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Ghosh S, Mitra P. MaTPIP: A deep-learning architecture with eXplainable AI for sequence-driven, feature mixed protein-protein interaction prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107955. [PMID: 38064959 DOI: 10.1016/j.cmpb.2023.107955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/09/2023] [Accepted: 11/26/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Protein-protein interaction (PPI) is a vital process in all living cells, controlling essential cell functions such as cell cycle regulation, signal transduction, and metabolic processes with broad applications that include antibody therapeutics, vaccines, and drug discovery. The problem of sequence-based PPI prediction has been a long-standing issue in computational biology. METHODS We introduce MaTPIP, a cutting-edge deep-learning framework for predicting PPI. MaTPIP stands out due to its innovative design, fusing pre-trained Protein Language Model (PLM)-based features with manually curated protein sequence attributes, emphasizing the part-whole relationship by incorporating two-dimensional granular part (amino-acid) level features and one-dimensional whole-level (protein) features. What sets MaTPIP apart is its ability to integrate these features across three different input terminals seamlessly. MatPIP also includes a distinctive configuration of Convolutional Neural Network (CNN) with Transformer components for concurrent utilization of CNN and sequential characteristics in each iteration and a one-dimensional to two-dimensional converter followed by a unified embedding. The statistical significance of this classifier is validated using McNemar's test. RESULTS MaTPIP outperformed the existing methods on both the Human PPI benchmark and cross-species PPI testing datasets, demonstrating its immense generalization capability for PPI prediction. We used seven diverse datasets with varying PPI target class distributions. Notably, within the novel PPI scenario, the most challenging category for Human PPI Benchmark, MaTPIP improves the existing state-of-the-art score from 74.1% to 78.6% (measured in Area under ROC Curve), from 23.2% to 32.8% (in average precision) and from 4.9% to 9.5% (in precision at 3% recall) for 50%, 10% and 0.3% target class distributions, respectively. In cross-species PPI evaluation, hybrid MaTPIP establishes a new benchmark score (measured in Area Under precision-recall curve) of 81.1% from the previous 60.9% for Mouse, 80.9% from 56.2% for Fly, 78.1% from 55.9% for Worm, 59.9% from 41.7% for Yeast, and 66.2% from 58.8% for E.coli. Our eXplainable AI-based assessment reveals an average contribution of different feature families per prediction on these datasets. CONCLUSIONS MaTPIP mixes manually curated features with the feature extracted from the pre-trained PLM to predict sequence-based protein-protein association. Furthermore, MaTPIP demonstrates strong generalization capabilities for cross-species PPI predictions.
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Affiliation(s)
- Shubhrangshu Ghosh
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India; TCS Research, Tata Consultancy Services Limited, Kolkata, West Bengal, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, India.
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Wu J, Liu B, Zhang J, Wang Z, Li J. DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning. BMC Bioinformatics 2023; 24:473. [PMID: 38097937 PMCID: PMC10722729 DOI: 10.1186/s12859-023-05594-5] [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: 08/08/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
PURPOSE Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets. RESULTS In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.
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Affiliation(s)
- Jiahui Wu
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Bo Liu
- School of Mathematical and Computational Sciences, Massey University, Auckland, 0745, New Zealand.
| | - Jidong Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zhihan Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
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Luo X, Wang L, Hu P, Hu L. Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3182-3194. [PMID: 37155405 DOI: 10.1109/tcbb.2023.3273567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negative rates observed in PPI data. To overcome this problem, a novel PPI prediction algorithm, namely PASNVGA, is proposed in this work by combining the sequence and network information of proteins via variational graph autoencoder. To do so, PASNVGA first applies different strategies to extract the features of proteins from their sequence and network information, and obtains a more compact form of these features using principal component analysis. In addition, PASNVGA designs a scoring function to measure the higher-order connectivity between proteins and so as to obtain a higher-order adjacency matrix. With all these features and adjacency matrices, PASNVGA trains a variational graph autoencoder model to further learn the integrated embeddings of proteins. The prediction task is then completed by using a simple feedforward neural network. Extensive experiments have been conducted on five PPI datasets collected from different species. Compared with several state-of-the-art algorithms, PASNVGA has been demonstrated as a promising PPI prediction algorithm.
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Zhang F, Zhang Y, Zhu X, Chen X, Lu F, Zhang X. DeepSG2PPI: A Protein-Protein Interaction Prediction Method Based on Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2907-2919. [PMID: 37079417 DOI: 10.1109/tcbb.2023.3268661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. First, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Second, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.
