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Barradas-Bautista D, Rosell M, Pallara C, Fernández-Recio J. Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems. PROTEIN-PROTEIN INTERACTIONS IN HUMAN DISEASE, PART A 2018; 110:203-249. [DOI: 10.1016/bs.apcsb.2017.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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52
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Vyas R, Bapat S, Goel P, Karthikeyan M, Tambe SS, Kulkarni BD. Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:27-37. [PMID: 28113781 DOI: 10.1109/tcbb.2016.2621042] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.
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53
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Peng X, Wang J, Peng W, Wu FX, Pan Y. Protein-protein interactions: detection, reliability assessment and applications. Brief Bioinform 2017; 18:798-819. [PMID: 27444371 DOI: 10.1093/bib/bbw066] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Indexed: 01/06/2023] Open
Abstract
Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.
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54
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Kotlyar M, Rossos AEM, Jurisica I. Prediction of Protein-Protein Interactions. ACTA ACUST UNITED AC 2017; 60:8.2.1-8.2.14. [PMID: 29220074 DOI: 10.1002/cpbi.38] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Max Kotlyar
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Andrea E M Rossos
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Departments of Medical Biophysics and Computer Science, University of Toronto, Ontario, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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55
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Li Y, Ilie L. SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome. BMC Bioinformatics 2017; 18:485. [PMID: 29141584 PMCID: PMC5688644 DOI: 10.1186/s12859-017-1871-x] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/17/2017] [Indexed: 12/30/2022] Open
Abstract
Background Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory. Results We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory. Conclusion SPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. Availability The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/
and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1871-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiwei Li
- Department of Computer Science, The University of Western Ontario, London, N6A 5B7, Ontario, Canada
| | - Lucian Ilie
- Department of Computer Science, The University of Western Ontario, London, N6A 5B7, Ontario, Canada.
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56
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Li ZW, You ZH, Chen X, Li LP, Huang DS, Yan GY, Nie R, Huang YA. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier. Oncotarget 2017; 8:23638-23649. [PMID: 28423569 PMCID: PMC5410333 DOI: 10.18632/oncotarget.15564] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Accepted: 01/11/2017] [Indexed: 11/25/2022] Open
Abstract
Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.
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Affiliation(s)
- Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Li-Ping Li
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
| | - De-Shuang Huang
- School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
| | - Gui-Ying Yan
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Ru Nie
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Yu-An Huang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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57
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Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep 2017; 7:10480. [PMID: 28874689 PMCID: PMC5585393 DOI: 10.1038/s41598-017-09654-8] [Citation(s) in RCA: 478] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 07/28/2017] [Indexed: 01/01/2023] Open
Abstract
Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.
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Affiliation(s)
- Martino Bertoni
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Florian Kiefer
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Marco Biasini
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Lorenza Bordoli
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland.,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Torsten Schwede
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland. .,Biozentrum, University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland.
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58
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Mirabello C, Wallner B. InterPred: A pipeline to identify and model protein-protein interactions. Proteins 2017; 85:1159-1170. [DOI: 10.1002/prot.25280] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 02/27/2017] [Accepted: 03/01/2017] [Indexed: 12/22/2022]
Affiliation(s)
- Claudio Mirabello
- Division of Bioinformatics, Department of Physics, Chemistry and Biology; Linköping University; Linköping 581 83 Sweden
| | - Björn Wallner
- Division of Bioinformatics, Department of Physics, Chemistry and Biology; Linköping University; Linköping 581 83 Sweden
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59
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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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60
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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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61
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Muthu Krishnan S. Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach. J Theor Biol 2016; 409:27-37. [PMID: 27575465 DOI: 10.1016/j.jtbi.2016.08.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 08/11/2016] [Accepted: 08/16/2016] [Indexed: 01/26/2023]
Abstract
Hemoglobin is an oxygen-binding protein widely present in all kingdoms of life from prokaryotic to eukaryotic, but well established in the vertebrate system. An attempt was made to determine the Vertebrate hemoglobin (VerHb) protein on their animal classifications, based on general pseudo amino acid composition (PseAAC)'s evolutionary profiles and hybrid approach. The support vector machine (SVM) has been applied to develop all models, the prediction results further compared according to their animal classification. The performance of the approaches estimated using five-fold cross-validation techniques. The prediction performance was further investigated by receiver operating characteristic (ROC) and prediction score graphs. The prediction accuracy (ACC), sensitivity (SN) and specificity (SP) were examined to find the accurate predictions on the threshold level. Based on the approach, a web-tool has been developed for identifying the VerHb proteins.
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Affiliation(s)
- S Muthu Krishnan
- CSIR - Institute of Microbial Technology (IMTECH), Sector-39A, Chandigarh, India.
