生物合成基因集群的计算进步发现和预测
Sisi Zhu1, Hongquan Xu2, Yuhong Liu1
1Key Laboratory of Elemene Class Anti-cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China.
Biotechnology advances
|February 9, 2025
概括
生物合成基因集群 (BGCs) 的基因组挖掘使用计算工具来发现新的自然产品. 包括机器学习在内的人工智能显著提高了识别这些关键基因组的速度和准确性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 生物合成基因集群 (BGCs) 是微生物和植物中必不可少的遗传元素,负责产生各种二次代谢产物.
- 来自BGCs的二次代谢产物对于天然产品的发现和新治疗剂的开发至关重要.
- 传统的BGC识别实验方法往往是耗时和劳动密集的.
研究的目的:
- 提供当前BGC数据库和预测工具的全面概述.
- 突出人工智能,特别是机器学习对BGC采矿的变革性影响.
- 讨论BGC发现中的计算方法的挑战和未来前景.
主要方法:
- 审查BGC数据库和计算预测工具的现有文献.
- 分析机器学习和深度学习算法在BGC识别中的应用和进展.
- 综合当前的挑战和该领域未来的研究方向.
主要成果:
- 计算方法,特别是人工智能驱动的方法,已经大大提高了BGC识别和注释的效率和精度.
- 有许多BGC数据库和预测工具可用,为研究人员提供有价值的资源.
- 机器学习模型在加速从BGCs中发现新型二次代谢物的过程中显示出显著的希望.
结论:
- 人工智能和机器学习正在彻底改变BGC采矿,使天然产品的发现更快,更准确.
- 持续开发BGC数据库和计算工具对于推进天然产品研究至关重要.
- 解决当前的计算挑战将进一步释放BGC挖矿用于药物开发的潜力.
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