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Wang X, Yang W, Yang Y, He Y, Zhang J, Wang L, Hu L. PPISB: A Novel Network-Based Algorithm of Predicting Protein-Protein Interactions With Mixed Membership Stochastic Blockmodel. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1606-1612. [PMID: 35939453 DOI: 10.1109/tcbb.2022.3196336] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Protein-protein interactions (PPIs) play an essential role for most of biological processes in cells. Many computational algorithms have thus been proposed to predict PPIs. However, most of them heavily rest on the biological information of proteins while ignoring the latent structural features of proteins presented in a PPI network. In this paper, we propose an efficient network-based prediction algorithm, namely PPISB, based on a mixed membership stochastic blockmodel. By simulating the generative process of a PPI network, PPISB is able to capture the latent community structures. The inference procedure adopted by PPISB further optimizes the membership distributions of proteins over different complexes. After that, a distance measure is designed to compute the similarity between two proteins in terms of their likelihoods of being in the same complex, thus verifying whether they interact with each other or not. To evaluate the performance of PPISB, a series of extensive experiments have been conducted with five PPI networks collected from different species and the results demonstrate that PPISB has a promising performance when applied to predict PPIs in terms of several evaluation metrics. Hence, we reason that PPISB is preferred over state-of-the-art network-based prediction algorithms especially for predicting potential PPIs.
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Sykes J, Holland BR, Charleston MA. A review of visualisations of protein fold networks and their relationship with sequence and function. Biol Rev Camb Philos Soc 2023; 98:243-262. [PMID: 36210328 PMCID: PMC10092621 DOI: 10.1111/brv.12905] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 09/08/2022] [Accepted: 09/09/2022] [Indexed: 01/12/2023]
Abstract
Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.
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Affiliation(s)
- Janan Sykes
- School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart, Tasmania, 7001, Australia
| | - Barbara R Holland
- School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart, Tasmania, 7001, Australia
| | - Michael A Charleston
- School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart, Tasmania, 7001, Australia
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Hu L, Li Z, Tang Z, Zhao C, Zhou X, Hu P. Effectively predicting HIV-1 protease cleavage sites by using an ensemble learning approach. BMC Bioinformatics 2022; 23:447. [PMID: 36303135 PMCID: PMC9608884 DOI: 10.1186/s12859-022-04999-y] [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: 04/26/2022] [Accepted: 10/13/2022] [Indexed: 11/10/2022] Open
Abstract
Background The site information of substrates that can be cleaved by human immunodeficiency virus 1 proteases (HIV-1 PRs) is of great significance for designing effective inhibitors against HIV-1 viruses. A variety of machine learning-based algorithms have been developed to predict HIV-1 PR cleavage sites by extracting relevant features from substrate sequences. However, only relying on the sequence information is not sufficient to ensure a promising performance due to the uncertainty in the way of separating the datasets used for training and testing. Moreover, the existence of noisy data, i.e., false positive and false negative cleavage sites, could negatively influence the accuracy performance. Results In this work, an ensemble learning algorithm for predicting HIV-1 PR cleavage sites, namely EM-HIV, is proposed by training a set of weak learners, i.e., biased support vector machine classifiers, with the asymmetric bagging strategy. By doing so, the impact of data imbalance and noisy data can thus be alleviated. Besides, in order to make full use of substrate sequences, the features used by EM-HIV are collected from three different coding schemes, including amino acid identities, chemical properties and variable-length coevolutionary patterns, for the purpose of constructing more relevant feature vectors of octamers. Experiment results on three independent benchmark datasets demonstrate that EM-HIV outperforms state-of-the-art prediction algorithm in terms of several evaluation metrics. Hence, EM-HIV can be regarded as a useful tool to accurately predict HIV-1 PR cleavage sites.
