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Zhao S, Cui Z, Zhang G, Gong Y, Su L. MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction. Front Genet 2024; 15:1440448. [PMID: 39076171 PMCID: PMC11284081 DOI: 10.3389/fgene.2024.1440448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 06/24/2024] [Indexed: 07/31/2024] Open
Abstract
Protein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferred due to their low cost and high accuracy. However, existing protein structure based methods are insufficient in the extraction of protein structural information. Furthermore, most methods are less interpretable, which hinder their practical application in the biomedical field. In this paper, we propose MGPPI, which is a Multiscale graph convolutional neural network model for PPI prediction. By incorporating multiscale module into the Graph Neural Network (GNN) and constructing multi convolutional layers, MGPPI can effectively capture both local and global protein structure information. For model interpretability, we introduce a novel visual explanation method named Gradient Weighted interaction Activation Mapping (Grad-WAM), which can highlight key binding residue sites. We evaluate the performance of MGPPI by comparing with state-of-the-arts methods on various datasets. Results shows that MGPPI outperforms other methods significantly and exhibits strong generalization capabilities on the multi-species dataset. As a practical case study, we predicted the binding affinity between the spike (S) protein of SARS-COV-2 and the human ACE2 receptor protein, and successfully identified key binding sites with known binding functions. Key binding sites mutation in PPIs can affect cancer patient survival statues. Therefore, we further verified Grad-WAM highlighted residue sites in separating patients survival groups in several different cancer type datasets. According to our results, some of the highlighted residues can be used as biomarkers in predicting patients survival probability. All these results together demonstrate the high accuracy and practical application value of MGPPI. Our method not only addresses the limitations of existing approaches but also can assists researchers in identifying crucial drug targets and help guide personalized cancer treatment.
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Affiliation(s)
| | | | | | | | - Lingtao Su
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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2
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Chen J, Ji Y, Liu Y, Cen Z, Chen Y, Zhang Y, Li X, Li X. Exhaled volatolomics profiling facilitates personalized screening for gastric cancer. Cancer Lett 2024; 590:216881. [PMID: 38614384 DOI: 10.1016/j.canlet.2024.216881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/02/2024] [Accepted: 04/09/2024] [Indexed: 04/15/2024]
Abstract
Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.
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Affiliation(s)
- Jian Chen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yongyan Ji
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yongqian Liu
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Zhengnan Cen
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China
| | - Yuanwen Chen
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China
| | - Yixuan Zhang
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China
| | - Xiaowen Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, 200040, PR China.
| | - Xiang Li
- Department of Environmental Science & Engineering, Fudan University, Shanghai, 200438, PR China.
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3
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Nada H, Kim S, Lee K. PT-Finder: A multi-modal neural network approach to target identification. Comput Biol Med 2024; 174:108444. [PMID: 38636325 DOI: 10.1016/j.compbiomed.2024.108444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.
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Affiliation(s)
- Hossam Nada
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Sungdo Kim
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Kyeong Lee
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
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4
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Li X, Wang GA, Wei Z, Wang H, Zhu X. Protein-DNA interface hotspots prediction based on fusion features of embeddings of protein language model and handcrafted features. Comput Biol Chem 2023; 107:107970. [PMID: 37866116 DOI: 10.1016/j.compbiolchem.2023.107970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 10/24/2023]
Abstract
The identification of hotspot residues at the protein-DNA binding interfaces plays a crucial role in various aspects such as drug discovery and disease treatment. Although experimental methods such as alanine scanning mutagenesis have been developed to determine the hotspot residues on protein-DNA interfaces, they are both inefficient and costly. Therefore, it is highly necessary to develop efficient and accurate computational methods for predicting hotspot residues. Several computational methods have been developed, however, they are mainly based on hand-crafted features which may not be able to represent all the information of proteins. In this regard, we propose a model called PDH-EH, which utilizes fused features of embeddings extracted from a protein language model (PLM) and handcrafted features. After we extracted the total 1141 dimensional features, we used mRMR to select the optimal feature subset. Based on the optimal feature subset, several different learning algorithms such as Random Forest, Support Vector Machine, and XGBoost were used to build the models. The cross-validation results on the training dataset show that the model built by using Random Forest achieves the highest AUROC. Further evaluation on the independent test set shows that our model outperforms the existing state-of-the-art models. Moreover, the effectiveness and interpretability of embeddings extracted from PLM were demonstrated in our analysis. The codes and datasets used in this study are available at: https://github.com/lixiangli01/PDH-EH.
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Affiliation(s)
- Xiang Li
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Gang-Ao Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Zhuoyu Wei
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Hong Wang
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui 230036, China.
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5
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Nikam R, Yugandhar K, Gromiha MM. DeepBSRPred: deep learning-based binding site residue prediction for proteins. Amino Acids 2023; 55:1305-1316. [PMID: 36574037 DOI: 10.1007/s00726-022-03228-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2022]
Abstract
MOTIVATION Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes. RESULTS We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods. AVAILABILITY AND IMPLEMENTATION The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .
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Affiliation(s)
- Rahul Nikam
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Kumar Yugandhar
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
- Department of Computational Biology, Cornell University, New York, NY, USA
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India.
- Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan.
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6
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Li K, Wu H, Yue Z, Sun Y, Xia C. A convolutional network and attention mechanism-based approach to predict protein-RNA binding residues. Comput Biol Chem 2023; 105:107901. [PMID: 37327559 DOI: 10.1016/j.compbiolchem.2023.107901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 06/18/2023]
Abstract
Protein-RNA interactions play a key role in various biological cellular processes, and many experimental and computational studies have been initiated to analyze their interactions. However, experimental determination is quite complex and expensive. Therefore, researchers have worked to develop efficient computational tools to detect protein-RNA binding residues. The accuracy of existing methods is limited by the features of the target and the performance of the computational models; there remains room for improvement. To solve the problem of the accurate detection of protein-RNA binding residues, we propose a convolutional network model named PBRPre based on improved MobileNet. First, by extracting the position information of the target complex and the 3-mer amino acid feature data, the position-specific scoring matrix (PSSM) is improved by using spatial neighbor smoothing processing and discrete wavelet transform to fully exploit the spatial structure information of the target and enrich the feature dataset. Second, the deep learning model MobileNet is used to integrate and optimize the potential features in the target complexes; then, by introducing the Vision Transformer (ViT) network classification layer, the deep-level information of the target is mined to enhance the processing ability of the model for global information and to improve the detection accuracy of the classifiers. The results show that the AUC value of the model can reach 0.866 in the independent testing dataset, which shows that PBRPre can effectively realize the detection of protein-RNA binding residues. All datasets and resource codes of PBRPre are available at https://github.com/linglewu/PBRPre for academic use.
