DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning

被引:6
作者
Du, XiuQuan [1 ,2 ]
Zhao, XiuJuan [2 ]
Zhang, YanPing [1 ]
机构
[1] Anhui Univ, Key Lab Intelligent Comp & Signal Proc, Minist Educ, Hefei 230601, Anhui, Peoples R China
[2] Anhui Univ, Sch Comp Sci & Technol, Hefei 230601, Anhui, Peoples R China
关键词
RNA-binding proteins; deep learning; ensemble learning; SEQUENCE; IDENTIFICATION; NETWORKS; ACCURATE; PROFILE; SITES;
D O I
10.1142/S0219720022500068
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence. Facing this challenge, we present a novel method called DeepBtoD, which predicts RBPs directly from RNA sequences. First, a k-BtoD encoding is designed, which takes into account the composition of k-nucleotides and their relative positions and forms a local module. Second, we designed a multi-scale convolutional module embedded with a self-attentive mechanism, the ms-focusCNN, which is used to further learn more effective, diverse, and discriminative high-level features. Finally, global information is considered to supplement local modules with ensemble learning to predict whether the target RNA binds to RBPs. Our preliminary 24 independent test datasets show that our proposed method can classify RBPs with the area under the curve of 0.933. Remarkably, DeepBtoD shows competitive results across seven state-of-the-art methods, suggesting that RBPs can be highly recognized by integrating local k-BtoD and global information only from RNA sequences. Hence, our integrative method may be useful to improve the power of RBPs prediction, which might be particularly useful for modeling protein-nucleic acid interactions in systems biology studies. Our DeepBtoD server can be accessed at http://175.27.228.227/DeepBtoD/.
引用
收藏
页数:30
相关论文
共 54 条
[1]   Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning [J].
Alipanahi, Babak ;
Delong, Andrew ;
Weirauch, Matthew T. ;
Frey, Brendan J. .
NATURE BIOTECHNOLOGY, 2015, 33 (08) :831-+
[2]   doRiNA: a database of RNA interactions in post-transcriptional regulation [J].
Anders, Gerd ;
Mackowiak, Sebastian D. ;
Jens, Marvin ;
Maaskola, Jonas ;
Kuntzagk, Andreas ;
Rajewsky, Nikolaus ;
Landthaler, Markus ;
Dieterich, Christoph .
NUCLEIC ACIDS RESEARCH, 2012, 40 (D1) :D180-D186
[3]  
[Anonymous], 2020, SCI REP-UK
[4]   Identification of RNA-protein interaction networks using PAR-CLIP [J].
Ascano, Manuel ;
Hafner, Markus ;
Cekan, Pavol ;
Gerstberger, Stefanie ;
Tuschl, Thomas .
WILEY INTERDISCIPLINARY REVIEWS-RNA, 2012, 3 (02) :159-177
[5]  
Bahdanau D., ARXIV
[6]   The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts [J].
Baltz, Alexander G. ;
Munschauer, Mathias ;
Schwanhaeusser, Bjoern ;
Vasile, Alexandra ;
Murakawa, Yasuhiro ;
Schueler, Markus ;
Youngs, Noah ;
Penfold-Brown, Duncan ;
Drew, Kevin ;
Milek, Miha ;
Wyler, Emanuel ;
Bonneau, Richard ;
Selbach, Matthias ;
Dieterich, Christoph ;
Landthaler, Markus .
MOLECULAR CELL, 2012, 46 (05) :674-690
[7]   Insights into RNA Biology from an Atlas of Mammalian mRNA-Binding Proteins [J].
Castello, Alfredo ;
Fischer, Bernd ;
Eichelbaum, Katrin ;
Horos, Rastislav ;
Beckmann, Benedikt M. ;
Strein, Claudia ;
Davey, Norman E. ;
Humphreys, David T. ;
Preiss, Thomas ;
Steinmetz, Lars M. ;
Krijgsveld, Jeroen ;
Hentze, Matthias W. .
CELL, 2012, 149 (06) :1393-1406
[8]  
Choong ACH, 2017, 2017 FIRST INTERNATIONAL CONFERENCE ON COMPUTER AND DRONE APPLICATIONS (ICONDA), P60, DOI 10.1109/ICONDA.2017.8270400
[9]   Recent progress in protein subcellular location prediction [J].
Chou, Kuo-Chen ;
Shen, Hong-Bin .
ANALYTICAL BIOCHEMISTRY, 2007, 370 (01) :1-16
[10]   Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network [J].
Chung, Taesu ;
Kim, Dongsup .
PLOS ONE, 2019, 14 (04)