To this end, the development of a robust and efficient malonyl-CoA biosensor was required to monitor the concentration of intracellular malonyl-CoA abundance as new strains are developed. Metabolic engineering researchers at KAIST addressed this issue. This research reports the development of a simple and robust malonyl-CoA biosensor by repurposing a type III polyketide synthase (also known as RppA), which produces flaviolin, a colorimetric indicator of malonyl-CoA. Subsequently, the RppA biosensor was used for the rapid and efficient colorimetric screening of gene manipulation targets enabling enhanced malonyl-CoA abundance. The screened beneficial gene targets were employed for the high-level production of four representative natural products derived from malonyl-CoA. Compared with the previous strategies, which were expensive and time-consuming, the new biosensor could be easily applied to industrially relevant bacteria including Escherichia coli, Pseudomonas putida, and Corynebacterium glutamicum to enable a one-step process. The study employs synthetic small regulatory RNA (sRNA) technology to rapidly and efficiently reduce endogenous target gene expression for improved malonyl-CoA production. The researchers constructed an E. coli genome-scale synthetic sRNA library targeting 1,858 genes covering all major metabolic genes in E. coli. This library was employed with the RppA biosensor to screen for gene targets which are believed to be beneficial for enhancing malonyl-CoA accumulation upon their expression knockdown. From this colorimetric screening, 14 gene targets were selected, all of which were successful at significantly increasing the production of four natural products (6-methylsalicylic acid, aloesone, resveratrol, and naringenin). Although specific examples are demonstrated in E. coli as a host, the researchers showed that the biosensor is also functional in P. putida and C. glutamicum, industrially important representative gram-negative and gram-positive bacteria, respectively. The malonyl-CoA biosensor developed in this research will serve as an efficient platform for the rapid development of strains capable of producing natural products crucial for the pharmaceutical, chemical, cosmetics, and food industries. An important aspect of this work is that the high-performance strains constructed in this research were developed rapidly and easily by utilizing the simple approach of colorimetric screening, without involving extensive metabolic engineering approaches. 6-Methylsalicylic acid (an antibiotic) could be produced to the highest titer reported for E. coli, and the microbial production of aloesone (a precursor of aloesin, an anti-inflammatory agent/whitening agent) was achieved for the first time. “A sustainable process for producing diverse natural products using renewable resources is of great interest. This study represents the development of a robust and efficient malonyl-CoA biosensor generally applicable to a wide range of industrially important bacteria. The capability of this biosensor for screening a large library was demonstrated to show that the rapid and efficient construction of high-performance strains is feasible. This research will be useful for further accelerating the development process of strains capable of producing valuable chemicals to industrially relevant levels,” said Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering, who led the research. This study entitled “Repurposing type III polyketide synthase as a malonyl-CoA biosensor for metabolic engineering in bacteria,” was published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on October 02. PhD students Dongsoo Yang and Won Jun Kim, MS student Shin Hee Ha, research staff Mun Hee Lee, Research Professor Seung Min Yoo, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering and Dr. Jong Hyun Choi of the Applied Microbiology Research Center at the Korea Research Institute of Bioscience and Biotechnology (KRIBB) participated in this research. Figure: Type III polyketide synthase (RppA) as a malonyl-CoA biosensor. RppA converts five molecules of malonyl-CoA into one molecule of red-colored flaviolin. This schematic diagram shows the overall conceptualization of the malonyl-CoA biosensor by indicating that higher malonyl-CoA abundance leads to higher production and secretion of flaviolin, resulting in a deeper red color of the culture. This system was employed for the enhanced production of four representative natural products (6-methylsalicylic acid, aloesone, resveratrol, and naringenin) from engineered E. coli strains.