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Affiliation(s)
- Ke Li
- School of Information & Computer, Anhui Agricultural University, Hefei, Anhui 230036, China; Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui 230601, China; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui 230036, China.
| | - Hongwei Wu
- School of Information & Computer, Anhui Agricultural University, Hefei, Anhui 230036, China; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Zhenyu Yue
- School of Information & Computer, Anhui Agricultural University, Hefei, Anhui 230036, China; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Yu Sun
- School of Information & Computer, Anhui Agricultural University, Hefei, Anhui 230036, China; Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui 230036, China
| | - Chuan Xia
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui 230036, China
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7
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Soleymani F, Paquet E, Viktor HL, Michalowski W, Spinello D. ProtInteract: A deep learning framework for predicting protein-protein interactions. Comput Struct Biotechnol J 2023; 21:1324-1348. [PMID: 36817951 PMCID: PMC9929211 DOI: 10.1016/j.csbj.2023.01.028] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/20/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.
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Affiliation(s)
- Farzan Soleymani
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Eric Paquet
- National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada,Corresponding author.
| | - Herna Lydia Viktor
- School of Electrical Engineering and Computer Science, University of Ottawa, ON K1N 6N5, Canada
| | | | - Davide Spinello
- Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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8
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Kang Y, Xu Y, Wang X, Pu B, Yang X, Rao Y, Chen J. HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction. Brief Bioinform 2023; 24:6833645. [PMID: 36403092 DOI: 10.1093/bib/bbac480] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 09/16/2022] [Accepted: 10/09/2022] [Indexed: 11/21/2022] Open
Abstract
MOTIVATION Biological experimental approaches to protein-protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL) techniques, the most popular Convolutional Neural Networks (CNN)-based methods have been proposed to address these problems. Although significant progress has been made, these methods still have limitations in encoding the characteristics of each amino acid in protein sequences. Current methods cannot efficiently explore the nature of Position Specific Scoring Matrix (PSSM), secondary structure and raw protein sequences by processing them all together. For PPI site prediction, how to effectively model the PPI context with attention to prediction remains an open problem. In addition, the long-distance dependencies of PPI features are important, which is very challenging for many CNN-based methods because the innate ability of CNN is difficult to outperform auto-regressive models like Transformers. RESULTS To effectively mine the properties of PPI features, a novel hybrid neural network named HN-PPISP is proposed, which integrates a Multi-layer Perceptron Mixer (MLP-Mixer) module for local feature extraction and a two-stage multi-branch module for global feature capture. The model merits Transformer, TextCNN and Bi-LSTM as a powerful alternative for PPI site prediction. On the one hand, this is the first application of an advanced Transformer (i.e. MLP-Mixer) with a hybrid network for sequence-based PPI prediction. On the other hand, unlike existing methods that treat global features altogether, the proposed two-stage multi-branch hybrid module firstly assigns different attention scores to the input features and then encodes the feature through different branch modules. In the first stage, different improved attention modules are hybridized to extract features from the raw protein sequences, secondary structure and PSSM, respectively. In the second stage, a multi-branch network is designed to aggregate information from both branches in parallel. The two branches encode the features and extract dependencies through several operations such as TextCNN, Bi-LSTM and different activation functions. Experimental results on real-world public datasets show that our model consistently achieves state-of-the-art performance over seven remarkable baselines. AVAILABILITY The source code of HN-PPISP model is available at https://github.com/ylxu05/HN-PPISP.
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Affiliation(s)
- Yan Kang
- National Pilot School of Software, Yunnan University, Kunming, 650091, P.R. China
| | - Yulong Xu
- National Pilot School of Software, Yunnan University, Kunming, 650091, P.R. China
| | - Xinchao Wang
- National Pilot School of Software, Yunnan University, Kunming, 650091, P.R. China
| | - Bin Pu
- College of Computer Science and Electronic Engineeringg, Hunan University, Changsha, 410082, P.R. China
| | - Xuekun Yang
- National Pilot School of Software, Yunnan University, Kunming, 650091, P.R. China
| | - Yulong Rao
- National Pilot School of Software, Yunnan University, Kunming, 650091, P.R. China
| | - Jianguo Chen
- School of Software Engineering, Sun Yat-Sen University, Zhuhai, 519082, P.R. China
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9
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Walder M, Edelstein E, Carroll M, Lazarev S, Fajardo JE, Fiser A, Viswanathan R. Integrated structure-based protein interface prediction. BMC Bioinformatics 2022; 23:301. [PMID: 35879651 PMCID: PMC9316365 DOI: 10.1186/s12859-022-04852-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Background Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model. Results We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces. Conclusions The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04852-2.
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Affiliation(s)
- M Walder
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - E Edelstein
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - M Carroll
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - S Lazarev
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA
| | - J E Fajardo
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - A Fiser
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - R Viswanathan
- Department of Chemistry, Yeshiva College, Yeshiva University, New York, NY, 10033, USA.
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10
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Wang L, Wong L, Chen ZH, Hu J, Sun XF, Li Y, You ZH. MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information. BIOLOGY 2022; 11:740. [PMID: 35625468 PMCID: PMC9138588 DOI: 10.3390/biology11050740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022]
Abstract
The key to new drug discovery and development is first and foremost the search for molecular targets of drugs, thus advancing drug discovery and drug repositioning. However, traditional drug-target interactions (DTIs) is a costly, lengthy, high-risk, and low-success-rate system project. Therefore, more and more pharmaceutical companies are trying to use computational technologies to screen existing drug molecules and mine new drugs, leading to accelerating new drug development. In the current study, we designed a deep learning computational model MSPEDTI based on Molecular Structure and Protein Evolutionary to predict the potential DTIs. The model first fuses protein evolutionary information and drug structure information, then a deep learning convolutional neural network (CNN) to mine its hidden features, and finally accurately predicts the associated DTIs by extreme learning machine (ELM). In cross-validation experiments, MSPEDTI achieved 94.19%, 90.95%, 87.95%, and 86.11% prediction accuracy in the gold-standard datasets enzymes, ion channels, G-protein-coupled receptors (GPCRs), and nuclear receptors, respectively. MSPEDTI showed its competitive ability in ablation experiments and comparison with previous excellent methods. Additionally, 7 of 10 potential DTIs predicted by MSPEDTI were substantiated by the classical database. These excellent outcomes demonstrate the ability of MSPEDTI to provide reliable drug candidate targets and strongly facilitate the development of drug repositioning and drug development.
