Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis.
Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis.
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DOI：
10.1186/1471-2229-12-142
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发表时间：
2012-08-11
影响因子：
5.3
通讯作者：
Reumann S
中科院分区：
文献类型：
--
作者：
Chowdhary G;Kataya AR;Lingner T;Reumann S
高精度的预测工具是必不可少的后基因组时代，以确定其完整的复杂性细胞器蛋白质组。我们最近应用了一种判别式机器学习方法来预测植物蛋白携带过氧化物酶体靶向信号（PTS）类型1从基因组序列。对于拟南芥，预测392个基因模型是过氧化物酶体靶向的。这些预测在体内进行了广泛的测试，导致了以前不知道是过氧化物酶体的拟南芥蛋白质的高实验验证率。在这项研究中，我们通过实验更深入地验证了预测，重点是最具挑战性的拟南芥蛋白，具有未知的非经典PTS 1三肽和接近阈值的预测分数。通过体内亚细胞靶向分析，三种新的PTS 1三肽（QRL>、SQM>和SDL>）和两种新的三肽残基（-3位的Q和pos.（2）被发现。为了理解为什么在许多携带相同C-末端三肽的拟南芥蛋白质中，这些蛋白质被特异性地预测为过氧化物酶体，计算置换PTS 1三肽上游的残基，并分析预测分数的变化。新鉴定的拟南芥蛋白质被发现在非经典PTS 1三肽前的-4至12位含有4至5个具有高预测靶向增强特性的氨基酸残基。预测的靶向增强残基的身份出乎意料地多样化，除了碱性残基之外，还包括脯氨酸、羟基化（Ser、Thr）、疏水性（Ala、瓦尔）、甚至酸性残基。我们的计算和实验分析表明，植物PTS 1三肽基序比以前认为的更多样化，包括越来越多的非典型序列和允许的残基。特异性靶向增强元件可以针对特定的感兴趣序列进行预测，并且在氨基酸组成和定位方面比先前假设的更加多样化。机器学习方法对于预测在携带相同的非经典PTS 1三肽的众多候选蛋白质中哪些特定蛋白质在数量、定位和总强度方面包含足够的增强子元件以引起过氧化物酶体靶向是必不可少的。
High-accuracy prediction tools are essential in the post-genomic era to define organellar proteomes in their full complexity. We recently applied a discriminative machine learning approach to predict plant proteins carrying peroxisome targeting signals (PTS) type 1 from genome sequences. For Arabidopsis thaliana 392 gene models were predicted to be peroxisome-targeted. The predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. In this study, we experimentally validated the predictions in greater depth by focusing on the most challenging Arabidopsis proteins with unknown non-canonical PTS1 tripeptides and prediction scores close to the threshold. By in vivo subcellular targeting analysis, three novel PTS1 tripeptides (QRL>, SQM>, and SDL>) and two novel tripeptide residues (Q at position −3 and D at pos. -2) were identified. To understand why, among many Arabidopsis proteins carrying the same C-terminal tripeptides, these proteins were specifically predicted as peroxisomal, the residues upstream of the PTS1 tripeptide were computationally permuted and the changes in prediction scores were analyzed. The newly identified Arabidopsis proteins were found to contain four to five amino acid residues of high predicted targeting enhancing properties at position −4 to −12 in front of the non-canonical PTS1 tripeptide. The identity of the predicted targeting enhancing residues was unexpectedly diverse, comprising besides basic residues also proline, hydroxylated (Ser, Thr), hydrophobic (Ala, Val), and even acidic residues. Our computational and experimental analyses demonstrate that the plant PTS1 tripeptide motif is more diverse than previously thought, including an increasing number of non-canonical sequences and allowed residues. Specific targeting enhancing elements can be predicted for particular sequences of interest and are far more diverse in amino acid composition and positioning than previously assumed. Machine learning methods become indispensable to predict which specific proteins, among numerous candidate proteins carrying the same non-canonical PTS1 tripeptide, contain sufficient enhancer elements in terms of number, positioning and total strength to cause peroxisome targeting.
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影响因子：
4.9
作者：
Kamada, T;Nito, K;Nishimura, M
通讯作者：
Nishimura, M
影响因子：
5.6
作者：
Neuberger, G;Maurer-Stroh, S;Eisenhaber, F
通讯作者：
Eisenhaber, F
DOI：
10.1073/pnas.95.22.13336
发表时间：
1998-10-27
影响因子：
11.1
作者：
Kragler, F;Lametschwandtner, G;Harada, JJ
通讯作者：
Harada, JJ
影响因子：
4
作者：
Chen, Jun;Stefano, Giovanni;Zheng, Huanquan
通讯作者：
Zheng, Huanquan
DOI：
10.1046/j.1432-1327.2000.01053.x
发表时间：
2000-02-01
期刊：
EUROPEAN JOURNAL OF BIOCHEMISTRY
影响因子：
--
作者：
Diefenbach, J;Kindl, H
通讯作者：
Kindl, H