BGFE: A Deep Learning Model for ncRNA-Protein Interaction Predictions Based on Improved Sequence Information

The interactions between ncRNAs and proteins are critical for regulating various cellular processes in organisms, such as gene expression regulations. However, due to limitations, including financial and material consumptions in recent experimental methods for predicting ncRNA and protein interactions, it is essential to propose an innovative and practical approach with convincing performance of prediction accuracy. In this study, based on the protein sequences from a biological perspective, we put forward an effective deep learning method, named BGFE, to predict ncRNA and protein interactions. Protein sequences are represented by bi-gram probability feature extraction method from Position Specific Scoring Matrix (PSSM), and for ncRNA sequences, k-mers sparse matrices are employed to represent them. Furthermore, to extract hidden high-level feature information, a stacked auto-encoder network is employed with the stacked ensemble integration strategy. We evaluate the performance of the proposed method by using three datasets and a five-fold cross-validation after classifying the features through the random forest classifier. The experimental results clearly demonstrate the effectiveness and the prediction accuracy of our approach. In general, the proposed method is helpful for ncRNA and protein interacting predictions and it provides some serviceable guidance in future biological research.

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Amalgamation of 3D structure and sequence information for protein–protein interaction prediction

Scientific Reports ◽

10.1038/s41598-020-75467-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kanchan Jha ◽

Sriparna Saha

Keyword(s):

Amino Acids ◽

Deep Learning ◽

Protein Interactions ◽

3D Structure ◽

Protein Sequences ◽

Building Blocks ◽

New Drugs ◽

Sequence Information ◽

Protein Protein Interactions ◽

Structure Information

Abstract Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein–protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein–protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

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Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

Briefings in Bioinformatics ◽

10.1093/bib/bbz107 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1798-1805 ◽

Cited By ~ 1

Author(s):

Kai Yu ◽

Qingfeng Zhang ◽

Zekun Liu ◽

Yimeng Du ◽

Xinjiao Gao ◽

...

Keyword(s):

Deep Learning ◽

Protein Interactions ◽

Web Server ◽

Data Sets ◽

Lysine Acetylation ◽

Protein Acetylation ◽

Protein Protein Interactions ◽

Cellular Processes ◽

Protein Lysine Acetylation ◽

Specific Regulation

Abstract Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein–protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

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lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning

International Journal of Molecular Sciences ◽

10.3390/ijms21155222 ◽

2020 ◽

Vol 21 (15) ◽

pp. 5222 ◽

Cited By ~ 1

Author(s):

Xiao-Nan Fan ◽

Shao-Wu Zhang ◽

Song-Yao Zhang ◽

Jin-Jie Ni

Keyword(s):

Deep Learning ◽

Prediction Accuracy ◽

Protein Coding ◽

Learning Framework ◽

Alignment Free ◽

Independent Test ◽

Non Coding Rnas ◽

High Level ◽

Fold Cross Validation ◽

Human Complex

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs.

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Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins

10.1101/168120 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mohammad Nauman ◽

Hafeez Ur Rehman ◽

Gianfranco Politano ◽

Alfredo Benso

Keyword(s):

Deep Learning ◽

Protein Interactions ◽

Protein Function ◽

Sequence Data ◽

3D Structure ◽

Input Sequence ◽

Sequence Information ◽

Large Set ◽

Automated Annotation ◽

Protein Functions

ABSTRACTAccurate annotation of protein functions is important for a profound understanding of molecular biology. A large number of proteins remain uncharacterized because of the sparsity of available supporting information. For a large set of uncharacterized proteins, the only type of information available is their amino acid sequence. In this paper, we propose DeepSeq – a deep learning architecture – that utilizes only the protein sequence information to predict its associated functions. The prediction process does not require handcrafted features; rather, the architecture automatically extracts representations from the input sequence data. Results of our experiments with DeepSeq indicate significant improvements in terms of prediction accuracy when compared with other sequence-based methods. Our deep learning model achieves an overall validation accuracy of 86.72%, with an F1 score of 71.13%. Moreover, using the automatically learned features and without any changes to DeepSeq, we successfully solved a different problem i.e. protein function localization, with no human intervention. Finally, we discuss how this same architecture can be used to solve even more complicated problems such as prediction of 2D and 3D structure as well as protein-protein interactions.