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Affiliation(s)
- Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Leon Wong
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
| | - Zhan-Heng Chen
- Computer Science and Technology, Tongji University, Shanghai 200092, China;
| | - Jing Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Xiao-Fei Sun
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China; (J.H.); (X.-F.S.)
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China;
| | - Zhu-Hong You
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China;
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
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11
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Hu X, Feng C, Ling T, Chen M. Deep learning frameworks for protein–protein interaction prediction. Comput Struct Biotechnol J 2022; 20:3223-3233. [PMID: 35832624 PMCID: PMC9249595 DOI: 10.1016/j.csbj.2022.06.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/27/2022] [Accepted: 06/12/2022] [Indexed: 11/26/2022] Open
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12
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Wang P, Zhang G, Yu ZG, Huang G. A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites. Front Genet 2021; 12:752732. [PMID: 34764983 PMCID: PMC8576272 DOI: 10.3389/fgene.2021.752732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 09/20/2021] [Indexed: 11/29/2022] Open
Abstract
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.
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Affiliation(s)
- Pan Wang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Guiyang Zhang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
| | - Zu-Guo Yu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang, China
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13
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Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information. Sci Rep 2021; 11:16910. [PMID: 34413375 PMCID: PMC8376940 DOI: 10.1038/s41598-021-96265-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 04/15/2021] [Indexed: 02/07/2023] Open
Abstract
Various biochemical functions of organisms are performed by protein-protein interactions (PPIs). Therefore, recognition of protein-protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.
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14
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Li Y, Golding GB, Ilie L. DELPHI: accurate deep ensemble model for protein interaction sites prediction. Bioinformatics 2021; 37:896-904. [PMID: 32840562 DOI: 10.1093/bioinformatics/btaa750] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/14/2020] [Accepted: 08/19/2020] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein-protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods. RESULTS We propose DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI), a new sequence-based deep learning suite for PPI-binding sites prediction. DELPHI has an ensemble structure which combines a CNN and a RNN component with fine tuning technique. Three novel features, HSP, position information and ProtVec are used in addition to nine existing ones. We comprehensively compare DELPHI to nine state-of-the-art programmes on five datasets, and DELPHI outperforms the competing methods in all metrics even though its training dataset shares the least similarities with the testing datasets. In the most important metrics, AUPRC and MCC, it surpasses the second best programmes by as much as 18.5% and 27.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model and, especially, the three new features. Using DELPHI it is shown that there is a strong correlation with protein-binding residues (PBRs) and sites with strong evolutionary conservation. In addition, DELPHI's predicted PBR sites closely match known data from Pfam. DELPHI is available as open-sourced standalone software and web server. AVAILABILITY AND IMPLEMENTATION The DELPHI web server can be found at delphi.csd.uwo.ca/, with all datasets and results in this study. The trained models, the DELPHI standalone source code, and the feature computation pipeline are freely available at github.com/lucian-ilie/DELPHI. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yiwei Li
- Department of Computer Science, The University of Western Ontario London, ON N6A 5B7, Canada
| | - G Brian Golding
- Department of Biology, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Lucian Ilie
- Department of Computer Science, The University of Western Ontario London, ON N6A 5B7, Canada
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15
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Wang B, Mei C, Wang Y, Zhou Y, Cheng MT, Zheng CH, Wang L, Zhang J, Chen P, Xiong Y. Imbalance Data Processing Strategy for Protein Interaction Sites Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:985-994. [PMID: 31751283 DOI: 10.1109/tcbb.2019.2953908] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-protein interactions play essential roles in various biological progresses. Identifying protein interaction sites can facilitate researchers to understand life activities and therefore will be helpful for drug design. However, the number of experimental determined protein interaction sites is far less than that of protein sites in protein-protein interaction or protein complexes. Therefore, the negative and positive samples are usually imbalanced, which is common but bring result bias on the prediction of protein interaction sites by computational approaches. In this work, we presented three imbalance data processing strategies to reconstruct the original dataset, and then extracted protein features from the evolutionary conservation of amino acids to build a predictor for identification of protein interaction sites. On a dataset with 10,430 surface residues but only 2,299 interface residues, the imbalance dataset processing strategies can obviously reduce the prediction bias, and therefore improve the prediction performance of protein interaction sites. The experimental results show that our prediction models can achieve a better prediction performance, such as a prediction accuracy of 0.758, or a high F-measure of 0.737, which demonstrated the effectiveness of our method.
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16
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Zhang J, Ghadermarzi S, Kurgan L. Prediction of protein-binding residues: dichotomy of sequence-based methods developed using structured complexes versus disordered proteins. Bioinformatics 2021; 36:4729-4738. [PMID: 32860044 DOI: 10.1093/bioinformatics/btaa573] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 05/22/2020] [Accepted: 06/10/2020] [Indexed: 01/08/2023] Open
Abstract
MOTIVATION There are over 30 sequence-based predictors of the protein-binding residues (PBRs). They use either structure-annotated or disorder-annotated training datasets, potentially creating a dichotomy where the structure-/disorder-specific models may not be able to cross-over to accurately predict the other type. Moreover, the structure-trained predictors were shown to substantially cross-predict PBRs among residues that interact with non-protein partners (nucleic acids and small ligands). We address these issues by performing first-of-its-kind comparative study of a representative collection of disorder- and structure-trained predictors using a comprehensive benchmark set with the structure- and disorder-derived annotations of PBRs (to analyze the cross-over) and the protein-, nucleic acid- and small ligand-binding proteins (to study the cross-predictions). RESULTS Three predictors provide accurate results: SCRIBER, ANCHOR and disoRDPbind. Some of the structure-trained methods make accurate predictions on the structure-annotated proteins. Similarly, the disorder-trained predictors predict well on the disorder-annotated proteins. However, the considered predictors generally fail to cross-over, with the exception of SCRIBER. Our study also reveals that virtually all methods substantially cross-predict PBRs, except for SCRIBER for the structure-annotated proteins and disoRDPbind for the disorder-annotated proteins. We formulate a novel hybrid predictor, hybridPBRpred, that combines results produced by disoRDPbind and SCRIBER to accurately predict disorder- and structure-annotated PBRs. HybridPBRpred generates accurate results that cross-over structure- and disorder-annotated proteins and produces relatively low amount of cross-predictions, offering an accurate alternative to predict PBRs. AVAILABILITY AND IMPLEMENTATION HybridPBRpred webserver, benchmark dataset and supplementary information are available at http://biomine.cs.vcu.edu/servers/hybridPBRpred/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
| | - Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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17
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Zhang F, Shi W, Zhang J, Zeng M, Li M, Kurgan L. PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection. Bioinformatics 2020; 36:i735-i744. [DOI: 10.1093/bioinformatics/btaa806] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
Abstract
Motivation
Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods.