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Affiliation(s)
- Lun Hu
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhenfeng Li
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Zehai Tang
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Cheng Zhao
- grid.162110.50000 0000 9291 3229School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Xi Zhou
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Pengwei Hu
- grid.9227.e0000000119573309Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
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Su XR, Hu L, You ZH, Hu PW, Zhao BW. Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction. BMC Bioinformatics 2022; 23:234. [PMID: 35710342 PMCID: PMC9205098 DOI: 10.1186/s12859-022-04766-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/27/2022] [Indexed: 01/02/2023] Open
Abstract
Background Protein–protein interaction (PPI) plays an important role in regulating cells and signals. Despite the ongoing efforts of the bioassay group, continued incomplete data limits our ability to understand the molecular roots of human disease. Therefore, it is urgent to develop a computational method to predict PPIs from the perspective of molecular system. Methods In this paper, a highly efficient computational model, MTV-PPI, is proposed for PPI prediction based on a heterogeneous molecular network by learning inter-view protein sequences and intra-view interactions between molecules simultaneously. On the one hand, the inter-view feature is extracted from the protein sequence by k-mer method. On the other hand, we use a popular embedding method LINE to encode the heterogeneous molecular network to obtain the intra-view feature. Thus, the protein representation used in MTV-PPI is constructed by the aggregation of its inter-view feature and intra-view feature. Finally, random forest is integrated to predict potential PPIs. Results To prove the effectiveness of MTV-PPI, we conduct extensive experiments on a collected heterogeneous molecular network with the accuracy of 86.55%, sensitivity of 82.49%, precision of 89.79%, AUC of 0.9301 and AUPR of 0.9308. Further comparison experiments are performed with various protein representations and classifiers to indicate the effectiveness of MTV-PPI in predicting PPIs based on a complex network. Conclusion The achieved experimental results illustrate that MTV-PPI is a promising tool for PPI prediction, which may provide a new perspective for the future interactions prediction researches based on heterogeneous molecular network.
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Affiliation(s)
- Xiao-Rui Su
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi, 830011, China
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A computational method for predicting nucleocapsid protein in retroviruses. Sci Rep 2022; 12:524. [PMID: 35017554 PMCID: PMC8752852 DOI: 10.1038/s41598-021-03182-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/26/2021] [Indexed: 11/08/2022] Open
Abstract
Nucleocapsid protein (NC) in the group-specific antigen (gag) of retrovirus is essential in the interactions of most retroviral gag proteins with RNAs. Computational method to predict NCs would benefit subsequent structure analysis and functional study on them. However, no computational method to predict the exact locations of NCs in retroviruses has been proposed yet. The wide range of length variation of NCs also increases the difficulties. In this paper, a computational method to identify NCs in retroviruses is proposed. All available retrovirus sequences with NC annotations were collected from NCBI. Models based on random forest (RF) and weighted support vector machine (WSVM) were built to predict initiation and termination sites of NCs. Factor analysis scales of generalized amino acid information along with position weight matrix were utilized to generate the feature space. Homology based gene prediction methods were also compared and integrated to bring out better predicting performance. Candidate initiation and termination sites predicted were then combined and screened according to their intervals, decision values and alignment scores. All available gag sequences without NC annotations were scanned with the model to detect putative NCs. Geometric means of sensitivity and specificity generated from prediction of initiation and termination sites under fivefold cross-validation are 0.9900 and 0.9548 respectively. 90.91% of all the collected retrovirus sequences with NC annotations could be predicted totally correct by the model combining WSVM, RF and simple alignment. The composite model performs better than the simplex ones. 235 putative NCs in unannotated gags were detected by the model. Our prediction method performs well on NC recognition and could also be expanded to solve other gene prediction problems, especially those whose training samples have large length variations.
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Zhao BW, Hu L, You ZH, Wang L, Su XR. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform 2021; 23:6456295. [PMID: 34891172 DOI: 10.1093/bib/bbab515] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 12/20/2022] Open
Abstract
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering the biological knowledge of drugs and diseases, which are believed to be useful for improving the accuracy of drug repositioning. To this end, a novel heterogeneous information network (HIN) based model, namely HINGRL, is proposed to precisely identify new indications for drugs based on graph representation learning techniques. More specifically, HINGRL first constructs a HIN by integrating drug-disease, drug-protein and protein-disease biological networks with the biological knowledge of drugs and diseases. Then, different representation strategies are applied to learn the features of nodes in the HIN from the topological and biological perspectives. Finally, HINGRL adopts a Random Forest classifier to predict unknown drug-disease associations based on the integrated features of drugs and diseases obtained in the previous step. Experimental results demonstrate that HINGRL achieves the best performance on two real datasets when compared with state-of-the-art models. Besides, our case studies indicate that the simultaneous consideration of network topology and biological knowledge of drugs and diseases allows HINGRL to precisely predict drug-disease associations from a more comprehensive perspective. The promising performance of HINGRL also reveals that the utilization of rich heterogeneous information provides an alternative view for HINGRL to identify novel drug-disease associations especially for new diseases.
