Sowlati-Hashjin Shahin, Gandhi Aanshi, Garton Michael
Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON, Canada, M5S 3E2.
Biodes Res. 2022 Apr 15;2022:9791435. doi: 10.34133/2022/9791435. eCollection 2022.
A major advancement has recently occurred in the ability to predict protein secondary structure from sequence using artificial neural networks. This new accessibility to high-quality predicted structures provides a big opportunity for the protein design community. It is particularly welcome for membrane protein design, where the scarcity of solved structures has been a major limitation of the field for decades. Here, we review the work done to date on the membrane protein design and set out established and emerging tools that can be used to most effectively exploit this new access to structures.
最近,利用人工神经网络从序列预测蛋白质二级结构的能力取得了重大进展。这种获得高质量预测结构的新途径为蛋白质设计领域提供了巨大机遇。对于膜蛋白设计而言,这尤其值得欢迎,因为几十年来,已解析结构的匮乏一直是该领域的主要限制因素。在此,我们回顾了迄今为止在膜蛋白设计方面所做的工作,并列出了已确立的和新兴的工具,这些工具可用于最有效地利用这种获取结构的新途径。