EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning
EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on ensemble learning
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DOI：
10.1016/j.crbiot.2021.12.002
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发表时间：
2022-01-01
影响因子：
5.6
通讯作者：
Maranas, Costas D.
中科院分区：
文献类型：
--
作者：
Banerjee, Deepro;Jindra, Michael A.;Maranas, Costas D.
植物酰基acp硫酯酶（TEs）是微生物宿主中用于生产可再生油脂化学品的关键酶类，实验表征其功能特性可能是一个昂贵且耗时的过程，因为它需要在数据库中手动筛选数千个候选酶。利用氨基酸序列来计算预测酶的功能可能会加速这一过程；然而，通过基于标准机器学习（ML）的方法来准确推断序列-函数关系所需的先前表征酶及其各自序列的必要数量的信息可能是禁止的，特别是在低通量测试周期下。实验噪声，不平衡的数据集，其中高序列相似性并不总是意味着相同的功能属性，将进一步阻碍稳健的预测性能。在这里，我们提出了一种ML方法，酶分类集成方法（酶类），这是专门设计来解决这些问题。我们使用酶类将TEs分为短、长和混合游离脂肪酸底物特异性三类。虽然之前已经提出了推断底物特异性的一般准则，但从植物酰基- acp TEs的一级序列预测链长偏好仍然难以捉摸。通过将酶类应用于百里香数据库中的一个TEs子集，我们确定了两个中链te， ClFatB3和CwFatB2，它们在大肠杆菌脂肪酸生产宿主中具有先前未被鉴定的活性。酶类可以很容易地应用于其他蛋白质分类挑战，并可在https:// github.com/deeprob/ThioesteraseEnzymeSpecificity。
Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme's function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts. EnZymClass can be readily applied to other protein classification challenges and is available at: https:// github.com/deeprob/ThioesteraseEnzymeSpecificity.