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Affiliation(s)
- Bo-Wei Zhao
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Lun Hu
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Wang
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 530007, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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Pan J, Li LP, Yu CQ, You ZH, Guan YJ, Ren ZH. Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest. Evol Bioinform Online 2021; 17:11769343211050067. [PMID: 34671178 PMCID: PMC8521741 DOI: 10.1177/11769343211050067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/13/2021] [Indexed: 11/24/2022] Open
Abstract
Protein-protein interactions (PPIs) in plants are essential for understanding the regulation of biological processes. Although high-throughput technologies have been widely used to identify PPIs, they are usually laborious, expensive, and suffer from high false-positive rates. Therefore, it is imperative to develop novel computational approaches as a supplement tool to detect PPIs in plants. In this work, we presented a method, namely DST-RoF, to identify PPIs in plants by combining an ensemble learning classifier-Rotation Forest (RoF) with discrete sine transformation (DST). Specifically, plant protein sequence is firstly converted into Position-Specific Scoring Matrix (PSSM). Then, the discrete sine transformation was employed to extract effective features for obtaining the evolutionary information of proteins. Finally, these optimal features were fed into the RoF classifier for training and prediction. When performed on the plant datasets Arabidopsis, Rice, and Maize, DST-RoF yielded high prediction accuracy of 82.95%, 88.82%, and 93.70%, respectively. To further evaluate the prediction ability of our approach, we compared it with 4 state-of-the-art classifiers and 3 different feature extraction methods. Comprehensive experimental results anticipated that our method is feasible and robust for predicting potential plant-protein interacted pairs.
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Affiliation(s)
- Jie Pan
- College of Information Engineering, Xijing University, Xi'an, China
| | - Li-Ping Li
- College of Information Engineering, Xijing University, Xi'an, China
| | - Chang-Qing Yu
- College of Information Engineering, Xijing University, Xi'an, China
| | - Zhu-Hong You
- College of Information Engineering, Xijing University, Xi'an, China
| | - Yong-Jian Guan
- College of Information Engineering, Xijing University, Xi'an, China
| | - Zhong-Hao Ren
- College of Information Engineering, Xijing University, Xi'an, China
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12
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Hu L, Wang X, Huang YA, Hu P, You ZH. A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology. Front Microbiol 2021; 12:735329. [PMID: 34512614 PMCID: PMC8425590 DOI: 10.3389/fmicb.2021.735329] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/02/2021] [Indexed: 11/24/2022] Open
Abstract
Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.
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Affiliation(s)
- Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Xiaojuan Wang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Yu-An Huang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Pengwei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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Vedithi SC, Malhotra S, Acebrón-García-de-Eulate M, Matusevicius M, Torres PHM, Blundell TL. Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae. Front Mol Biosci 2021; 8:663301. [PMID: 34026836 PMCID: PMC8138464 DOI: 10.3389/fmolb.2021.663301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.
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Affiliation(s)
- Sundeep Chaitanya Vedithi
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Sundeep Chaitanya Vedithi,
| | - Sony Malhotra
- Rutherford Appleton Laboratory, Science and Technology Facilities Council, Oxon, United Kingdom
| | | | | | - Pedro Henrique Monteiro Torres
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom,Tom L. Blundell,
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14
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Li Z, Hu L, Tang Z, Zhao C. Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning. Front Genet 2021; 12:658078. [PMID: 33868387 PMCID: PMC8044780 DOI: 10.3389/fgene.2021.658078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.
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Affiliation(s)
- Zhenfeng Li
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zehai Tang
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
| | - Cheng Zhao
- School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China
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15
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Hu L, Wang X, Huang YA, Hu P, You ZH. A survey on computational models for predicting protein-protein interactions. Brief Bioinform 2021; 22:6159365. [PMID: 33693513 DOI: 10.1093/bib/bbab036] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/31/2020] [Indexed: 12/24/2022] Open
Abstract
Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are often not robust and considerably uncertain. Due recently to advances in high-throughput technologies, a large amount of proteomics data has been collected and this presents a significant opportunity and also a challenge to develop computational models to predict protein-protein interactions (PPIs) based on these data. In this paper, we present a comprehensive survey of the recent efforts that have been made towards the development of effective computational models for PPI prediction. The survey introduces the algorithms that can be used to learn computational models for predicting PPIs, and it classifies these models into different categories. To understand their relative merits, the paper discusses different validation schemes and metrics to evaluate the prediction performance. Biological databases that are commonly used in different experiments for performance comparison are also described and their use in a series of extensive experiments to compare different prediction models are discussed. Finally, we present some open issues in PPI prediction for future work. We explain how the performance of PPI prediction can be improved if these issues are effectively tackled.
