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Gao Q, Zhang C, Li M, Yu T. Protein-Protein Interaction Prediction Model Based on ProtBert-BiGRU-Attention. J Comput Biol 2024. [PMID: 39069885 DOI: 10.1089/cmb.2023.0297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024] Open
Abstract
The physiological activities within cells are mainly regulated through protein-protein interactions (PPI). Therefore, studying protein interactions has become an essential part of researching protein function and mechanisms. Traditional biological experiments required for PPI prediction are expensive and time consuming. For this reason, many methods based on predicting PPI from protein sequences have been proposed in recent years. However, existing computational methods usually require the combination of evolutionary feature information of proteins to predict PPI docking situations. Because different relevant features of selected proteins are chosen, there may be differences in the predicted results for PPI. This article proposes a PPI prediction method based on the pretrained protein sequence model ProtBert, combined with the Bidirectional Gated Recurrent Unit (BiGRU) and attention mechanism. Only using protein sequence information and leveraging ProtBert's powerful ability to capture amino acid feature information, BiGRU is used for further feature extraction of the amino acid vectors output by ProtBert. The attention mechanism is then applied to enhance the focus on different amino acid features and improve the expression ability of protein sequence features, ultimately obtaining binary classification results for protein interactions. Experimental results show that our proposed ProtBert-BiGRU-Attention model has good predictive performance for PPI. Through relevant comparative experiments, it has been proven that our model performs well in protein binary prediction. Furthermore, through the ablation experiment of the model, different deep learning modules' contributions to the prediction have been demonstrated.
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Affiliation(s)
- Qian Gao
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Chi Zhang
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Ming Li
- College of Computer and Control Engineering, Qiqihar University, Qiqihar, China
| | - Tianfei Yu
- College of Life Science and Agriculture Forestry, Qiqihar University, Qiqihar, China
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2
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Humphreys IR, Zhang J, Baek M, Wang Y, Krishnakumar A, Pei J, Anishchenko I, Tower CA, Jackson BA, Warrier T, Hung DT, Peterson SB, Mougous JD, Cong Q, Baker D. Essential and virulence-related protein interactions of pathogens revealed through deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589144. [PMID: 38645026 PMCID: PMC11030334 DOI: 10.1101/2024.04.12.589144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.
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3
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Mezghrani A, Simon J, Reys V, Labesse G. Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server. Methods Mol Biol 2024; 2836:253-281. [PMID: 38995545 DOI: 10.1007/978-1-0716-4007-4_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.
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Affiliation(s)
- Alexandre Mezghrani
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France
| | - Juliette Simon
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France
| | - Victor Reys
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France.
| | - Gilles Labesse
- Centre de Biologie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier, France.
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4
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James K, Alsobhe A, Cockell SJ, Wipat A, Pocock M. Integration of probabilistic functional networks without an external Gold Standard. BMC Bioinformatics 2022; 23:302. [PMID: 35879662 PMCID: PMC9316706 DOI: 10.1186/s12859-022-04834-4] [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: 11/16/2021] [Accepted: 07/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Probabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. Additionally, there typically are no Gold Standard data for non-model organisms. RESULTS We describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping. In addition ssNet results in less loss of data and produces a more complete network. CONCLUSIONS The ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source and with reduced data loss.
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Affiliation(s)
- Katherine James
- Department of Applied Sciences, Northumbria University, Sandyford Rd, Newcastle upon Tyne, NE1 8ST, UK. .,Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.
