1
|
Magnano CS, Gitter A. Graph algorithms for predicting subcellular localization at the pathway level. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:145-156. [PMID: 36540972 PMCID: PMC9817068 DOI: 10.1142/9789811270611_0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Protein subcellular localization is an important factor in normal cellular processes and disease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization annotations from curated pathway databases. We also perform a case study where we construct biological pathways and predict localizations of human fibroblasts undergoing viral infection. Pathway localization prediction is a promising approach for integrating publicly available localization data into the analysis of large-scale biological data.
Collapse
Affiliation(s)
- Chris S Magnano
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Gitter
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
| |
Collapse
|
2
|
Li GP, Du PF, Shen ZA, Liu HY, Luo T. DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks. Front Genet 2020; 11:600454. [PMID: 33193746 PMCID: PMC7644922 DOI: 10.3389/fgene.2020.600454] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Accepted: 10/07/2020] [Indexed: 12/29/2022] Open
Abstract
Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.
Collapse
Affiliation(s)
- Guang-Ping Li
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Zi-Ang Shen
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Hang-Yu Liu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Tao Luo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| |
Collapse
|
3
|
Abstract
Background:Pseudouridine (Ψ) is the most abundant RNA modification and has important functions in a series of biological and cellular processes. Although experimental techniques have made great contributions to identify Ψ sites, they are still labor-intensive and costineffective. In the past few years, a series of computational approaches have been developed, which provided rapid and efficient approaches to identify Ψ sites.Results:To provide the readership with a clear landscape about the recent development in this important area, in this review, we summarized and compared the representative computational approaches developed for identifying Ψ sites. Moreover, future directions in computationally identifying Ψ sites were discussed as well.Conclusion:We anticipate that this review will provide novel insights into the researches on pseudouridine modification.
Collapse
Affiliation(s)
- Wei Chen
- School of Life Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063210, China
| | - Kewei Liu
- School of Life Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063210, China
| |
Collapse
|
4
|
Taxonomy dimension reduction for colorectal cancer prediction. Comput Biol Chem 2019; 83:107160. [DOI: 10.1016/j.compbiolchem.2019.107160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 11/02/2019] [Accepted: 11/04/2019] [Indexed: 02/01/2023]
|
5
|
Yi HC, You ZH, Zhou X, Cheng L, Li X, Jiang TH, Chen ZH. ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:1-9. [PMID: 31173946 PMCID: PMC6554234 DOI: 10.1016/j.omtn.2019.04.025] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/08/2019] [Accepted: 04/08/2019] [Indexed: 01/10/2023]
Abstract
Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.
Collapse
Affiliation(s)
- Hai-Cheng Yi
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Li Cheng
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Xiao Li
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Tong-Hai Jiang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zhan-Heng Chen
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| |
Collapse
|
6
|
Mirzaei Mehrabad E, Hassanzadeh R, Eslahchi C. PMLPR: A novel method for predicting subcellular localization based on recommender systems. Sci Rep 2018; 8:12006. [PMID: 30104743 PMCID: PMC6089892 DOI: 10.1038/s41598-018-30394-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 07/30/2018] [Indexed: 12/16/2022] Open
Abstract
The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.
Collapse
Affiliation(s)
- Elnaz Mirzaei Mehrabad
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
| | - Reza Hassanzadeh
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran
- Department of Bioinformatics, Faculty of Computer Engineering and Information Technology, Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Changiz Eslahchi
- Department of Computer Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| |
Collapse
|
7
|
Zuo Y, Jia CZ. CarSite: identifying carbonylated sites of human proteins based on a one-sided selection resampling method. MOLECULAR BIOSYSTEMS 2018; 13:2362-2369. [PMID: 28937156 DOI: 10.1039/c7mb00363c] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Protein carbonylation is one of the most important biomarkers of oxidative protein damage and such protein damage is linked to various diseases and aging. It is thus vital that carbonylation sites are identified accurately. In this study, CarSite, a novel bioinformatics tool, was established to identify carbonylation sites in human proteins. The one-sided selection (OSS) resampling method was used to establish balanced training datasets and this resampling method is demonstrated to perform better than a Monte Carlo resampling method via 10-fold cross-validation tests on the Jia dataset. Moreover, the hybrid combination of position-specific amino acid propensity (PSAAP), composition of k-spaced amino acid pairs (CKSAAP), amino acid composition (AAC), and composition of hydrophobic and hydrophilic amino acids (CHHAA) was selected to optimize the performance of the predictor. On 10-fold cross-validation of the Jia dataset, CarSite obtained rates of sensitivity corresponding to K/P/R/T-type peptides of ∼21%, 22%, 19%, or 18% higher than those obtained by iCar-PseCp, respectively, which was previously considered as the best predictor for identifying carbonylation sites in human proteins. Furthermore, compared with other existing predictors, CarSite obtained much higher sensitivity and accuracy when tested on the same dataset.