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Affiliation(s)
- Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 830011, Urumqi, China
| | - Xiaojuan Wang
- School of Computer Science and Technology, Wuhan University of Technology, 430070, Wuhan, China
| | - Yu-An Huang
- College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China
| | | | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, 830011, Urumqi, China
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16
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Hu L, Hu P, Luo X, Yuan X, You ZH. Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:2017-2028. [PMID: 31056514 DOI: 10.1109/tcbb.2019.2914208] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Human immunodeficiency virus 1 (HIV-1) protease (PR) plays a crucial role in the maturation of the virus. The study of substrate specificity of HIV-1 PR as a new endeavor strives to increase our ability to understand how HIV-1 PR recognizes its various cleavage sites. To predict HIV-1 PR cleavage sites, most of the existing approaches have been developed solely based on the homogeneity of substrate sequence information with supervised classification techniques. Although efficient, these approaches are found to be restricted to the ability of explaining their results and probably provide few insights into the mechanisms by which HIV-1 PR cleaves the substrates in a site-specific manner. In this work, a coevolutionary pattern-based prediction model for HIV-1 PR cleavage sites, namely EvoCleave, is proposed by integrating the coevolving information obtained from substrate sequences with a linear SVM classifier. The experiment results showed that EvoCleave yielded a very promising performance in terms of ROC analysis and f-measure. We also prospectively assessed the biological significance of coevolutionary patterns by applying them to study three fundamental issues of HIV-1 PR cleavage site. The analysis results demonstrated that the coevolutionary patterns offered valuable insights into the understanding of substrate specificity of HIV-1 PR.
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17
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Abstract
Background:
Thermophilic proteins can maintain good activity under high temperature,
therefore, it is important to study thermophilic proteins for the thermal stability of proteins.
Objective:
In order to solve the problem of low precision and low efficiency in predicting
thermophilic proteins, a prediction method based on feature fusion and machine learning was
proposed in this paper.
Methods:
For the selected thermophilic data sets, firstly, the thermophilic protein sequence was
characterized based on feature fusion by the combination of g-gap dipeptide, entropy density and
autocorrelation coefficient. Then, Kernel Principal Component Analysis (KPCA) was used to reduce
the dimension of the expressed protein sequence features in order to reduce the training time and
improve efficiency. Finally, the classification model was designed by using the classification
algorithm.
Results:
A variety of classification algorithms was used to train and test on the selected thermophilic
dataset. By comparison, the accuracy of the Support Vector Machine (SVM) under the jackknife
method was over 92%. The combination of other evaluation indicators also proved that the SVM
performance was the best.
Conclusion:
Because of choosing an effectively feature representation method and a robust
classifier, the proposed method is suitable for predicting thermophilic proteins and is superior to
most reported methods.
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Affiliation(s)
- Xian-Fang Wang
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Peng Gao
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Yi-Feng Liu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Hong-Fei Li
- School of Computer and Information Engineering, Henan Normal University, Henan, China
| | - Fan Lu
- School of Computer and Information Engineering, Henan Normal University, Henan, China
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18
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Poot Velez AH, Fontove F, Del Rio G. Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes. Int J Mol Sci 2020; 21:E4787. [PMID: 32640745 PMCID: PMC7370293 DOI: 10.3390/ijms21134787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 01/22/2023] Open
Abstract
Predicting protein-protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm-parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96-99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.