Recombinant E. Coli As a Biofactory for the Biosynthesis of Diverse Nanomaterials
(Distinguished Professor Lee and PhD candidate Choi) A metabolic research group at KAIST and Chung-Ang University in Korea has developed a recombinant E. coli strain that biosynthesizes 60 different nanomaterials covering 35 elements on the periodic table. Among the elements, the team could biosynthesize 33 novel nanomaterials for the first time, advancing the forward design of nanomaterials through the biosynthesis of various single and multi-elements. The study analyzed the nanomaterial biosynthesis conditions using a Pourbaix diagram to predict the producibility and crystallinity. Researchers studied a Pourbaix diagram to predict the stable chemical species of each element for nanomaterial biosynthesis at varying levels of reduction potential (Eh) and pH. Based on the Pourbaix diagram analyses, the initial pH of the reaction was changed from 6.5 to 7.5, resulting in the biosynthesis of various crystalline nanomaterials that were previously amorphous or not synthesized. This strategy was extended to biosynthesize multi-element nanomaterials. Various single and multi-element nanomaterials biosynthesized in this research can potentially serve as new and novel nanomaterials for industrial applications such as catalysts, chemical sensors, biosensors, bioimaging, drug delivery, and cancer therapy. A research group consisting of PhD candidate Yoojin Choi, Associate Professor Doh Chang Lee, and Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST and Associate Professor Tae Jung Park of the Department of Chemistry at Chung-Ang University reported the synthesis. This study, entitled “Recombinant Escherichia coli as a biofactory for various single- and multi-element nanomaterials,” was published online in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on May 21. A recent successful biosynthesis of nanomaterials under mild conditions without requiring physical and chemical treatments has triggered the exploration of the full biosynthesis capacity of a biological system for producing a diverse range of nanomaterials as well as for understanding biosynthesis mechanisms for crystalline versus amorphous nanomaterials. There has been increased interest in synthesizing various nanomaterials that have not yet been synthesized for various applications including semiconducting materials, enhanced solar cells, biomedical materials, and many others. This research reports the construction of a recombinant E. coli strain that co-expresses metallothionein, a metal binding protein, and phytochelatin synthase that synthesizes the metal-binding peptide phytochelatin for the biosynthesis of various nanomaterials. Subsequently, an E. coli strain was engineered to produce a diverse range of nanomaterials, including those never biosynthesized before, by using 35 individual elements from the periodic table and also by combining multi-elements. Distinguished Professor Lee said, “An environmentally-friendly and sustainable process is of much interest for producing nanomaterials by not only chemical and physical methods but biological synthesis. Moreover, there has been much attention paid to producing diverse and novel nanomaterials for new industrial applications. This is the first report to predict the biosynthesis of various nanomaterials, by far the largest number of various single- and multi-elements nanomaterials. The strategies used for nanomaterial biosynthesis in this research will be useful for further diversifying the portfolio of nanomaterials that can be manufactured.” Figure: The biosynthesis of diverse nanomaterials using recombinant E. coli. This schematic diagram shows the overall conceptualization of the biosynthesis of various single and multi-element nanomaterials using recombinant E. coli under incubation with corresponding elemental precursors. The 35 elements that were tested to biosynthesize nanomaterials are shown in black circles on the periodic table.
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), with causal mechanisms often unknown. However, current prediction methods do not provide sufficient details beyond the chance of DDI occurrence, or require detailed drug information often unavailable for DDI prediction. To tackle this problem, Dr. Jae Yong Ryu, Assistant Professor Hyun Uk Kim and Distinguished Professor Sang Yup Lee, all from the Department of Chemical and Biomolecular Engineering at Korea Advanced Institute of Science and Technology (KAIST), developed a computational framework, named DeepDDI, that accurately predicts 86 DDI types for a given drug pair. The research results were published online in Proceedings of the National Academy of Sciences of the United States of America (PNAS) on April 16, 2018, which is entitled “Deep learning improves prediction of drug-drug and drug-food interactions.” DeepDDI takes structural information and names of two drugs in pair as inputs, and predicts relevant DDI types for the input drug pair. DeepDDI uses deep neural network to predict 86 DDI types with a mean accuracy of 92.4% using the DrugBank gold standard DDI dataset covering 192,284 DDIs contributed by 191,878 drug pairs. Very importantly, DDI types predicted by DeepDDI are generated in the form of human-readable sentences as outputs, which describe changes in pharmacological effects and/or the risk of ADEs as a result of the interaction between two drugs in pair. For example, DeepDDI output sentences describing potential interactions between oxycodone (opioid pain medication) and atazanavir (antiretroviral medication) were generated as follows: “The metabolism of Oxycodone can be decreased when combined with Atazanavir”; and “The risk or severity of adverse effects can be increased when