蓝图
计算机科学
一套
数据科学
领域(数学)
生物信息学
蛋白质结构预测
人工智能
计算生物学
蛋白质结构
工程类
化学
生物
机械工程
生物化学
数学
考古
基因
纯数学
历史
作者
Zhiwei Yang,Gerald H. Lushington
标识
DOI:10.2174/0113862073189534250217114015
摘要
The innovation in antibody and protein design highlights the transformation from empirical approaches to sophisticated strategies integrating computational methods and artificial intelligence (AI). Key principles, such as combinatorial, structure-based, consensus, and computational designs, have been pivotal in predicting structures from sequences (in silico design). Advances in tools, like AlphaFold and Rosetta suite, enable accurate structure prediction, facilitating the development of functional proteins and antibodies. However, challenges remain, including improving prediction accuracy, modeling flexible regions, understanding structural dynamics, and designing catalytic and binding sites. Despite these, the field promises groundbreaking advancements in biomedical sciences, enriching our understanding and serving human health and scientific discovery.
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