Results
We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein.
Availability and implementation
PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fuhao Zhang
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Wenbo Shi
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Min Zeng
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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18
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Qiu Z, Liu Q. Protein-protein interaction site prediction using random forest proximity distance. J Bioinform Comput Biol 2020; 19:2050042. [PMID: 33215966 DOI: 10.1142/s0219720020500420] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A front-end method based on random forest proximity distance (PD) is used to screen the test set to improve protein-protein interaction site (PPIS) prediction. The assessment of a distance metric is done under the assumption that a distance definition of higher quality leads to higher classification. On an independent test set, the numerical analysis based on statistical inference shows that the PD has the advantage over Mahalanobis and Cosine distance. Based on the fact that the proximity distance depends on the tree composition of the random forest model, an iterative method is designed to optimize the proximity distance, which adjusts the tree composition of the random forest model by adjusting the size of the training set. Two PD metrics, 75PD and 50PD, are obtained by the iterative method. On two independent test sets, compared with the PD produced by the original training set, the values of 75PD in Matthews correlation coefficient and F1 score were higher, and the differences between them were statistically significant. All numerical experiments show that the closer the distance between the test data and the training data, the better the prediction results of the predictor. These indicate that the iterative method can optimize proximity distance definition and the distance information provided by PD can be used to indicate the reliability of prediction results.
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Affiliation(s)
- Zhijun Qiu
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, P. R. China.,Henan Engineering Research Center of Food Microbiology, Luoyang 471023, P. R. China
| | - Qingjie Liu
- College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, P. R. China
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19
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Li Y, Liu X, You Z, Li L, Guo J, Wang Z. A computational approach for predicting drug–target interactions from protein sequence and drug substructure fingerprint information. INT J INTELL SYST 2020. [DOI: 10.1002/int.22332] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Yang Li
- School of Computer Science & Cyberspace Security Hainan University Haikou China
| | - Xiao‐zhang Liu
- School of Computer Science & Cyberspace Security Hainan University Haikou China
| | - Zhu‐Hong You
- School of Information Engineering Xijing University Xi'an China
| | - Li‐Ping Li
- School of Information Engineering Xijing University Xi'an China
| | - Jian‐Xin Guo
- School of Information Engineering Xijing University Xi'an China
| | - Zheng Wang
- School of Information Engineering Xijing University Xi'an China
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20
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Wang J, Dai W, Li J, Xie R, Dunstan RA, Stubenrauch C, Zhang Y, Lithgow T. PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins. Nucleic Acids Res 2020; 48:W348-W357. [PMID: 32459325 PMCID: PMC7319593 DOI: 10.1093/nar/gkaa432] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/22/2020] [Accepted: 05/13/2020] [Indexed: 01/09/2023] Open
Abstract
Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR–Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR–Cas machineries for the development of new CRISPR–Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.
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Affiliation(s)
- Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
| | - Wei Dai
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jiahui Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ruopeng Xie
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Rhys A Dunstan
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
| | - Christopher Stubenrauch
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, VIC 3800, Australia
| | - Yanju Zhang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Trevor Lithgow
- To whom correspondence should be addressed. Tel: +61 3 9902 9217; Fax: +61 3 9905 3726;
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21
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Zhang Y, Yu S, Xie R, Li J, Leier A, Marquez-Lago TT, Akutsu T, Smith AI, Ge Z, Wang J, Lithgow T, Song J. PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. Bioinformatics 2020; 36:704-712. [PMID: 31393553 DOI: 10.1093/bioinformatics/btz629] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 07/17/2019] [Accepted: 08/07/2019] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanju Zhang
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Sha Yu
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia
| | - Ruopeng Xie
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia
| | - Jiahui Li
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.,Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - André Leier
- Department of Genetics, AL, USA.,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, AL, USA.,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
| | - A Ian Smith
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, VIC 3800, Australia
| | - Zongyuan Ge
- Monash e-Research Centre and Faculty of Engineering, Monash University, Melbourne, VIC 3800, Australia
| | - Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Trevor Lithgow
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, VIC 3800, Australia.,ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, VIC 3800, Australia
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22
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Zhu X, Liu L, He J, Fang T, Xiong Y, Mitchell JC. iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots. BMC Bioinformatics 2020; 21:289. [PMID: 32631222 PMCID: PMC7336410 DOI: 10.1186/s12859-020-03636-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 06/25/2020] [Indexed: 12/20/2022] Open
Abstract
Background The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. Results In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods. Conclusion In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.
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Affiliation(s)
- Xiaolei Zhu
- School of Sciences, Anhui Agricultural University, Hefei, Anhui, China. .,School of Life Sciences, Anhui University, Hefei, Anhui, China.
| | - Ling Liu
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Jingjing He
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Ting Fang
- School of Life Sciences, Anhui University, Hefei, Anhui, China
| | - Yi Xiong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.
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23
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Xie R, Li J, Wang J, Dai W, Leier A, Marquez-Lago TT, Akutsu T, Lithgow T, Song J, Zhang Y. DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy. Brief Bioinform 2020; 22:5864586. [PMID: 32599617 DOI: 10.1093/bib/bbaa125] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 05/22/2020] [Accepted: 05/22/2020] [Indexed: 12/14/2022] Open
Abstract
Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user's viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.