Oxycodone is combined with Atazanavir”. By doing this, DeepDDI can provide more specific information on drug interactions beyond the occurrence chance of DDIs or ADEs typically reported to date. DeepDDI was first used to predict DDI types of 2,329,561 drug pairs from all possible combinations of 2,159 approved drugs, from which DDI types of 487,632 drug pairs were newly predicted. Also, DeepDDI can be used to suggest which drug or food to avoid during medication in order to minimize the chance of adverse drug events or optimize the drug efficacy. To this end, DeepDDI was used to suggest potential causal mechanisms for the reported ADEs of 9,284 drug pairs, and also predict alternative drug candidates for 62,707 drug pairs having negative health effects to keep only the beneficial effects. Furthermore, DeepDDI was applied to 3,288,157 drug-food constituent pairs (2,159 approved drugs and 1,523 well-characterized food constituents) to predict DFIs. The effects of 256 food constituents on pharmacological effects of interacting drugs and bioactivities of 149 food constituents were also finally predicted. All these prediction results can be useful if an individual is taking medications for a specific (chronic) disease such as hypertension or diabetes mellitus type 2. Distinguished Professor Sang Yup Lee said, “We have developed a platform technology DeepDDI that will allow precision medicine in the era of Fourth Industrial Revolution. DeepDDI can serve to provide important information on drug prescription and dietary suggestions while taking certain drugs to maximize health benefits and ultimately help maintain a healthy life in this aging society.” Figure 1. Overall scheme of Deep DDDI and prediction of food constituents that reduce the in vivo concentration of approved drugs
Distinguished Professor Lee Elected to the NAS
Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering was elected as a foreign associate to the US National Academy of Sciences (NAS) on May 2. The National Academy of Sciences elected 84 new members and 21 foreign associates in recognition of their distinguished and continuing achievements in their original research. Election to the Academy is widely regarded as one of the highest honors that a scientist can receive. Professor Lee was also elected in 2010 as a member of the US National Academy of Engineering (NAE) for his leadership in microbial biotechnology and metabolic engineering, including the development of fermentation processes for biodegradable polymers and organic acids. Until 2016, there are only 12 people worldwide who are foreign associates of both NAS and NAE. He is the first Korean elected to both prestigious academies, the NAS and the NAE in the US. Professor Lee is currently the dean of KAIST Institutes, the world leading institute for multi-and interdisciplinary research. He is also serving as co-chair of the Global Council on Biotechnology and member of the Global Future Council on the Fourth Industrial Revolution, the World Economic Forum.
Professor Seyun Kim Identifies a Neuron Signal Controlling Molecule
A research team led by Professor Seyun Kim of the Department of Biological Sciences at KAIST has identified inositol pyrophosphates as the molecule that strongly controls neuron signaling via synaptotagmin. Professors Tae-Young Yoon of Yonsei University’s Y-IBS and Sung-Hyun Kim of Kyung Hee University’s Department of Biomedical Science also joined the team. The results were published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS) on June 30, 2016. This interdisciplinary research project was conducted by six research teams from four different countries and covered a wide scope of academic fields, from neurobiology to super resolution optic imaging. Inositol pyrophosphates such as 5-diphosphoinositol pentakisphos-phate (5-IP7), which naturally occur in corns and beans, are essential metabolites in the body. In particular, inositol hexakisphosphate (IP6) has anti-cancer properties and is thought to have an important role in cell signaling. Inositol pentakisphosphate (IP7) differs from IP6 by having an additional phosphate group, which was first discovered 20 years ago. IP7 has recently been identified as playing a key role in diabetes and obesity. Psychopathy and neurodegenerative diseases are known to result from the disrupted balance of inositol pyrophosphates. However, the role and the mechanism of action of IP7 in brain neurons and nerve transmission remained unknown. Professor Kim’s team has worked on inositol pyrophosphates for several years and discovered that very small quantities of IP7 control cell-signaling transduction. Professor Yoon of Yonsei University identified IP7 as a much stronger inhibitor of neuron signaling compared to IP6. In particular, IP7 directly suppresses synaptotagmin, one of the key proteins in neuron signaling. Moreover, Professor Kim of Kyung Hee University observed IP7 inhibition in sea horse neurons. Together, the joint research team identified inositol pyrophosphates as the key switch metabolite of brain-signaling transduction. The researchers hope that future research on synaptotagmin and IP7 will reveal the mechanism of neuron-signal transduction and thus enable the treatment of neurological disorders. These research findings were the result of cooperation of various science and technology institutes: KAIST, Yonsei-IBS (Institute for Basic Science), Kyung Hee University, Sungkyunkwan University, KIST, University of Zurich in Switzerland, and Albert-Ludwigs-University Freiburg in Germany. Schematic Image of Controlling the Synaptic Exocytotic Pathway by 5-IP7 , Helping the Understanding of the Signaling Mechanisms of Inositol Pyrophosphates
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