| | - Aoesha Alsobhe
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK.,Saudi Electronic University, Abi Bakr As Siddiq Branch Rd, Riyadh, 1332, Saudi Arabia
| | - Simon J Cockell
- School of Biomedical, Nutritional and Sports Science, Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - Anil Wipat
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
| | - Matthew Pocock
- Interdisciplinary Computing and Complex BioSystems Group, Newcastle University, Science Square, Newcastle upon Tyne, NE4 5TG, UK
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5
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Humphreys IR, Pei J, Baek M, Krishnakumar A, Anishchenko I, Ovchinnikov S, Zhang J, Ness TJ, Banjade S, Bagde SR, Stancheva VG, Li XH, Liu K, Zheng Z, Barrero DJ, Roy U, Kuper J, Femández IS, Szakal B, Branzei D, Rizo J, Kisker C, Greene EC, Biggins S, Keeney S, Miller EA, Fromme JC, Hendrickson TL, Cong Q, Baker D. Computed structures of core eukaryotic protein complexes. Science 2021; 374:eabm4805. [PMID: 34762488 PMCID: PMC7612107 DOI: 10.1126/science.abm4805] [Citation(s) in RCA: 251] [Impact Index Per Article: 83.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Protein-protein interactions play critical roles in biology, but the structures of many eukaryotic protein complexes are unknown, and there are likely many interactions not yet identified. We take advantage of advances in proteome-wide amino acid coevolution analysis and deep-learning–based structure modeling to systematically identify and build accurate models of core eukaryotic protein complexes within the Saccharomyces cerevisiae proteome. We use a combination of RoseTTAFold and AlphaFold to screen through paired multiple sequence alignments for 8.3 million pairs of yeast proteins, identify 1505 likely to interact, and build structure models for 106 previously unidentified assemblies and 806 that have not been structurally characterized. These complexes, which have as many as five subunits, play roles in almost all key processes in eukaryotic cells and provide broad insights into biological function.
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Affiliation(s)
- Ian R. Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Minkyung Baek
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Aditya Krishnakumar
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Ivan Anishchenko
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Sergey Ovchinnikov
- Faculty of Arts and Sciences, Division of Science, Harvard University, Cambridge, MA, USA
- John Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Travis J. Ness
- Department of Chemistry, Wayne State University, Detroit, MI, USA
| | - Sudeep Banjade
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | - Saket R. Bagde
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | | | - Xiao-Han Li
- MRC Laboratory of Molecular Biology, Cambridge, CB2 0QH, UK
| | - Kaixian Liu
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Zhi Zheng
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY
| | - Daniel J. Barrero
- Howard Hughes Medical Institute, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Upasana Roy
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Jochen Kuper
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
| | - Israel S. Femández
- Department of Structural Biology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Barnabas Szakal
- IFOM, the FIRC Institute of Molecular Oncology, Via Adamello 16, 20139, Milan, Italy
| | - Dana Branzei
- IFOM, the FIRC Institute of Molecular Oncology, Via Adamello 16, 20139, Milan, Italy
- Istituto di Genetica Molecolare, Consiglio Nazionale delle Ricerche (IGM-CNR), Via Abbiategrasso 207, 27100, Pavia, Italy
| | - Josep Rizo
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Caroline Kisker
- Rudolf Virchow Center for Integrative and Translational Bioimaging, University of Würzburg, Würzburg, Germany
| | - Eric C. Greene
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Sue Biggins
- Howard Hughes Medical Institute, Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Scott Keeney
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - J. Christopher Fromme
- Department of Molecular Biology & Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, USA
| | | | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA
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6
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Zhang X, Wang W, Ren CX, Dai DQ. Learning representation for multiple biological networks via a robust graph regularized integration approach. Brief Bioinform 2021; 23:6381251. [PMID: 34607360 DOI: 10.1093/bib/bbab409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 08/23/2021] [Accepted: 09/06/2021] [Indexed: 01/18/2023] Open
Abstract
Learning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network-based inference tasks, including drug-target interaction prediction, gene function identification and fine-grained species categorization, are conducted using representation features learned from our method. Biological networks at different scales and levels of sparsity are involved. Experimental results on real-world data show that the proposed method has robust performance compared with alternatives. Overall, by eliminating noise and integrating effectively, the proposed method is able to learn useful representations from multiple biological networks.
