Výsledky vyhledávání - "Pin-Kuang Lai"

Akademický článek

Rapid discovery of Transglutaminase 2 inhibitors for celiac disease with boosting ensemble machine learning

Publikováno v: Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 3669-3679 (2024)

Celiac disease poses a significant health challenge for individuals consuming gluten-containing foods. While the availability of gluten-free products has increased, there is still a need for therapeutic treatments. The advancement of computational dr

Externí odkaz: https://doaj.org/article/cf856de826aa42019b81ec5742025b4b

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Akademický článek

DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

Autor: Lateefat Kalejaye, I-En Wu, Taylor Terry, Pin-Kuang Lai

Publikováno v: Computational and Structural Biotechnology Journal, Vol 23, Iss , Pp 2220-2229 (2024)

Therapeutic antibody development faces challenges due to high viscosities and aggregation tendencies. The spatial charge map (SCM) and spatial aggregation propensity (SAP) are computational techniques that aid in predicting viscosity and aggregation,

Externí odkaz: https://doaj.org/article/fadb7dbfba6a45bd8604a5eb6936ffd2

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Akademický článek

DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Autor: Pin-Kuang Lai

Publikováno v: Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 2143-2152 (2022)

Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers

Externí odkaz: https://doaj.org/article/17fa73f9859340e29970e123fa131faa

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Akademický článek

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Autor: Pin-Kuang Lai, Austin Gallegos, Neil Mody, Hasige A. Sathish, Bernhardt L. Trout

Publikováno v: mAbs, Vol 14, Iss 1 (2022)

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this

Externí odkaz: https://doaj.org/article/f4ca71bf37414333844364400a211ee9

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Akademický článek

Differences in human IgG1 and IgG4 S228P monoclonal antibodies viscosity and self-interactions: Experimental assessment and computational predictions of domain interactions

Autor: Pin-Kuang Lai, Gaurav Ghag, Yao Yu, Veronica Juan, Laurence Fayadat-Dilman, Bernhardt L. Trout

Publikováno v: mAbs, Vol 13, Iss 1 (2021)

Human/humanized IgG4 antibodies have reduced effector function relative to IgG1 antibodies, which is desirable for certain therapeutic purposes. However, the developability and biophysical properties for IgG4 antibodies are not well understood. This

Externí odkaz: https://doaj.org/article/944b83f5f34449e7a67f388df2b8013b

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Akademický článek

Calculation of therapeutic antibody viscosity with coarse-grained models, hydrodynamic calculations and machine learning-based parameters

Autor: Pin-Kuang Lai, James W. Swan, Bernhardt L. Trout

Publikováno v: mAbs, Vol 13, Iss 1 (2021)

High viscosity presents a challenge for manufacturing and drug delivery of therapeutic antibodies. The viscosity is determined by protein–protein interactions among many antibodies. Molecular simulation is a promising method to study protein–prot

Externí odkaz: https://doaj.org/article/a8d3524eb652421da213579340202493

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Akademický článek

Insights into Membrane Translocation of Protegrin Antimicrobial Peptides by Multistep Molecular Dynamics Simulations

Autor: Pin-Kuang Lai, Yiannis N. Kaznessis

Publikováno v: ACS Omega, Vol 3, Iss 6, Pp 6056-6065 (2018)

Externí odkaz: https://doaj.org/article/87a679bbc6c04511a36d0566c8d6adcd

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Dissertation/ Thesis

Rapid Determination of entropy and free energy of complex fluids from Molecular Dynamic Simulation

Autor: Pin-Kuang Lai, 賴品光

101
The two-phase thermodynamic (2PT) model is a new method which utilizes the results from molecular dynamic simulation to determine thermodynamic properties rapidly and accurately, especially entropy and free energy. In this method, the vibrat

Externí odkaz: http://ndltd.ncl.edu.tw/handle/40536058783603776312

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