Collapse
Affiliation(s)
- Yun Zuo
- Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian, 116026, China.
| | | |
Collapse
|
8
|
Du PF, Zhao W, Miao YY, Wei LY, Wang L. UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences. Int J Mol Sci 2017; 18:ijms18112400. [PMID: 29135934 PMCID: PMC5713368 DOI: 10.3390/ijms18112400] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 11/01/2017] [Accepted: 11/03/2017] [Indexed: 01/12/2023] Open
Abstract
With the avalanche of biological sequences in public databases, one of the most challenging problems in computational biology is to predict their biological functions and cellular attributes. Most of the existing prediction algorithms can only handle fixed-length numerical vectors. Therefore, it is important to be able to represent biological sequences with various lengths using fixed-length numerical vectors. Although several algorithms, as well as software implementations, have been developed to address this problem, these existing programs can only provide a fixed number of representation modes. Every time a new sequence representation mode is developed, a new program will be needed. In this paper, we propose the UltraPse as a universal software platform for this problem. The function of the UltraPse is not only to generate various existing sequence representation modes, but also to simplify all future programming works in developing novel representation modes. The extensibility of UltraPse is particularly enhanced. It allows the users to define their own representation mode, their own physicochemical properties, or even their own types of biological sequences. Moreover, UltraPse is also the fastest software of its kind. The source code package, as well as the executables for both Linux and Windows platforms, can be downloaded from the GitHub repository.
Collapse
Affiliation(s)
- Pu-Feng Du
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Wei Zhao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Yang-Yang Miao
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
- School of Chemical Engineering, Tianjin University, Tianjin 300350, China.
| | - Le-Yi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
| | - Likun Wang
- Institute of Systems Biomedicine, Beijing Key Laboratory of Tumor Systems Biology, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
| |
Collapse
|
9
|
Du PF. Predicting Protein Submitochondrial Locations: The 10th Anniversary. Curr Genomics 2017; 18:316-321. [PMID: 29081687 PMCID: PMC5635615 DOI: 10.2174/1389202918666170228143256] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/16/2016] [Accepted: 11/02/2016] [Indexed: 12/16/2022] Open
Abstract
Predicting protein submitochondrial location has been studied for about ten years. A number of methods have been developed. The prediction performances have been improved to an almost perfect level. In this review, we introduce the background of this research topic. We also compare the methods, the performances and the datasets that have been used by these studies. Towards the end, we provide hints for the future directions of this research topic.
Collapse
Affiliation(s)
- Pu-Feng Du
- School of Computer Science and Technology, Tianjin University, Tianjin300350, China
| |
Collapse
|
10
|
Pan Y, Liu D, Deng L. Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties. PLoS One 2017; 12:e0179314. [PMID: 28614374 PMCID: PMC5470696 DOI: 10.1371/journal.pone.0179314] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Accepted: 05/27/2017] [Indexed: 12/20/2022] Open
Abstract
Single amino acid variations (SAVs) potentially alter biological functions, including causing diseases or natural differences between individuals. Identifying the relationship between a SAV and certain disease provides the starting point for understanding the underlying mechanisms of specific associations, and can help further prevention and diagnosis of inherited disease.We propose PredSAV, a computational method that can effectively predict how likely SAVs are to be associated with disease by incorporating gradient tree boosting (GTB) algorithm and optimally selected neighborhood features. A two-step feature selection approach is used to explore the most relevant and informative neighborhood properties that contribute to the prediction of disease association of SAVs across a wide range of sequence and structural features, especially some novel structural neighborhood features. In cross-validation experiments on the benchmark dataset, PredSAV achieves promising performances with an AUC score of 0.908 and a specificity of 0.838, which are significantly better than that of the other existing methods. Furthermore, we validate the capability of our proposed method by an independent test and gain a competitive advantage as a result. PredSAV, which combines gradient tree boosting with optimally selected neighborhood features, can return reliable predictions in distinguishing between disease-associated and neutral variants. Compared with existing methods, PredSAV shows improved specificity as well as increased overall performance.
Collapse
Affiliation(s)
- Yuliang Pan
- School of Software, Central South University, Changsha, China
| | - Diwei Liu
- School of Software, Central South University, Changsha, China
| | - Lei Deng
- School of Software, Central South University, Changsha, China
- Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
| |
Collapse
|
11
|
Breckels LM, Holden SB, Wojnar D, Mulvey CM, Christoforou A, Groen A, Trotter MWB, Kohlbacher O, Lilley KS, Gatto L. Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol 2016; 12:e1004920. [PMID: 27175778 PMCID: PMC4866734 DOI: 10.1371/journal.pcbi.1004920] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 04/16/2016] [Indexed: 11/19/2022] Open
Abstract
Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis. Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.
