使用BioNeMo折叠人类蛋白质组:用于机器学习目的的结构模型的融合数据集
Michael Hetmann1, Lena Parigger1, Hassan Sirelkhatim2
1Innophore, San Francisco, CA, USA.
Scientific data
|June 6, 2024
概括
研究人员创建了一个全面的数据集,预测人类蛋白质结构. 本资源有助于了解蛋白质功能,相互作用和药物设计,用于健康和疾病研究.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 结构生物学 结构生物学
- 生物信息学是一种生物信息学.
背景情况:
- 人类蛋白质对生物过程和疾病状态至关重要.
- 了解人类蛋白质的分子结构是生物研究的一个关键领域.
- 为了推进研究,需要预测人类蛋白质结构的全面数据集.
研究的目的:
- 为42,042种不同的人类蛋白质,包括拼接变体,提供一个广泛的预测蛋白质结构数据集.
- 提供使用最先进的建模工具生成的高质量,可比的数据集.
- 为了促进各种应用,如基于结构的药物设计和蛋白质功能和相互作用的预测.
主要方法:
- 利用来自NVIDIA的BioNeMo平台的AlphaFold 2,OpenFold和ESMFold进行蛋白质结构预测.
- 通过Innophore的CavitomiX平台使用同质模型.
- 生成未经编辑和编辑的数据集,包括精制的结构和预测的连接体复合体.
主要成果:
- 开发了一个包含42,042个人类蛋白质及其拼接变体的数据集.
- 结合了多个先进的建模工具,用于强大的结构预测.
- 提供了经过编辑的结构,提高了信心,并预测了连接体相互作用.
结论:
- 该数据集代表了目前可用的最全面的人类蛋白质结构集合.
- 该数据集将显著帮助基于结构的药物设计和蛋白质功能和相互作用的预测.
- 预计这种资源的可用性将加速生物研究和治疗开发.
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