Reverse Engineering: Learning from Proteins How to Enhance the Performance of Synthetic Nanosystems

被引:0
|
作者
Viola Vogel
机构
来源
MRS Bulletin | 2002年 / 27卷
关键词
cell signaling; forced-unfolding pathways; molecular shuttles; motor proteins; nanosystems; specific recognition; structural fluctuations; structure/function relations; tissue engineering;
D O I
暂无
中图分类号
学科分类号
摘要
Proteins are nature’s workhorses. They enable living systems to use available energy sources and convert energy from one form into another. Understanding the underlying design principles of how proteins have evolved to fulfill the necessary functions of life can provide researchers with new insights into how to enhance the performance of synthetic nanosystems with far greater sophistication. This review summarizes the relationship between various protein functions and the underlying engineering principles of their overall structures. For example, proteins can specifically recognize other biomolecules with a selectivity and affinity several orders of magnitude superior to their synthetic counterparts. Mimicking a protein binding site with a structurally fixed synthetic analogue is insufficient, since structural changes in the active sites enhance molecular recognition and the catalytic activity of proteins. Recent data also show that protein function can be switched by stretching proteins into nonequilibrium states under physiological conditions. Schemes by which the exposure and structure of recognition sites are switched can be implemented in the design of mechanically responsive synthetic and hybrid systems. Motor proteins, finally, are the jewel in nature’s crown, as they can convert one free-energy form into another to generate mechanical force. It is thus of considerable interest to integrate the chemically powered engines into synthetic materials and devices. Finally, we have to advance our ability to assemble nanocomponents into functional systems. Again, lessons can be learned from how biology solves the challenge of systems integration.
引用
收藏
页码:972 / 978
页数:6
相关论文
共 50 条
  • [2] Reverse engineering ontologies from performance systems
    Richards, D
    RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEM XIX, 2003, : 193 - 206
  • [3] How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?
    Alam, Manaar
    Mukhopadhyay, Debdeep
    PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC), 2019,
  • [4] Learning from nature to enhance Blue engineering of marine infrastructure
    Bugnot, A. B.
    Mayer-Pinto, M.
    Johnston, E. L.
    Schaefer, N.
    Dafforn, K. A.
    ECOLOGICAL ENGINEERING, 2018, 120 : 611 - 621
  • [5] How innovativeness and handedness affect learning performance of engineering students?
    Law, Kris M. Y.
    Geng, Shuang
    INTERNATIONAL JOURNAL OF TECHNOLOGY AND DESIGN EDUCATION, 2019, 29 (04) : 897 - 914
  • [6] How innovativeness and handedness affect learning performance of engineering students?
    Kris M. Y. Law
    Shuang Geng
    International Journal of Technology and Design Education, 2019, 29 : 897 - 914
  • [7] A COMPARED APPROACH ON HOW DEEP LEARNING MAY SUPPORT REVERSE ENGINEERING FOR TOLERANCE INSPECTION
    Bici, Michele
    Mohammadi, Saber Seyed
    Campana, Francesca
    PROCEEDINGS OF THE ASME INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, 2019, VOL 14, 2020,
  • [8] Implementation of Active Learning Methods in Mechanical Engineering Education to Enhance Students' Performance
    Tembe, B. L.
    Kamble, S. K.
    PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS (COMPSAC), VOL 2, 2016, : 258 - 263
  • [9] Performing engineering: How the performance metaphor for engineering can transform communications learning and teaching
    Evans, Rick
    Gabriel, Jerry
    2007 37TH ANNUAL FRONTIERS IN EDUCATION CONFERENCE, GLOBAL ENGINEERING : KNOWLEDGE WITHOUT BORDERS - OPPORTUNITIES WITHOUT PASSPORTS, VOLS 1- 4, 2007, : 376 - +
  • [10] Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction
    Lopez-Chacon, Sergio Ricardo
    Salazar, Fernando
    Blade, Ernest
    WATER, 2023, 15 (11)