Collapse
Affiliation(s)
- Lisa M. Breckels
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Sean B. Holden
- Computer Laboratory, University of Cambridge, Cambridge, United Kingdom
| | - David Wojnar
- Quantitative Biology Center, Universität Tübingen, Tübingen, Germany
| | - Claire M. Mulvey
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Andy Christoforou
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Arnoud Groen
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | | | - Oliver Kohlbacher
- Quantitative Biology Center, Universität Tübingen, Tübingen, Germany
- Center for Bioinformatics, Universität Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Kathryn S. Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
| | - Laurent Gatto
- Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
- * E-mail:
| |
Collapse
|
12
|
iDPF-PseRAAAC: A Web-Server for Identifying the Defensin Peptide Family and Subfamily Using Pseudo Reduced Amino Acid Alphabet Composition. PLoS One 2015; 10:e0145541. [PMID: 26713618 PMCID: PMC4694767 DOI: 10.1371/journal.pone.0145541] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 12/04/2015] [Indexed: 11/29/2022] Open
Abstract
Defensins as one of the most abundant classes of antimicrobial peptides are an essential part of the innate immunity that has evolved in most living organisms from lower organisms to humans. To identify specific defensins as interesting antifungal leads, in this study, we constructed a more rigorous benchmark dataset and the iDPF-PseRAAAC server was developed to predict the defensin family and subfamily. Using reduced dipeptide compositions were used, the overall accuracy of proposed method increased to 95.10% for the defensin family, and 98.39% for the vertebrate subfamily, which is higher than the accuracy from other methods. The jackknife test shows that more than 4% improvement was obtained comparing with the previous method. A free online server was further established for the convenience of most experimental scientists at http://wlxy.imu.edu.cn/college/biostation/fuwu/iDPF-PseRAAAC/index.asp. A friendly guide is provided to describe how to use the web server. We anticipate that iDPF-PseRAAAC may become a useful high-throughput tool for both basic research and drug design.
Collapse
|
13
|
Ding H, Feng PM, Chen W, Lin H. Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. MOLECULAR BIOSYSTEMS 2015; 10:2229-35. [PMID: 24931825 DOI: 10.1039/c4mb00316k] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells. Accurate identification of bacteriophage virion proteins is very important for understanding their functions and clarifying the lysis mechanism of bacterial cells. In this study, a new sequence-based method was developed to identify phage virion proteins. In the new method, the protein sequences were initially formulated by the g-gap dipeptide compositions. Subsequently, the analysis of variance (ANOVA) with incremental feature selection (IFS) was used to search for the optimal feature set. It was observed that, in jackknife cross-validation, the optimal feature set including 160 optimized features can produce the maximum accuracy of 85.02%. By performing feature analysis, we found that the correlation between two amino acids with one gap was more important than other correlations for phage virion protein prediction and that some of the 1-gap dipeptides were important and mainly contributed to the virion protein prediction. This analysis will provide novel insights into the function of phage virion proteins. On the basis of the proposed method, an online web-server, PVPred, was established and can be freely accessed from the website (http://lin.uestc.edu.cn/server/PVPred). We believe that the PVPred will become a powerful tool to study phage virion proteins and to guide the related experimental validations.
Collapse
Affiliation(s)
- Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | | | | | | |
Collapse
|
14
|
Li L, Yu S, Xiao W, Li Y, Hu W, Huang L, Zheng X, Zhou S, Yang H. Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction. MOLECULAR BIOSYSTEMS 2015; 11:170-7. [PMID: 25335193 DOI: 10.1039/c4mb00340c] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Mitochondrion, a tiny energy factory, plays an important role in various biological processes of most eukaryotic cells.
Collapse
Affiliation(s)
- Liqi Li
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Sanjiu Yu
- Institute of Cardiovascular Diseases of PLA
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Weidong Xiao
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Yongsheng Li
- Institute of Cancer
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Wenjuan Hu
- Department of Pathophysiology and High Altitude Pathology
- College of High Altitude Military Medicine
- Third Military Medical University
- Chongqing 400038
- China
| | - Lan Huang
- Institute of Cardiovascular Diseases of PLA
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Xiaoqi Zheng
- Department of Mathematics
- Shanghai Normal University
- Shanghai 200234
- China
| | - Shiwen Zhou
- National Drug Clinical Trial Institution
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| | - Hua Yang
- Department of General Surgery
- Xinqiao Hospital
- Third Military Medical University
- Chongqing 400037
- China
| |
Collapse
|
15
|
Ding H, Feng PM, Chen W, Lin H. Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis. MOLECULAR BIOSYSTEMS 2014. [DOI: 10.1039/c4mb00316k pmid: 24931825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells.
Collapse
Affiliation(s)
- Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054, China
| | - Peng-Mian Feng
- School of Public Health
- Hebei United University
- Tangshan 063000, China
| | - Wei Chen
- Department of Physics
- School of Sciences
- and Center for Genomics and Computational Biology
- Hebei United University
- Tangshan 063000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education
- Center of Bioinformatics
- School of Life Science and Technology
- University of Electronic Science and Technology of China
- Chengdu 610054, China
| |
Collapse
|