Agricultural & Biological Engineering, Purdue University, 225 S University St, West Lafayette, IN, 47907, USA.
School of Chemical Engineering, Oklahoma State University, 420 Engineering North, Stillwater, OK, 74078, USA.
Biotechnol J. 2021 Apr;16(4):e2000311. doi: 10.1002/biot.202000311. Epub 2020 Nov 13.
Biomolecules are increasingly attractive templates for the synthesis of functional nanomaterials. Chief among them is the plant tobacco mosaic virus (TMV) due to its high aspect ratio, narrow size distribution, diverse biochemical functionalities presented on the surface, and compatibility with a number of chemical conjugations. These properties are also easily manipulated by genetic modification to enable the synthesis of a range of metallic and non-metallic nanomaterials for diverse applications. This article reviews the characteristics of TMV and related viruses, and their virus-like particle (VLP) derivatives, and how these may be manipulated to extend their use and function. A focus of recent efforts has been on greater understanding and control of the self-assembly processes that drive biotemplate formation. How these features have been exploited in engineering applications such as, sensing, catalysis, and energy storage are briefly outlined. While control of VLP surface features is well-established, fewer tools exist to control VLP self-assembly, which limits efforts to control template uniformity and synthesis of certain templated nanomaterials. However, emerging advances in synthetic biology, machine learning, and other fields promise to accelerate efforts to control template uniformity and nanomaterial synthesis enabling more widescale industrial use of VLP-based biotemplates.
生物分子作为合成功能纳米材料的模板越来越受到关注。其中，烟草花叶病毒（TMV）因其高纵横比、窄的尺寸分布、表面呈现出多样化的生化功能以及与多种化学偶联物的兼容性而成为主要选择。这些特性也很容易通过遗传修饰来操纵，从而能够合成一系列用于各种应用的金属和非金属纳米材料。本文综述了 TMV 及其相关病毒和它们的病毒样颗粒（VLP）衍生物的特性，以及如何操纵这些特性来扩展它们的用途和功能。最近的研究重点是更深入地了解和控制驱动生物模板形成的自组装过程。简要概述了这些特性如何在传感、催化和储能等工程应用中得到利用。尽管 VLP 表面特性的控制已经得到很好的建立，但控制 VLP 自组装的工具较少，这限制了对模板均匀性和某些模板化纳米材料合成的控制。然而，合成生物学、机器学习和其他领域的新兴进展有望加速对模板均匀性和纳米材料合成的控制，从而使基于 VLP 的生物模板能够更广泛地应用于工业领域。