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62
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Rost B, Radivojac P, Bromberg Y. Protein function in precision medicine: deep understanding with machine learning. FEBS Lett 2016; 590:2327-41. [PMID: 27423136 PMCID: PMC5937700 DOI: 10.1002/1873-3468.12307] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 07/12/2016] [Accepted: 07/12/2016] [Indexed: 12/21/2022]
Abstract
Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
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Affiliation(s)
- Burkhard Rost
- Department of Informatics and Bioinformatics, Institute for Advanced Studies, Technical University of Munich, Garching, Germany
| | - Predrag Radivojac
- School of Informatics and Computing, Indiana University, Bloomington, IN, USA
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USA
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63
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Yue J, Xu W, Ban R, Huang S, Miao M, Tang X, Liu G, Liu Y. PTIR: Predicted Tomato Interactome Resource. Sci Rep 2016; 6:25047. [PMID: 27121261 PMCID: PMC4848565 DOI: 10.1038/srep25047] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 04/08/2016] [Indexed: 01/18/2023] Open
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.
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Affiliation(s)
- Junyang Yue
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Wei Xu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Rongjun Ban
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
| | - Shengxiong Huang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Min Miao
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Xiaofeng Tang
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Guoqing Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
| | - Yongsheng Liu
- School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei 230009, China
- Ministry of Education Key Laboratory for Bio-resource and Eco-environment, College of Life Science, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, China
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64
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Abbasi WA, Minhas FUAA. Issues in performance evaluation for host-pathogen protein interaction prediction. J Bioinform Comput Biol 2016; 14:1650011. [PMID: 26932275 DOI: 10.1142/s0219720016500116] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein-protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host-pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose.
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Affiliation(s)
- Wajid Arshad Abbasi
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
| | - Fayyaz Ul Amir Afsar Minhas
- 1 Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Nilore, Islamabad, Pakistan
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65
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Muratcioglu S, Presman DM, Pooley JR, Grøntved L, Hager GL, Nussinov R, Keskin O, Gursoy A. Structural Modeling of GR Interactions with the SWI/SNF Chromatin Remodeling Complex and C/EBP. Biophys J 2015; 109:1227-39. [PMID: 26278180 PMCID: PMC4576152 DOI: 10.1016/j.bpj.2015.06.044] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 06/23/2015] [Accepted: 06/23/2015] [Indexed: 11/23/2022] Open
Abstract
The glucocorticoid receptor (GR) is a steroid-hormone-activated transcription factor that modulates gene expression. Transcriptional regulation by the GR requires dynamic receptor binding to specific target sites located across the genome. This binding remodels the chromatin structure to allow interaction with other transcription factors. Thus, chromatin remodeling is an essential component of GR-mediated transcriptional regulation, and understanding the interactions between these molecules at the structural level provides insights into the mechanisms of how GR and chromatin remodeling cooperate to regulate gene expression. This study suggests models for the assembly of the SWI/SNF-A (SWItch/Sucrose-NonFermentable) complex and its interaction with the GR. We used the PRISM algorithm (PRotein Interactions by Structural Matching) to predict the three-dimensional complex structures of the target proteins. The structural models indicate that BAF57 and/or BAF250 mediate the interaction between the GR and the SWI/SNF-A complex, corroborating experimental data. They further suggest that a BAF60a/BAF155 and/or BAF60a/BAF170 interaction is critical for association between the core and variant subunits. Further, we model the interaction between GR and CCAAT-enhancer-binding proteins (C/EBPs), since the GR can regulate gene expression indirectly by interacting with other transcription factors like C/EBPs. We observe that GR can bind to bZip domains of the C/EBPα homodimer as both a monomer and dimer of the DNA-binding domain. In silico mutagenesis of the predicted interface residues confirm the importance of these residues in binding. In vivo analysis of the computationally suggested mutations reveals that double mutations of the leucine residues (L317D+L335D) may disrupt the interaction between GR and C/EBPα. Determination of the complex structures of the GR is of fundamental relevance to understanding its interactions and functions, since the function of a protein or a complex is dictated by its structure. In addition, it may help us estimate the effects of mutations on GR interactions and signaling.
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Affiliation(s)
- Serena Muratcioglu
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey
| | - Diego M Presman
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - John R Pooley
- Henry Wellcome Laboratories for Integrated Neuroscience and Endocrinology, University of Bristol, Bristol, United Kingdom
| | - Lars Grøntved
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark
| | - Gordon L Hager
- Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Ruth Nussinov
- Cancer and Inflammation Program, Basic Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey; Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey.
| | - Attila Gursoy
- Center for Computational Biology and Bioinformatics, Koc University, Istanbul, Turkey; Department of Computer Engineering, Koc University, Istanbul, Turkey.
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