自愈水凝胶
定制
序列(生物学)
两亲性
分子动力学
材料科学
折叠(DSP实现)
计算机科学
纳米技术
化学
聚合物
共聚物
工程类
生物化学
有机化学
高分子化学
计算化学
电气工程
政治学
法学
作者
Diego López Barreiro,Abel Folch‐Fortuny,Iain Muntz,Jens Thies,Cees M. J. Sagt,Gijsje H. Koenderink
出处
期刊:Biomacromolecules
[American Chemical Society]
日期:2022-12-14
卷期号:24 (1): 489-501
被引量:8
标识
DOI:10.1021/acs.biomac.2c01405
摘要
The biofabrication of structural proteins with controllable properties via amino acid sequence design is interesting for biomedicine and biotechnology, yet a complete framework that connects amino acid sequence to material properties is unavailable, despite great progress to establish design rules for synthesizing peptides and proteins with specific conformations (e.g., unfolded, helical, β-sheets, or β-turns) and intermolecular interactions (e.g., amphipathic peptides or hydrophobic domains). Molecular dynamics (MD) simulations can help in developing such a framework, but the lack of a standardized way of interpreting the outcome of these simulations hinders their predictive value for the design of de novo structural proteins. To address this, we developed a model that unambiguously classifies a library of de novo elastin-like polypeptides (ELPs) with varying numbers and locations of hydrophobic/hydrophilic and physical/chemical-cross-linking blocks according to their thermoresponsiveness at physiological temperature. Our approach does not require long simulation times or advanced sampling methods. Instead, we apply (un)supervised data analysis methods to a data set of molecular properties from relatively short MD simulations (150 ns). We also experimentally investigate hydrogels of those ELPs from the library predicted to be thermoresponsive, revealing several handles to tune their mechanical and structural properties: chain hydrophilicity/hydrophobicity or block distribution control the viscoelasticity and thermoresponsiveness, whereas ELP concentration defines the network permeability. Our findings provide an avenue to accelerate the design of de novo ELPs with bespoke phase behavior and material properties.
科研通智能强力驱动
Strongly Powered by AbleSci AI