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174 results on '"Choi, Yoonjoo"'

11. Ultrathin, High‐Aspect‐Ratio Bismuth Sulfohalide Nanowire Bundles for Solution‐Processed Flexible Photodetectors.

Author

Lee, Da Won, Oh, Seongkeun, Lee, Dong Hyun David, Woo, Ho Young, Ahn, Junhyuk, Kim, Seung Hyeon, Jung, Byung Ku, Choi, Yoonjoo, Kim, Dagam, Yu, Mi Yeon, Park, Chun Gwon, Yun, Hongseok, Kim, Tae‐Hyung, Han, Myung Joon, Oh, Soong Ju, and Paik, Taejong

Subjects
SEMICONDUCTOR nanowires, OPTOELECTRONIC devices, QUANTUM efficiency, BISMUTH, PHOTODETECTORS, NANOWIRES
Abstract

In this study, a novel synthesis of ultrathin, highly uniform colloidal bismuth sulfohalide (BiSX where X = Cl, Br, I) nanowires (NWs) and NW bundles (NBs) for room‐temperature and solution‐processed flexible photodetectors are presented. High‐aspect‐ratio bismuth sulfobromide (BiSBr) NWs are synthesized via a heat‐up method using bismuth bromide and elemental S as precursors and 1‐dodecanethiol as a solvent. Bundling of the BiSBr NWs occurs upon the addition of 1‐octadecene as a co‐solvent. The morphologies of the BiSBr NBs are easily tailored from sheaf‐like structures to spherulite nanostructures by changing the solvent ratio. The optical bandgaps are modulated from 1.91 (BiSCl) and 1.88 eV (BiSBr) to 1.53 eV (BiSI) by changing the halide compositions. The optical bandgap of the ultrathin BiSBr NWs and NBs exhibits blueshift, whose origin is investigated through density functional theory‐based first‐principles calculations. Visible‐light photodetectors are fabricated using BiSBr NWs and NBs via solution‐based deposition followed by solid‐state ligand exchanges. High photo‐responsivities and external quantum efficiencies (EQE) are obtained for BiSBr NW and NB films even under strain, which offer a unique opportunity for the application of the novel BiSX NWs and NBs in flexible and environmentally friendly optoelectronic devices. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Assessing the predictive ability of computational epitope prediction methods on Fel d 1 and other allergens.

Author

Kwon, Hyeji, Ko, Soobon, Ha, Kyungsoo, Lee, Jungjoon K., and Choi, Yoonjoo

Subjects
ALLERGENS, EPITOPES, T cells, DATABASES, FORECASTING
Abstract

While computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Pan-cancer analysis reveals multifaceted roles of retrotransposon-fusion RNAs

Author

Lee, Boram, primary, Park, Junseok, additional, Voshall, Adam, additional, Maury, Eduardo, additional, Kang, Yeeok, additional, Kim, Yoen Jeong, additional, Lee, Jin-Young, additional, Shim, Hye-Ran, additional, Kim, Hyo-Ju, additional, Lee, Jung-Woo, additional, Jung, Min-Hyeok, additional, Kim, Si-Cho, additional, Khanh Chu, Hoang Bao, additional, Kim, Da-Won, additional, Kim, Minjeong, additional, Choi, Eun-Ji, additional, Hwang, Ok Kyung, additional, Lee, Ho Won, additional, Ha, Kyungsoo, additional, Choi, Jung Kyoon, additional, Kim, Yongjoon, additional, Choi, Yoonjoo, additional, Park, Woong-Yang, additional, and Lee, Eunjung Alice, additional

Published
2023
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16. Protein loop structure prediction

Author

Choi, Yoonjoo and Deane, Charlotte

Subjects
572.6, Bioinformatics (life sciences), Bioinformatics (biochemistry), Protein folding, Statistics (see also social sciences), protein, protein structure prediction, computational biology, bioinformatics, protein loop structure prediction, protein loop
Abstract

This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.

Published
2011

21. Abstract 1122: Pan-cancer analysis reveals roles of retrotransposon-fusion RNAs

Author

Lee, E. Alice, primary, Lee, Boram, additional, Choi, Yoonjoo, additional, Park, Junseok, additional, Voshall, Adam, additional, Maury, Eduardo, additional, Kang, Yeeok, additional, Kim, Yeon Jeong, additional, Lee, Jin-Young, additional, Shim, Hye-Ran, additional, Kim, Si-Cho, additional, Chu, Hoang Bao Khanh, additional, Kim, Da-Won, additional, Kim, Minjeong, additional, Choi, Eun-Ji, additional, Ha, Kyungsoo, additional, Choi, Jung Kyoon, additional, Kim, Yongjoon, additional, and Park, Woong-Yang, additional

Published
2023
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46. Computer-aided Binding Mode Prediction and Affinity Maturation of LRR Protein Binder without Structural Determination

Author

Choi, Yoonjoo, Jeong, Sukyo, Choi, Jung-Min, Ndong, Christian, Bailey-Kellogg, Chris, Griswold, Karl E., and Kim, Hak-Sung

Subjects
Energy minimization, Computational biology, Binding selectivity, Ligand (biochemistry), Genetics, Epitope, Protein engineering, Structural biology, Affinity maturation, Biology
Abstract

Binding affinity maturation without structure determination remains a difficult challenge in the computer-aided protein engineering. Precise binding mode identification is a vital prerequisite for the affinity maturation. However, pure computational methods have been unreliable in practice so far and experimental structural biology techniques are generally too costly. Herein, we show that computational epitope localization followed by the full-atom energy minimization with intermediate experimental validation can yield precisely bound complex model structure, which ultimately enables effective affinity maturation and redesign of binding specificity. As a proof-of-concept, we targeted a leucine-rich repeat (LRR) protein binder which specifically binds to the human IgG1 (hIgG1). Based on the computationally predicted binding mode of the LRR protein binder to hIgG1, the binding affinity of the protein binder was significantly increased and its specificity was redesigned toward multiple IgGs from other species. Experimental determination of the complex structure showed that the predicted model closely matched the X-ray crystal structure. Through the benchmark of therapeutically relevant existing LRR protein complexes, we demonstrate that the present approach can be broadly applicable to other proteins which undergo small conformational changes upon binding.Significance StatementComputer-aided design of binding affinity and specificity of two interacting proteins without structural determination is a challenging problem in protein engineering. Despite recent advances in the computational biology techniques, however, in silico evaluation of binding energies has proven to be extremely difficult. We show that, in the cases of protein-protein interactions where only small structural changes upon binding occur, partial experimental validation of binding can greatly complement the computational energy. Using an LRR (leucine-rich repeat) protein binder as a model system, the binding orientation of two binding partners was precisely dentified by the hybrid approach. Based on the predicted model, we could successfully redesign the binding affinity and specificity of the protein binder by the full-atom energy calculation.

Published
2019
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