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Affiliation(s)
- Ruopeng Xie
- Bioinformatics Lab at Guilin University of Electronic Technology
| | - Jiahui Li
- Bioinformatics Lab at Guilin University of Electronic Technology
| | - Jiawei Wang
- Biomedicine Discovery Institute and the Department of Microbiology at Monash University, Australia
| | - Wei Dai
- School of Computer Science and Information Security, Guilin University of Electronic Technology, China
| | - André Leier
- Department of Genetics and the Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham (UAB) School of Medicine, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics and the Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham (UAB) School of Medicine, USA
| | | | - Trevor Lithgow
- Biomedicine Discovery Institute and the Director of the Centre to Impact AMR at Monash University, Australia
| | - Jiangning Song
- Group Leader in the Biomedicine Discovery Institute and the Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
| | - Yanju Zhang
- Leiden Institute of Advanced Computer Science, Leiden University
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24
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Zhang J, Kurgan L. SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences. Bioinformatics 2020; 35:i343-i353. [PMID: 31510679 PMCID: PMC6612887 DOI: 10.1093/bioinformatics/btz324] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Motivation Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use. Results We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins. Availability and implementation SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, China.,Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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25
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Zhu H, Du X, Yao Y. ConvsPPIS: Identifying Protein-protein Interaction Sites by an Ensemble Convolutional Neural Network with Feature Graph. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191105155713] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background/Objective:
Protein-protein interactions are essentials for most cellular
processes and thus, unveiling how proteins interact with is a crucial question that can be better
understood by recognizing which residues participate in the interaction. Although many
computational approaches have been proposed to predict interface residues, their feature
perspective and model learning ability are not enough to achieve ideal results. So, our objective is
to improve the predictive performance under considering feature perspective and new learning
algorithm.
Method:
In this study, we proposed an ensemble deep convolutional neural network, which
explores the context and positional context of consecutive residues within a protein sub-sequence.
Specifically, unlike the feature view of previous methods, ConvsPPIS uses evolutionary,
physicochemical, and structural protein characteristics to construct their own feature graph
respectively. After that, three independent deep convolutional neural networks are trained on each
type of feature graph for learning the underlying pattern in sub-sequence. Lastly, we integrated
those three deep networks into an ensemble predictor with leveraging complementary information
of those features to predict potential interface residues.
Results:
Some comparative experiments have conducted through 10-fold cross-validation. The
results indicated that ConvsPPIS achieved superior performance on DBv5-Sel dataset with an
accuracy of 88%. Additional experiments on CAPRI-Alone dataset demonstrated ConvsPPIS has
also better prediction performance.
Conclusion:
The ConvsPPIS method provided a new perspective to capture protein feature
expression for identifying protein-protein interaction sites. The results proved the superiority of
this method.
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Affiliation(s)
- Huaixu Zhu
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Xiuquan Du
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Yu Yao
- School of Computer Science and Technology, Anhui University, Hefei, China
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26
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Wang L, You ZH, Huang DS, Zhou F. Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:972-980. [PMID: 30296240 DOI: 10.1109/tcbb.2018.2874267] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions. In this study, we propose a computational method for predicting the RNA-protein interactions using sequence information. The deep learning convolution neural network (CNN) algorithm is utilized to mine the hidden high-level discriminative features from the RNA and protein sequences and feed it into the extreme learning machine (ELM) classifier. The experimental results with 5-fold cross-validation indicate that the proposed method achieves superior performance on benchmark datasets (RPI1807, RPI2241, and RPI369) with the accuracy of 98.83, 90.83, and 85.63 percent, respectively. We further evaluate the performance of the proposed model by comparing it with the state-of-the-art SVM classifier and other existing methods on the same benchmark data set. In addition, we predicted the independent NPInter v2.0 data set using the model trained on RPI369. The experimental results show that our model can serve as a useful tool for predicting RNA-protein interactions.
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27
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Xie Z, Deng X, Shu K. Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets. Int J Mol Sci 2020; 21:E467. [PMID: 31940793 PMCID: PMC7013409 DOI: 10.3390/ijms21020467] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 12/23/2019] [Accepted: 01/08/2020] [Indexed: 12/20/2022] Open
Abstract
Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.
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Affiliation(s)
- Zengyan Xie
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
| | | | - Kunxian Shu
- Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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28
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Wang J, Yang B, Leier A, Marquez-Lago TT, Hayashida M, Rocker A, Zhang Y, Akutsu T, Chou KC, Strugnell RA, Song J, Lithgow T. Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors. Bioinformatics 2019; 34:2546-2555. [PMID: 29547915 DOI: 10.1093/bioinformatics/bty155] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 03/09/2018] [Indexed: 12/28/2022] Open
Abstract
Motivation Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data. Results We extracted a wide range of features from T6SE protein sequences and comprehensively analyzed the prediction performance of these features through unsupervised and supervised learning. By integrating these features, we subsequently developed a two-layer SVM-based ensemble model with fine-grain optimized parameters, to identify potential T6SEs. We further validated the predictive model using an independent dataset, which showed that the proposed model achieved an impressive performance in terms of ACC (0.943), F-value (0.946), MCC (0.892) and AUC (0.976). To demonstrate applicability, we employed this method to correctly identify two very recently validated T6SE proteins, which represent challenging prediction targets because they significantly differed from previously known T6SEs in terms of their sequence similarity and cellular function. Furthermore, a genome-wide prediction across 12 bacterial species, involving in total 54 212 protein sequences, was carried out to distinguish 94 putative T6SE candidates. We envisage both this information and our publicly accessible web server will facilitate future discoveries of novel T6SEs. Availability and implementation http://bastion6.erc.monash.edu/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiawei Wang
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia
| | - Bingjiao Yang
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tatiana T Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Morihiro Hayashida
- National Institute of Technology, Matsue College, Matsue, Shimane, Japan
| | - Andrea Rocker
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia
| | - Yanju Zhang
- Bioinformatics Group, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA.,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Richard A Strugnell
- Department of Microbiology and Immunology and Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Parkville, VIC, Australia
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology.,Monash Centre for Data Science, Faculty of Information Technolog, Monash University, Clayton, VIC, Australia.,ARC Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, VIC, Australia
| | - Trevor Lithgow
- Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia
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Li Y, Huang YA, You ZH, Li LP, Wang Z. Drug-Target Interaction Prediction Based on Drug Fingerprint Information and Protein Sequence. Molecules 2019; 24:molecules24162999. [PMID: 31430892 PMCID: PMC6719962 DOI: 10.3390/molecules24162999] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 01/09/2023] Open
Abstract
The identification of drug-target interactions (DTIs) is a critical step in drug development. Experimental methods that are based on clinical trials to discover DTIs are time-consuming, expensive, and challenging. Therefore, as complementary to it, developing new computational methods for predicting novel DTI is of great significance with regards to saving cost and shortening the development period. In this paper, we present a novel computational model for predicting DTIs, which uses the sequence information of proteins and a rotation forest classifier. Specifically, all of the target protein sequences are first converted to a position-specific scoring matrix (PSSM) to retain evolutionary information. We then use local phase quantization (LPQ) descriptors to extract evolutionary information in the PSSM. On the other hand, substructure fingerprint information is utilized to extract the features of the drug. We finally combine the features of drugs and protein together to represent features of each drug-target pair and use a rotation forest classifier to calculate the scores of interaction possibility, for a global DTI prediction. The experimental results indicate that the proposed model is effective, achieving average accuracies of 89.15%, 86.01%, 82.20%, and 71.67% on four datasets (i.e., enzyme, ion channel, G protein-coupled receptors (GPCR), and nuclear receptor), respectively. In addition, we compared the prediction performance of the rotation forest classifier with another popular classifier, support vector machine, on the same dataset. Several types of methods previously proposed are also implemented on the same datasets for performance comparison. The comparison results demonstrate the superiority of the proposed method to the others. We anticipate that the proposed method can be used as an effective tool for predicting drug-target interactions on a large scale, given the information of protein sequences and drug fingerprints.
