김완규 교수

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of int

Protein-protein interaction plays a critical role in biological processes. The identification of interacting proteins by computational methods can provide new leads in functional studies of uncharacterized proteins without performing extensive experiments. We developed a database for the potentially interacting domain pairs (PID) extracted from a dataset of experimentally identified interacting protein pairs (DIP: database of interacting proteins) with InterPro, an integrated database of protein

Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To

A systematic classification of protein-protein interfaces is a valuable resource for understanding the principles of molecular recognition and for modelling protein complexes. Here, we present a classification of domain interfaces according to their geometry. Our new algorithm uses a hybrid approach of both sequential and structural features. The accuracy is evaluated on a hand-curated dataset of 416 interfaces. Our hybrid procedure achieves 83% precision and 95% recall, which improves the earli

An in silico chemical genomics approach is developed to predict drug repositioning (DR) candidates for three types of cancer: glioblastoma, lung cancer, and breast cancer. It is based on a recent large-scale dataset of ~20,000 drug-induced expression profiles in multiple cancer cell lines, which provides i) a global impact of transcriptional perturbation of both known targets and unknown off-targets, and ii) rich information on drug's mode-of-action. First, the drug-induced expression profile is

Proteins interact in complex protein-protein interaction (PPI) networks whose topological properties-such as scale-free topology, hierarchical modularity, and dissortativity-have suggested models of network evolution. Currently preferred models invoke preferential attachment or gene duplication and divergence to produce networks whose topology matches that observed for real PPIs, thus supporting these as likely models for network evolution. Here, we show that the interaction density and homodime

The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from availabl

In radio-frequency identification (RFID) system, a directional coupler has been used to isolate RX from TX due to its simplicity and low cost compared with a circulator. Nevertheless, a conventional microstrip directional coupler has inherent drawback of poor isolation due to unequal phase velocity between even and odd mode. In this paper, a newly proposed circulator using a microstrip directional coupler is proposed to achieve good isolation between TX and RX. The proposed circulator is used fo

The results of co-evolution analysis are available online at http://www.biointeraction.org

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important questio

Considering the limited success of the most sophisticated docking methods available and the amount of computation required for systematic docking, cataloging all the known interfaces may be an alternative basis for the prediction of protein tertiary and quaternary structures. We classify domain interfaces according to the geometry of domain-domain association. By applying a simple and efficient method called "interface tag clustering," more than 4,000 distinct types of domain interfaces are coll

In this paper, a newly invented circulator for T/R switch of UHF radio-frequency identification (RFID) application is proposed to overcome TX-to-RX leakage problem. A microstrip coupled-line directional coupler is used for this passive circulator and the isolation characteristic is drastically improved not by complex method such as compensating phase velocity, but by considering both directional coupler itself and imperfect input impedance of the employed antenna. The measurement result of the p

The DJ-1 superfamily (DJ-1/ThiJ/PfpI superfamily) is distributed across all three kingdoms of life. These proteins are involved in a highly diverse range of cellular functions, including chaperone and protease activity. DJ-1 proteins usually form dimers or hexamers in vivo and show at least four different binding orientations via distinct interface patches. Abnormal oligomerization of human DJ-1 is related to neurodegenerative disorders including Parkinson's disease, suggesting important functio