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Affiliation(s)
- Albros Hermes Poot Velez
- Department of biochemistry and structural biology, Instituto de fisiologia celular, UNAM Mexico City 04510, Mexico;
| | | | - Gabriel Del Rio
- Department of biochemistry and structural biology, Instituto de fisiologia celular, UNAM Mexico City 04510, Mexico;
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19
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An JY, Zhou Y, Yan ZJ, Zhao YJ. Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information. Evol Bioinform Online 2020; 16:1176934320924674. [PMID: 32550764 PMCID: PMC7278102 DOI: 10.1177/1176934320924674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 04/16/2020] [Indexed: 11/15/2022] Open
Abstract
Self-interacting proteins (SIPs) play crucial roles in biological activities of
organisms. Many high-throughput methods can be used to identify SIPs. However,
these methods are both time-consuming and expensive. How to develop effective
computational approaches for identifying SIPs is a challenging task. In the
article, we present a novel computational method called RRN-SIFT, which combines
the recurrent neural network (RNN) with scale invariant feature transform (SIFT)
to predict SIPs based on protein evolutionary information. The main advantage of
the proposed RNN-SIFT model is that it uses SIFT for extracting key feature by
exploring the evolutionary information embedded in Position-Specific Iterated
BLAST–constructed position-specific scoring matrix and employs an RNN classifier
to perform classification based on extracted features. Extensive experiments
show that the RRN-SIFT obtained average accuracy of 94.34% and 97.12% on the
yeast and human dataset, respectively. We
also compared our performance with the back propagation neural network (BPNN),
the state-of-the-art support vector machine (SVM), and other existing methods.
By comparing with experimental results, the performance of RNN-SIFT is
significantly better than that of the BPNN, SVM, and other previous methods in
the domain. Therefore, we conclude that the proposed RNN-SIFT model is a useful
tool for predicting SIPs, as well to solve other bioinformatics tasks. To
facilitate widely studies and encourage future proteomics research, a freely
available web server called RNN-SIFT-SIPs was developed at http://219.219.62.123:8888/RNNSIFT/ including the source code
and the SIP datasets.
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Affiliation(s)
- Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Zi-Ji Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Yu-Jun Zhao
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
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20
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A Novel Stochastic Block Model for Network-Based Prediction of Protein-Protein Interactions. INTELLIGENT COMPUTING THEORIES AND APPLICATION 2020. [DOI: 10.1007/978-3-030-60802-6_54] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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21
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Wang L, Wang HF, Liu SR, Yan X, Song KJ. Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest. Sci Rep 2019; 9:9848. [PMID: 31285519 PMCID: PMC6614364 DOI: 10.1038/s41598-019-46369-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 06/10/2019] [Indexed: 01/09/2023] Open
Abstract
Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions.
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Affiliation(s)
- Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China. .,Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, P.R. China.
| | - Hai-Feng Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China
| | - San-Rong Liu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China
| | - Xin Yan
- School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong, 277100, P.R. China.
| | - Ke-Jian Song
- School of information engineering, JiangXi University of Science and Technology, Ganzhou, Jiangxi, 341000, P.R. China
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22
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Yin C, Yau SST. A coevolution analysis for identifying protein-protein interactions by Fourier transform. PLoS One 2017; 12:e0174862. [PMID: 28430779 PMCID: PMC5400233 DOI: 10.1371/journal.pone.0174862] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 03/16/2017] [Indexed: 12/29/2022] Open
Abstract
Protein-protein interactions (PPIs) play key roles in life processes, such as signal transduction, transcription regulations, and immune response, etc. Identification of PPIs enables better understanding of the functional networks within a cell. Common experimental methods for identifying PPIs are time consuming and expensive. However, recent developments in computational approaches for inferring PPIs from protein sequences based on coevolution theory avoid these problems. In the coevolution theory model, interacted proteins may show coevolutionary mutations and have similar phylogenetic trees. The existing coevolution methods depend on multiple sequence alignments (MSA); however, the MSA-based coevolution methods often produce high false positive interactions. In this paper, we present a computational method using an alignment-free approach to accurately detect PPIs and reduce false positives. In the method, protein sequences are numerically represented by biochemical properties of amino acids, which reflect the structural and functional differences of proteins. Fourier transform is applied to the numerical representation of protein sequences to capture the dissimilarities of protein sequences in biophysical context. The method is assessed for predicting PPIs in Ebola virus. The results indicate strong coevolution between the protein pairs (NP-VP24, NP-VP30, NP-VP40, VP24-VP30, VP24-VP40, and VP30-VP40). The method is also validated for PPIs in influenza and E.coli genomes. Since our method can reduce false positive and increase the specificity of PPI prediction, it offers an effective tool to understand mechanisms of disease pathogens and find potential targets for drug design. The Python programs in this study are available to public at URL (https://github.com/cyinbox/PPI).
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Affiliation(s)
- Changchuan Yin
- Department of Mathematics, Statistics and Computer Science, The University of Illinois at Chicago, Chicago, IL 60607-7045, United States of America
| | - Stephen S. -T. Yau
- Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China
- * E-mail:
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