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Affiliation(s)
- Yang Li
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Yu-An Huang
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Zhu-Hong You
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Zheng Wang
- School of Information Engineering, Xijing University, Xi'an 710123, China
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30
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein–protein interactions. J Biosci 2019. [DOI: 10.1007/s12038-019-9909-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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31
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Wang J, Li J, Yang B, Xie R, Marquez-Lago TT, Leier A, Hayashida M, Akutsu T, Zhang Y, Chou KC, Selkrig J, Zhou T, Song J, Lithgow T. Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics 2018; 35:2017-2028. [PMID: 30388198 PMCID: PMC7963071 DOI: 10.1093/bioinformatics/bty914] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 10/15/2018] [Accepted: 10/31/2018] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins' complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model. RESULTS In this work, we present Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data. In contrast with existing methods that employ single models with few features, Bastion3 explores a wide range of features, from various types, trains single models based on these features and finally integrates these models through ensemble learning. We trained the models using a new gradient boosting machine, LightGBM and further boosted the models' performances through a novel genetic algorithm (GA) based two-step parameter optimization strategy. Our benchmark test demonstrates that Bastion3 achieves a much better performance compared to commonly used methods, with an ACC value of 0.959, F-value of 0.958, MCC value of 0.917 and AUC value of 0.956, which comprehensively outperformed all other toolkits by more than 5.6% in ACC value, 5.7% in F-value, 12.4% in MCC value and 5.8% in AUC value. Based on our proposed two-layer ensemble model, we further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction. With its design to ease future discoveries of novel T3SEs and improved performance, Bastion3 is poised to become a widely used, state-of-the-art toolkit for T3SE prediction. AVAILABILITY AND IMPLEMENTATION http://bastion3.erc.monash.edu/. CONTACT selkrig@embl.de or wyztli@163.com or or trevor.lithgow@monash.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiawei Wang
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia
| | - Jiahui Li
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia,Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bingjiao Yang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Ruopeng Xie
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Tatiana T Marquez-Lago
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - André Leier
- Department of Genetics, School of Medicine, University of Alabama at Birmingham, AL, USA,Department of Cell, Developmental and Integrative Biology, School of Medicine, University of Alabama at Birmingham, AL, USA
| | - Morihiro Hayashida
- National Institute of Technology, Matsue College, Matsue, Shimane, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Yanju Zhang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Kuo-Chen Chou
- Gordon Life Science Institute, Boston, MA, USA,Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China,Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Joel Selkrig
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Tieli Zhou
- Department of Clinical Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | | | - Trevor Lithgow
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC, Australia
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32
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Wang L, Yan X, Liu ML, Song KJ, Sun XF, Pan WW. Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method. J Theor Biol 2018; 461:230-238. [PMID: 30321541 DOI: 10.1016/j.jtbi.2018.10.029] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Revised: 03/22/2018] [Accepted: 10/11/2018] [Indexed: 01/01/2023]
Abstract
RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a large extent. So there is an urgent need for reliable computational methods to quickly and accurately predict RNA-protein interaction. In this study, we propose a novel computational method RPIFSE (predicting RPI with Feature Selection Ensemble method) based on RNA and protein sequence information to predict RPI. Firstly, RPIFSE disturbs the features extracted by the convolution neural network (CNN) and generates multiple data sets according to the weight of the feature, and then use extreme learning machine (ELM) classifier to classify these data sets. Finally, the results of each classifier are combined, and the highest score is chosen as the final prediction result by weighting voting method. In 5-fold cross-validation experiments, RPIFSE achieved 91.87%, 89.74%, 97.76% and 98.98% accuracy on RPI369, RPI2241, RPI488 and RPI1807 data sets, respectively. To further evaluate the performance of RPIFSE, we compare it with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on those data sets. Furthermore, we also predicted the RPI on the independent data set NPInter2.0 and drew the network graph based on the prediction results. These promising comparison results demonstrated the effectiveness of RPIFSE and indicated that RPIFSE could be a useful tool for predicting RPI.
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Affiliation(s)
- Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
| | - Xin Yan
- School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China.
| | - Meng-Lin Liu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China
| | - Ke-Jian Song
- School of Information Engineering, JiangXi University of Science and Technology, Ganzhou, Jiangxi 341000, China.
| | - Xiao-Fei Sun
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
| | - Wen-Wen Pan
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
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33
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Tran L, Hamp T, Rost B. ProfPPIdb: Pairs of physical protein-protein interactions predicted for entire proteomes. PLoS One 2018; 13:e0199988. [PMID: 30020956 PMCID: PMC6051629 DOI: 10.1371/journal.pone.0199988] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Accepted: 06/17/2018] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. RESULTS We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, recent methods explicitly ignored such a network-structure and rely either on domain knowledge or sequence-only methods. Our approach is independent of domain-knowledge and leverages evolutionary information. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/. The data can also be downloaded from https://figshare.com/collections/ProfPPI-DB/4141784.
