通过机器学习算法,通过酒精脱酶减少异质亲基质催化物的变化
Arindam Ghatak1, Anirudh P Shanbhag2, Santanu Datta2
1Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, India; Biomoneta Research Pvt. Ltd., C-CAMP, National Centre for Biological Sciences (NCBS), UAS GKVK, Bangalore, 560065, India.
Biochemical and biophysical research communications
|November 27, 2023
概括
机器学习准确地预测酒精脱酶 (ADHs) 对于性合成生产. 这种方法可以确定理想的酶-基质配对,减少了在制药中间体合成中需要广泛的选和酶工程的需要.
科学领域:
- 生物催化剂是一种生物催化剂.
- 酶工程是什么?酶工程是什么?
- 机器学习在化学中的应用
背景情况:
- 酒精脱酶 (ADHs) 是生产性合成子的关键生物催化剂,对于活性药物成分 (API) 制造至关重要.
- 目前的酶基质匹配方法涉及资源密集的选或酶工程.
- 抗氧化被分为三个超级家族:中链 (MDR),短链脱酶/还原酶 (SDR) 和含铁的抗氧化,其中MDR和SDR被广泛使用.
研究的目的:
- 开发一种机器学习模型,用于预测最佳的酒精脱酶 (ADH) 酶-基质配对.
- 解读284种抗菌中间体和MDR/SDR之间的活性模式.
- 为了减少对高通量选和酶工程在生物催化剂选择的依赖.
主要方法:
- 策划了284种抗菌基中间体的数据集,其中33种具有特征.
- 采用了一组机器学习技术:部分最小平方 (PLS) 回归,k-最近邻居 (kNN) 回归和支持向量机器 (SVM) 回归.
- 利用主要组件分析 (PCA) 来提高模型准确性和划分化合物类别.
主要成果:
- 在特定的ADH和抗菌中间体之间确定了独特的活性模式.
- 证明了机器学习在预测酶基质兼容性方面的有效性.
- 成功地根据它们对不同ADHs的适应性来分类化合物类别.
结论:
- 开发的机器学习模型准确地预测了适合特定基质的酒精脱化.
- 这种方法为传统的高通量选和酶工程提供了更有效的替代方案.
- 促进合理选择生物催化剂用于制药制造中的性合成生产.
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