Publications by authors named "Jongham Park"

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to enhance the efficacy and safety of novel therapeutic agents. The capacity to identify molecular pathways that contribute to drug-induced toxicity has been significantly enhanced by recent developments in omics technologies, such as transcriptomics, proteomics, and metabolomics. This has enabled the early identification of potential adverse effects.

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The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design.

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Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of mass spectrometry (MS), particularly when coupled with liquid chromatography (LC), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized-medicine advancement.

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Article Synopsis
  • Therapeutic protein engineering is transforming medicine by creating highly targeted treatments for various diseases, focusing on areas like antibody engineering and enzyme therapies.
  • Recent advancements combine computational methods, such as structure-based design and machine learning, with experimental techniques like directed evolution to improve protein therapeutics.
  • Despite significant progress, challenges like predicting in vivo behavior and manufacturing remain, necessitating further integration of computational and experimental approaches for future breakthroughs in treatment development.
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  • - Recent advancements in experimental techniques and computational methods have transformed our understanding of protein dynamics, highlighting motions from tiny atomic vibrations to significant structural changes.
  • - Tools like nuclear magnetic resonance spectroscopy and molecular dynamics simulations allow for detailed observation of protein movements, while machine learning innovations like AlphaFold2 improve predictions of protein structures and dynamics.
  • - Understanding these dynamics is crucial for aspects such as allosteric regulation, enzyme function, and drug design, representing a major shift in fields like structural biology and biocatalyst development.
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Article Synopsis
  • - The introduction highlights the importance of disease screening to prevent symptoms, but acknowledges challenges like diagnostic errors, patient discomfort, and complications from concurrent screenings.
  • - The research proposes using Selected Reaction Monitoring (SRM) assays for multi-disease screening, which could reduce demands on healthcare systems, despite facing obstacles like detecting low-abundance proteins and model overfitting.
  • - The expert opinion suggests that advancements in mass spectrometry in clinical labs are poised to enhance diagnostic accuracy and efficiency, making it a crucial tool for precise medical testing.
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Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand.

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