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Affiliation(s)
- Linh Tran
- Imperial College London (ICL), Department of Computing, United Kingdom
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
- * E-mail:
| | - Tobias Hamp
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
| | - Burkhard Rost
- Technical University of Munich (TUM), Department of Informatics, Bioinformatics & Computational Biology - i12, Boltzmannstr, Germany
- Technical University of Munich (TUM), Institute for Advanced Study (TUM-IAS), Lichtenbergstr, Germany
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34
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Daberdaku S, Ferrari C. Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction. BMC Bioinformatics 2018; 19:35. [PMID: 29409446 PMCID: PMC5802066 DOI: 10.1186/s12859-018-2043-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 01/24/2018] [Indexed: 12/22/2022] Open
Abstract
Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class. Electronic supplementary material The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sebastian Daberdaku
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy.
| | - Carlo Ferrari
- Department of Information Engineering, University of Padova, via Gradenigo 6/A, Padova, 35131, Italy
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35
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Qiao Y, Xiong Y, Gao H, Zhu X, Chen P. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics 2018; 19:14. [PMID: 29334889 PMCID: PMC5769548 DOI: 10.1186/s12859-018-2009-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/03/2018] [Indexed: 11/16/2022] Open
Abstract
Background Hot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model. Results In this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection. Conclusion In this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/. Electronic supplementary material The online version of this article (10.1186/s12859-018-2009-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yanhua Qiao
- School of Life Sciences, Anhui University, Hefei, Anhui, 230601, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai JiaoTong University, Shanghai, 200240, China.,School of Life Sciences and Biotechnology, Shanghai JiaoTong University, Shanghai, 200240, China
| | - Hongyun Gao
- Information and Engineering College, Dalian University, Dalian, Liaoning, 116622, China
| | - Xiaolei Zhu
- School of Life Sciences, Anhui University, Hefei, Anhui, 230601, China.
| | - Peng Chen
- Institute of Health Sciences, Anhui University, Hefei, Anhui, 230601, China.
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36
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Qiu Z, Zhou B, Yuan J. Protein–protein interaction site predictions with minimum covariance determinant and Mahalanobis distance. J Theor Biol 2017; 433:57-63. [DOI: 10.1016/j.jtbi.2017.08.026] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/26/2017] [Accepted: 08/30/2017] [Indexed: 10/18/2022]
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37
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Wang L, You ZH, Chen X, Xia SX, Liu F, Yan X, Zhou Y, Song KJ. A Computational-Based Method for Predicting Drug-Target Interactions by Using Stacked Autoencoder Deep Neural Network. J Comput Biol 2017; 25:361-373. [PMID: 28891684 DOI: 10.1089/cmb.2017.0135] [Citation(s) in RCA: 103] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Identifying the interaction between drugs and target proteins is an important area of drug research, which provides a broad prospect for low-risk and faster drug development. However, due to the limitations of traditional experiments when revealing drug-protein interactions (DTIs), the screening of targets not only takes a lot of time and money but also has high false-positive and false-negative rates. Therefore, it is imperative to develop effective automatic computational methods to accurately predict DTIs in the postgenome era. In this article, we propose a new computational method for predicting DTIs from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which can adequately extract the raw data information. The proposed method has the advantage that it can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers. The feature descriptors are then constructed by combining the molecular substructure fingerprint information, and fed into the rotation forest for accurate prediction. The experimental results of fivefold cross-validation indicate that the proposed method achieves superior performance on gold standard data sets (enzymes, ion channels, GPCRs [G-protein-coupled receptors], and nuclear receptors) with accuracy of 0.9414, 0.9116, 0.8669, and 0.8056, respectively. We further comprehensively explore the performance of the proposed method by comparing it with other feature extraction algorithms, state-of-the-art classifiers, and other excellent methods on the same data set. The excellent comparison results demonstrate that the proposed method is highly competitive when predicting drug-target interactions.
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Affiliation(s)
- Lei Wang
- 1 School of Computer Science and Technology, China University of Mining and Technology , Xuzhou, China .,2 College of Information Science and Engineering, Zaozhuang University , Zaozhuang, China
| | - Zhu-Hong You
- 3 Xinjiang Technical Institutes of Physics and Chemistry , Chinese Academy of Science, Urumqi, China
| | - Xing Chen
- 4 School of Information and Control Engineering, China University of Mining and Technology , Xuzhou, China
| | - Shi-Xiong Xia
- 1 School of Computer Science and Technology, China University of Mining and Technology , Xuzhou, China
| | - Feng Liu
- 5 China National Coal Association , Beijing, China
| | - Xin Yan
- 6 School of Foreign Languages, Zaozhuang University , Zaozhuang, China
| | - Yong Zhou
- 1 School of Computer Science and Technology, China University of Mining and Technology , Xuzhou, China
| | - Ke-Jian Song
- 7 School of Information Engineering, JiangXi University of Science and Technology , Ganzhou, China
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38
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Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. J Theor Biol 2017; 418:105-110. [DOI: 10.1016/j.jtbi.2017.01.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2016] [Revised: 09/24/2016] [Accepted: 01/04/2017] [Indexed: 12/13/2022]
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39
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Zhang J, Kurgan L. Review and comparative assessment of sequence-based predictors of protein-binding residues. Brief Bioinform 2017; 19:821-837. [DOI: 10.1093/bib/bbx022] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Indexed: 12/31/2022] Open
Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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40
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Wang L, You ZH, Chen X, Li JQ, Yan X, Zhang W, Huang YA. An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences. Oncotarget 2017; 8:5149-5159. [PMID: 28029645 PMCID: PMC5354898 DOI: 10.18632/oncotarget.14103] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 11/15/2016] [Indexed: 11/25/2022] Open
Abstract
Protein-Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126:8888/PsePSSM/ for academic use.
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Affiliation(s)
- Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
| | - Xing Chen
- School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Xin Yan
- School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China
| | - Wei Zhang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China
| | - Yu-An Huang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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41
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A Novel Sample Selection Strategy for Imbalanced Data of Biomedical Event Extraction with Joint Scoring Mechanism. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:7536494. [PMID: 28096894 PMCID: PMC5206857 DOI: 10.1155/2016/7536494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 10/04/2016] [Accepted: 10/31/2016] [Indexed: 11/24/2022]
Abstract
Biomedical event extraction is an important and difficult task in bioinformatics. With the rapid growth of biomedical literature, the extraction of complex events from unstructured text has attracted more attention. However, the annotated biomedical corpus is highly imbalanced, which affects the performance of the classification algorithms. In this study, a sample selection algorithm based on sequential pattern is proposed to filter negative samples in the training phase. Considering the joint information between the trigger and argument of multiargument events, we extract triplets of multiargument events directly using a support vector machine classifier. A joint scoring mechanism, which is based on sentence similarity and importance of trigger in the training data, is used to correct the predicted results. Experimental results indicate that the proposed method can extract events efficiently.
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42
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Khavinson VK, Lin'kova NS, Tarnovskaya SI. Short Peptides Regulate Gene Expression. Bull Exp Biol Med 2016; 162:288-292. [PMID: 27909961 DOI: 10.1007/s10517-016-3596-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Indexed: 02/07/2023]
Abstract
Short peptides constitute the system of signal molecules regulating the functions of the organism at the molecular, genetic, subcellular, cellular, and tissue levels. One short peptide can regulate dozens of genes, but the molecular mechanism of this process remains unclear. We suppose that short peptides penetrate through the cytoplasmic and nuclear membrane and bind to DNA. Spatial models of DNA-peptide complexes are constructed for 19 short peptides by the docking method. Some peptides have the same binding sites. Peptides KE and EDP bind agat sequence, peptides KEDW and AED to acct sequence, and peptides AEDL and EDL to ctcc sequence.