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Affiliation(s)
- Xiwen Zhang
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Weiwen Wang
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Chuan-Xian Ren
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
| | - Dao-Qing Dai
- Intelligent Data Center, School of Mathematics, Sun Yat-Sen University, 510275, Guangzhou, China
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7
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Ding P, Ouyang W, Luo J, Kwoh CK. Heterogeneous information network and its application to human health and disease. Brief Bioinform 2021; 21:1327-1346. [PMID: 31566212 DOI: 10.1093/bib/bbz091] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 06/29/2019] [Accepted: 06/30/2019] [Indexed: 12/11/2022] Open
Abstract
The molecular components with the functional interdependencies in human cell form complicated biological network. Diseases are mostly caused by the perturbations of the composite of the interaction multi-biomolecules, rather than an abnormality of a single biomolecule. Furthermore, new biological functions and processes could be revealed by discovering novel biological entity relationships. Hence, more and more biologists focus on studying the complex biological system instead of the individual biological components. The emergence of heterogeneous information network (HIN) offers a promising way to systematically explore complicated and heterogeneous relationships between various molecules for apparently distinct phenotypes. In this review, we first present the basic definition of HIN and the biological system considered as a complex HIN. Then, we discuss the topological properties of HIN and how these can be applied to detect network motif and functional module. Afterwards, methodologies of discovering relationships between disease and biomolecule are presented. Useful insights on how HIN aids in drug development and explores human interactome are provided. Finally, we analyze the challenges and opportunities for uncovering combinatorial patterns among pharmacogenomics and cell-type detection based on single-cell genomic data.
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Affiliation(s)
- Pingjian Ding
- School of Computer Science, University of South China, Hengyang, China
| | - Wenjue Ouyang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chee-Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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8
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Zhang W, Meng Q, Tang J, Guo F. Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset. Brief Bioinform 2021; 22:6265196. [PMID: 33959764 DOI: 10.1093/bib/bbab150] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/27/2021] [Indexed: 12/27/2022] Open
Abstract
Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.
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Affiliation(s)
- Wei Zhang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Qiaozhen Meng
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, U.S.,Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, China
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China
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9
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Kirykowicz AM, Woodward JD. Shotgun EM of mycobacterial protein complexes during stationary phase stress. Curr Res Struct Biol 2020; 2:204-212. [PMID: 34235480 PMCID: PMC8244302 DOI: 10.1016/j.crstbi.2020.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 10/27/2022] Open
Abstract
There is little structural information about the protein complexes conferring resistance in Mycobacterium tuberculosis (Mtb) to anti-microbial oxygen and nitrogen radicals in the phagolysosome. Here, we expose the model Mycobacterium, Mycobacterium smegmatis, to simulated oxidative-stress conditions and apply a shotgun EM method for the structural detection of the resulting protein assemblies. We identified: glutamine synthetase I, essential for Mtb virulence; bacterioferritin A, critical for Mtb iron regulation; aspartyl aminopeptidase M18, a protease; and encapsulin, which produces a cage-like structure to enclose cargo proteins. After further investigation, we found that encapsulin carries dye-decolourising peroxidase, a protein antioxidant, as its primary cargo under the conditions tested.
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Affiliation(s)
- Angela M. Kirykowicz
- Department of Biochemistry, University of Cambridge, Sanger Building, Tennis Court Road, Cambridge, CB2 1GA, UK
- Division of Medical Biochemistry and Structural Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Anzio Road, Observatory, 7925, Cape Town, South Africa
| | - Jeremy D. Woodward
- Division of Medical Biochemistry and Structural Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Anzio Road, Observatory, 7925, Cape Town, South Africa
- Structural Biology Research Unit, University of Cape Town, South Africa
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10
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Perscheid C. Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches. Brief Bioinform 2020; 22:5881664. [PMID: 32761115 DOI: 10.1093/bib/bbaa151] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.
