使用MIND-S进行翻译后修饰的预测,解释和突变评估的协议
Yu Yan1, Dean Wang2, Ruiqi Xin3
1NIH BRIDGE2AI Center at UCLA & NHLBI Integrated Cardiovascular Data Science Training Program at UCLA, Suite 1-609, MRL Building, 675 Charles E. Young Dr. South, Los Angeles, CA 90095-1760, USA; Medical Informatics Program, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA; Department of Physiology, UCLA School of Medicine, Suite 1-609, MRL Building, 675 Charles E. Young Dr., Los Angeles, CA 90095-1760, USA.
STAR protocols
|November 18, 2023
概括
本研究介绍了MIND-S,这是一种用于预测翻译后修改 (PTM) 的深度学习工具. MIND-S有助于识别关键氨基酸并了解突变如何影响PTM,这对疾病研究至关重要.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 翻译后修饰 (PTMs) 是细胞功能的关键调节者.
- PTMs的失调与各种人类疾病的发病有关.
- 准确预测和分析PTM对于了解细胞机制和疾病至关重要.
研究的目的:
- 为使用MIND-S (PTM预测结构版本的多标签可解释深度学习方法) 提出详细的协议.
- 为了证明MIND-S用于从蛋白质序列中预测PTM的应用.
- 展示MIND-S在识别关键氨基酸和分析突变诱导的PTM变化的能力.
主要方法:
- 开发和应用MIND-S计算工具.
- 利用深度学习进行多标签PTM预测.
- 基于序列的分析用于PTM站点识别.
- 对PTM监管的基于结构的见解.
主要成果:
- MIND-S为PTM分析提供了一个强大的协议.
- 该工具可以直接从蛋白质序列中准确预测PTM.
- MIND-S 促进了 PTM 中关键氨基酸残留物的识别.
- 该方法阐明了分子突变对PTM景观的影响.
结论:
- MIND-S协议为研究PTM提供了宝贵的资源.
- 这种深度学习方法提高了我们对PTM调节及其在疾病中的作用的理解.
- 在PTM分析中,MIND-S是基础研究和临床应用的强大工具.
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