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Affiliation(s)
- V Kh Khavinson
- St. Petersburg Institute of Bioregulation and Gerontology, St. Petersburg, Russia.,I. P. Pavlov Institute of Physiology, Russian Academy of Sciences, St. Petersburg, Russia.,I. I. Mechnikov North-Western State Medical University, St. Petersburg, Russia
| | - N S Lin'kova
- St. Petersburg Institute of Bioregulation and Gerontology, St. Petersburg, Russia. .,Peter the Great Polytechnical University, St. Petersburg, Russia.
| | - S I Tarnovskaya
- St. Petersburg Institute of Bioregulation and Gerontology, St. Petersburg, Russia.,Peter the Great Polytechnical University, St. Petersburg, Russia
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43
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Du T, Liao L, Wu CH. Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2016; 2016:17. [PMID: 27818677 PMCID: PMC5075339 DOI: 10.1186/s13637-016-0051-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 09/25/2016] [Indexed: 11/10/2022]
Abstract
Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.
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Affiliation(s)
- Tianchuan Du
- Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA
| | - Li Liao
- Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA
| | - Cathy H Wu
- Department of Computer and Information Sciences, University of Delaware, Newark, DE 19716 USA
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44
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Du T, Liao L, Wu CH, Sun B. Prediction of residue-residue contact matrix for protein-protein interaction with Fisher score features and deep learning. Methods 2016; 110:97-105. [DOI: 10.1016/j.ymeth.2016.06.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 06/03/2016] [Indexed: 11/28/2022] Open
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45
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Kuo TH, Li KB. Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids. Int J Mol Sci 2016; 17:ijms17111788. [PMID: 27792167 PMCID: PMC5133789 DOI: 10.3390/ijms17111788] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 10/14/2016] [Accepted: 10/18/2016] [Indexed: 12/17/2022] Open
Abstract
Information about the interface sites of Protein–Protein Interactions (PPIs) is useful for many biological research works. However, despite the advancement of experimental techniques, the identification of PPI sites still remains as a challenging task. Using a statistical learning technique, we proposed a computational tool for predicting PPI interaction sites. As an alternative to similar approaches requiring structural information, the proposed method takes all of the input from protein sequences. In addition to typical sequence features, our method takes into consideration that interaction sites are not randomly distributed over the protein sequence. We characterized this positional preference using protein complexes with known structures, proposed a numerical index to estimate the propensity and then incorporated the index into a learning system. The resulting predictor, without using structural information, yields an area under the ROC curve (AUC) of 0.675, recall of 0.597, precision of 0.311 and accuracy of 0.583 on a ten-fold cross-validation experiment. This performance is comparable to the previous approach in which structural information was used. Upon introducing the B-factor data to our predictor, we demonstrated that the AUC can be further improved to 0.750. The tool is accessible at http://bsaltools.ym.edu.tw/predppis.
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Affiliation(s)
- Tzu-Hao Kuo
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.
| | - Kuo-Bin Li
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan.
- Office of Information Management, National Yang-Ming University Hospital, Yilan 260, Taiwan.
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46
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Using the SMOTE technique and hybrid features to predict the types of ion channel-targeted conotoxins. J Theor Biol 2016; 403:75-84. [DOI: 10.1016/j.jtbi.2016.04.034] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 04/25/2016] [Accepted: 04/29/2016] [Indexed: 12/22/2022]
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47
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Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4563524. [PMID: 27437399 PMCID: PMC4942601 DOI: 10.1155/2016/4563524] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2016] [Accepted: 05/08/2016] [Indexed: 11/17/2022]
Abstract
Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.
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48
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Wei ZS, Han K, Yang JY, Shen HB, Yu DJ. Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.02.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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49
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Yeon JH, Heinkel F, Sung M, Na D, Gsponer J. Systems-wide Identification of cis-Regulatory Elements in Proteins. Cell Syst 2016; 2:89-100. [PMID: 27135163 DOI: 10.1016/j.cels.2016.02.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Revised: 12/15/2015] [Accepted: 02/01/2016] [Indexed: 10/22/2022]
Abstract
Protein interactions in cis that can activate or autoinhibit protein function play an important role in the fine-tuning of regulatory and signaling processes in the cell, but thus far cis-regulatory elements (CREs) in proteins have not been systematically identified and studied. Here, we introduce a computational tool that identifies intrinsically disordered protein segments that contribute to protein function regulation via interactions in cis. We apply this tool to estimate the prevalence of CREs in the human proteome and reveal that cis regulation is enriched in several signaling pathways, including the MAP kinase pathway, for which we provide a detailed map of its "cis regulome." We also show that disease-causing mutations are highly enriched in CREs, but not in motifs that classically mediate protein-protein interactions of disordered protein segments. Our approach should facilitate the discovery and characterization of CREs in proteins and the identification of disease-causing mutations that disrupt protein regulation in cis.
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Affiliation(s)
- Ju Hun Yeon
- Department of Integrative Bioscience, University of Brain Education, 284-31 Gyocheonjisan-gil, Mokcheon-eup, Dongnam-gu, Cheonan, Chungcheongnam-do 31228, Republic of Korea
| | - Florian Heinkel
- Department of Biochemistry and Molecular Biology, Michael Smith Laboratories, University of British Columbia, 2125 East Mall, Vancouver, BC V6T 1Z4, Canada
| | - Minhui Sung
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea
| | - Dokyun Na
- School of Integrative Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea; Centre for High-Throughput Biology, University of British Columbia, 2125 East Mall, Vancouver, BC V6T 1Z4, Canada.
| | - Jörg Gsponer
- Department of Biochemistry and Molecular Biology, Michael Smith Laboratories, University of British Columbia, 2125 East Mall, Vancouver, BC V6T 1Z4, Canada; Centre for High-Throughput Biology, University of British Columbia, 2125 East Mall, Vancouver, BC V6T 1Z4, Canada.
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50
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iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules 2016; 21:E95. [PMID: 26797600 PMCID: PMC6274413 DOI: 10.3390/molecules21010095] [Citation(s) in RCA: 136] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 12/18/2015] [Accepted: 01/07/2016] [Indexed: 12/25/2022] Open
Abstract
Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.
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