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lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

10.21203/rs.2.16792/v2 ◽

2020 ◽

Author(s):

Xiao-Nan Fan ◽

Shao-Wu Zhang ◽

Song-Yao Zhang ◽

Jin-Jie Ni

Keyword(s):

Deep Learning ◽

Prediction Accuracy ◽

Protein Coding ◽

Learning Framework ◽

Alignment Free ◽

Independent Test ◽

Non Coding Rnas ◽

High Level ◽

Fold Cross Validation ◽

Human Complex

Abstract Background: Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. Results: In this study, we present an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporates three different input modalities (i.e. OFH modality, k-mer modality, and sequence modality), then a multimodal deep learning framework is built for learning the high-level abstract representations and predicting the probability whether a transcript is lncRNA or not. Conclusions: LncRNA_Mdeep achieves 98.73% prediction accuracy in 10-fold cross-validation test on human. Compared with other eight state-of-the-art methods, lncRNA_Mdeep shows 93.12% prediction accuracy independent test on human, which is 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets show that lncRNA_Mdeep is a powerful predictor for identifying lncRNAs. The source code can be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep.

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RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

International Journal of Molecular Sciences ◽

10.3390/ijms20051070 ◽

2019 ◽

Vol 20 (5) ◽

pp. 1070 ◽

Cited By ~ 12

Author(s):

Cheng Peng ◽

Siyu Han ◽

Hui Zhang ◽

Ying Li

Keyword(s):

Neural Network ◽

Deep Learning ◽

Protein Interaction ◽

Rna Binding ◽

Prediction Models ◽

Rna Binding Proteins ◽

Biological Research ◽

Sequence Information ◽

Learning Framework ◽

Coding Method

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA–protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA–protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA–protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA–protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA–protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the k-mer features of RNA and protein sequence. The improved CTF coding method showed performance gain compared with the original CTF method. Moreover, our designed RPITER performed well in predicting RNA–protein interaction (RPI) and could outperform most of the previous methods. On five widely used RPI datasets, RPI369, RPI488, RPI1807, RPI2241 and NPInter, RPITER obtained A U C of 0.821, 0.911, 0.990, 0.957 and 0.985, respectively. The proposed RPITER could be a complementary method for predicting RPI and constructing RPI network, which would help push forward the related biological research on ncRNAs and lncRNAs.

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Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area

Remote Sensing ◽

10.3390/rs13173436 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3436

Author(s):

Yao Li ◽

Peng Cui ◽

Chengming Ye ◽

José Marcato Junior ◽

Zhengtao Zhang ◽

...

Keyword(s):

Deep Learning ◽

Prediction Accuracy ◽

Large Scale ◽

Source Area ◽

Spatial Prediction ◽

Disaster Mitigation ◽

Support Vector ◽

Learning Models ◽

Elevation Model ◽

High Level

An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.

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A deep learning framework for improving protein interaction prediction using sequence properties

10.1101/843755 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yi Guo ◽

Xiang Chen

Keyword(s):

Deep Learning ◽

Cellular Networks ◽

Protein Interactions ◽

Computational Method ◽

Sequence Information ◽

Protein Protein Interactions ◽

Learning Framework ◽

Amino Acid Properties ◽

Wide Scale ◽

Almost All

AbstractMotivationAlmost all critical functions and processes in cells are sustained by the cellular networks of protein-protein interactions (PPIs), understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack high-quality PPI data for constructing the networks, which makes it challenging to study the functions of association of proteins. High-throughput experimental techniques have produced abundant data for systematically studying the cellular networks of a biological system and the development of computational method for PPI identification.ResultsWe have developed a deep learning-based framework, named iPPI, for accurately predicting PPI on a proteome-wide scale depended only on sequence information. iPPI integrates the amino acid properties and compositions of protein sequence into a unified prediction framework using a hybrid deep neural network. Extensive tests demonstrated that iPPI can greatly outperform the state-of-the-art prediction methods in identifying PPIs. In addition, the iPPI prediction score can be related to the strength of protein-protein binding affinity and further showed the biological relevance of our deep learning framework to identify PPIs.Availability and ImplementationiPPI is available as an open-source software and can be downloaded from https://github.com/model-lab/[email protected]

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