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通过序列统计和机器学习实现脂质发现。

Lipid discovery enabled by sequence statistics and machine learning.

作者信息

Christensen Priya M, Martin Jonathan, Uppuluri Aparna, Joyce Luke R, Wei Yahan, Guan Ziqiang, Morcos Faruck, Palmer Kelli L

出版信息

bioRxiv. 2024 Aug 19:2023.10.12.562061. doi: 10.1101/2023.10.12.562061.

DOI:10.1101/2023.10.12.562061
PMID:37873101
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10592805/
Abstract

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococ-cal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in , diglucosyl-diacylglycerol (Glc -DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

摘要

细菌膜复杂且动态,源于一系列进化压力。一种通过共价脂质修饰改变膜组成的酶是MprF。我们最近发现,MprF可由阴离子磷脂酰甘油(PG)合成赖氨酰磷脂酰甘油(Lys-PG),并由中性糖脂葡萄糖二酰甘油(Glc-DAG)合成一种新型阳离子脂质,赖氨酰葡糖基二酰甘油(Lys-Glc-DAG)。这一意外结果促使我们研究Lys-Glc-DAG是否存在于其他含MprF的细菌中,以及是否存在其他新型MprF产物。在此,我们研究了决定MprF底物特异性的蛋白质序列特征。首先,成对分析确定了几种合成Lys-Glc-DAG的链球菌MprF。其次,一种受限玻尔兹曼机引导的方法使我们在 中发现了MprF的一种全新底物,二葡糖基二酰甘油(Glc₂-DAG),以及一组使用MprF修饰糖脂底物的扩展生物体。总体而言,我们将丰富的可用序列数据与机器学习相结合,以模拟细菌域中MprF序列的进化限制,从而鉴定出一种新型阳离子脂质。

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Lipid discovery enabled by sequence statistics and machine learning.通过序列统计和机器学习实现脂质发现。
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