Academic Journal

A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging

التفاصيل البيبلوغرافية
العنوان: A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging
المؤلفون: Pouyan Asgharzadeh, Annette I. Birkhold, Zubin Trivedi, Bugra Özdemir, Ralf Reski, Oliver Röhrle
المصدر: Computational and Structural Biotechnology Journal, Vol 18, Iss , Pp 2774-2788 (2020)
بيانات النشر: Elsevier
سنة النشر: 2020
المجموعة: Directory of Open Access Journals: DOAJ Articles
مصطلحات موضوعية: Structure-function relationship, Protein network, Machine learning, Finite element analysis, Confocal imaging, Biotechnology, TP248.13-248.65
الوصف: Sub-cellular mechanics plays a crucial role in a variety of biological functions and dysfunctions. Due to the strong structure-function relationship in cytoskeletal protein networks, light can be shed on their mechanical functionality by investigating their structures. Here, we present a data-driven approach employing a combination of confocal live imaging of fluorescent tagged protein networks, in silico mechanical experiments and machine learning to investigate this relationship. Our designed image processing and nanoFE mechanical simulation framework resolves the structure and mechanical behaviour of cytoskeletal networks and the developed gradient boosting surrogate models linking network structure to its functionality. In this study, for the first time, we perform mechanical simulations of Filamentous Temperature Sensitive Z (FtsZ) complex protein networks with realistic network geometry depicting its skeletal functionality inside organelles (here, chloroplasts) of the moss Physcomitrella patens. Training on synthetically produced simulation data enables predicting the mechanical characteristics of FtsZ network purely based on its structural features (R2⩾0.93), therefore allowing to extract structural principles enabling specific mechanical traits of FtsZ, such as load bearing and resistance to buckling failure in case of large network deformation.
نوع الوثيقة: article in journal/newspaper
اللغة: English
تدمد: 2001-0370
Relation: http://www.sciencedirect.com/science/article/pii/S2001037020304086; https://doaj.org/toc/2001-0370; https://doaj.org/article/2e631368415241b6adcb7c931e4c9401
DOI: 10.1016/j.csbj.2020.09.024
الاتاحة: https://doi.org/10.1016/j.csbj.2020.09.024
https://doaj.org/article/2e631368415241b6adcb7c931e4c9401
رقم الانضمام: edsbas.E1FAF052
قاعدة البيانات: BASE
الوصف
تدمد:20010370
DOI:10.1016/j.csbj.2020.09.024