北京生物醫(yī)學(xué)工程

基于三維卷積神經(jīng)網(wǎng)絡(luò)的RNA結(jié)構(gòu)上鎂離子結(jié)合位點(diǎn)預(yù)測(cè)

Prediction of magnesium binding sites in RNA structures based onthree-dimensional convolutional neural network

作者：趙彥彭鞏衛(wèi)康劉洋王京京李春華

單位：北京工業(yè)大學(xué)環(huán)境與生命學(xué)部(北京100124) 通信作者:李春華。E-mail: chunhuali@ bjut. edu. cn <p> </p>

關(guān)鍵詞： RNA-Mg2+結(jié)合位點(diǎn);三維卷積神徑網(wǎng)絡(luò);RNA三維結(jié)構(gòu);特征貢獻(xiàn);局部坐標(biāo)系

分類(lèi)號(hào)：R318 <p> </p>

出版年·卷·期（頁(yè)碼）：2021·40·6（570-576）

摘要：

目的核糖核酸(ribonucleic acid,RNA)在許多生命過(guò)程中起著關(guān)鍵作用，它幾乎參與了遺傳信息轉(zhuǎn)錄和翻譯的各個(gè)方面。鎂離子(M/+)是RNA折疊成穩(wěn)定三級(jí)結(jié)構(gòu)所必需的，而且常常與核酶的催化活性有關(guān)。盡管實(shí)驗(yàn)解析的RNA結(jié)構(gòu)越來(lái)越多，但實(shí)驗(yàn)確定RNA結(jié)構(gòu)中的離子存在諸多困難。三維卷積神經(jīng)網(wǎng)絡(luò)(three-dimensional convolutional neural network,3D-CNN)能夠直接從原始數(shù)據(jù)中學(xué)習(xí) 有用的特征，已被應(yīng)用于一些基于結(jié)構(gòu)的預(yù)測(cè)工作。為此本文擬提出一種基于3D-CNN的RNA結(jié)構(gòu)上 Mg?+結(jié)合位點(diǎn)的預(yù)測(cè)方法RNAMgo方法首先收集蛋白質(zhì)數(shù)據(jù)庫(kù)(protein data bank,PDB)中的RNA結(jié) 構(gòu)構(gòu)建了高分辨率的非冗余RNA-M/+結(jié)合位點(diǎn)數(shù)據(jù)庫(kù)(113個(gè)結(jié)構(gòu))；隨后，對(duì)每個(gè)結(jié)合位點(diǎn)建立局部坐標(biāo)系，以周?chē)h(huán)境(碳、氧、氮、磷原子和電荷)構(gòu)建5個(gè)特征通道，將RNA三級(jí)結(jié)構(gòu)作為3D圖像送入3D-CNN模型預(yù)測(cè)RNA-Mg2+結(jié)合位點(diǎn);最后，使用五折交叉驗(yàn)證評(píng)估模型的性能。結(jié)果獨(dú)立測(cè)試集上的結(jié)果表明，在識(shí)別RNA結(jié)構(gòu)中的Mg，+位點(diǎn)上,RNAMg優(yōu)于目前最先進(jìn)的方法。結(jié)論RNAMg可以識(shí)別出RNA結(jié)構(gòu)中的M/+結(jié)合位點(diǎn),對(duì)理解RNA折疊、核酶催化反應(yīng)等各種關(guān)鍵生物學(xué)過(guò)程及相關(guān)疾病的產(chǎn)生機(jī)制有重要意義。

Objective Ribonucleic acids ( RNA) play an important role in many life processes. They take part in almost all the aspects of gene transcription and translation. Magnesium ions ( Mg2+ ) are essential for RNA folding into stable tertiary structures and are often involved in the catalytic activity of ribozymes. Despite many RNA structures have been resolved experimentally, accurately detecting them still faces many challenges experimentally. Three-dimensional convolutional neural network ( 3D - CNN) are capable of learning useful

features directly from raw data, and have been used in many structure-based prediction works. Therefore, this paper proposes a 3D - CNN based method for predicting Mg2+ binding sites in RNA structure, RNAMg. Methods Firstly, a database of RNA-Mg2+ binding sites (113 RNAs) is constructed by collecting RNA structures from PDB (protein data bank) database. Then, a local coordinate system is established for each binding site, the ambient microenvironment is represented as five ' channels' ( corresponding to atom types of carbon, oxygen, nitrogen and phosphorus and charge) ,and the tertiary structure of RNA is sent into the 3D-CNN model as a 3D image to predict the RNA-Mg2+ binding sites. In the end, the performance of the model is evaluated by 5-fold cross validation. Results Results on an independent test set show that RNAMg is superior to state-of-the-art methods in identifying Mg2+ binding sites in RNA structures. Conclusions RNAMg can identify the Mg2+ binding sites in RNA structures, which is of great significance in understanding various key biological processes such as RNA folding, ribozyme-catalyzed reactions and the generation mechanism of related diseases.

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