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Affiliation(s)
- Cindy Perscheid
- Hasso Plattner Institute, University of Potsdam, Potsdam, 14482, Germany
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11
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Yerneni S, Khan IK, Wei Q, Kihara D. IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1247-1258. [PMID: 26415209 DOI: 10.1109/tcbb.2015.2476809] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for understanding the functions of proteins and pathways. PPIs are determined primarily by experimental methods; however, computational PPI prediction methods can supplement or verify PPIs identified by experiment. Here, we developed a novel scoring method for predicting PPIs from Gene Ontology (GO) annotations of proteins. Unlike existing methods that consider functional similarity as an indication of interaction between proteins, the new score, named the protein-protein Interaction Association Score (IAS), was computed from GO term associations of known interacting protein pairs in 49 organisms. IAS was evaluated on PPI data of six organisms and found to outperform existing GO term-based scoring methods. Moreover, consensus scoring methods that combine different scores further improved performance of PPI prediction.
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12
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Abstract
High-throughput, low-cost sequencing technologies have begun to yield new insights into biology and medicine. New data enable the interrogation of the molecular biology of disease from DNA to RNA to protein, charting the central dogma. This chapter reviews some of the key advances and resources in the application of bioinformatics to understanding, and ultimately diagnosing and treating, diseases of the nervous system. Array genotyping, exome sequencing, and whole-genome sequencing, in both disease and healthy populations, have enabled the interpretation of new genetic data. Profiling of epigenetic markers, such as histone modifications, has added to our understanding of the regulatory machinery of the genome. Downstream, mRNA, and protein expression data from published experiments and high-throughput studies enable complex analyses of gene function across many experimental conditions and tissues. Further delineation of molecular mechanism arises from the concept of genes working together in pathways or networks, reflecting direct protein interactions and regulatory relationships. The rapidly moving field of bioinformatics has made significant contributions to neurology in these early days; continued advances promise to transform medicine from basic science to clinical practice, as more genomics data are generated, combined, and analyzed in the future.
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Affiliation(s)
- Jason Chen
- Interdepartmental Program in Bioinformatics and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States
| | - Giovanni Coppola
- Interdepartmental Program in Bioinformatics and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, United States.
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13
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Meysman P, Titeca K, Eyckerman S, Tavernier J, Goethals B, Martens L, Valkenborg D, Laukens K. Protein complex analysis: From raw protein lists to protein interaction networks. MASS SPECTROMETRY REVIEWS 2017; 36:600-614. [PMID: 26709718 DOI: 10.1002/mas.21485] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2015] [Accepted: 11/17/2015] [Indexed: 06/05/2023]
Abstract
The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.
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Affiliation(s)
- Pieter Meysman
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - Kevin Titeca
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Sven Eyckerman
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Bart Goethals
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - Lennart Martens
- Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium
- Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium
| | - Dirk Valkenborg
- Flemish Institute for Technological Research (VITO), Mol, Belgium
- IBioStat, Hasselt University, Hasselt, Belgium
- CFP-CeProMa, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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14
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Systematic approaches to identify E3 ligase substrates. Biochem J 2017; 473:4083-4101. [PMID: 27834739 PMCID: PMC5103871 DOI: 10.1042/bcj20160719] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2016] [Revised: 08/25/2016] [Accepted: 08/30/2016] [Indexed: 12/11/2022]
Abstract
Protein ubiquitylation is a widespread post-translational modification, regulating cellular signalling with many outcomes, such as protein degradation, endocytosis, cell cycle progression, DNA repair and transcription. E3 ligases are a critical component of the ubiquitin proteasome system (UPS), determining the substrate specificity of the cascade by the covalent attachment of ubiquitin to substrate proteins. Currently, there are over 600 putative E3 ligases, but many are poorly characterized, particularly with respect to individual protein substrates. Here, we highlight systematic approaches to identify and validate UPS targets and discuss how they are underpinning rapid advances in our understanding of the biochemistry and biology of the UPS. The integration of novel tools, model systems and methods for target identification is driving significant interest in drug development, targeting various aspects of UPS function and advancing the understanding of a diverse range of disease processes.
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15
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Tahir M, Hayat M. Machine learning based identification of protein–protein interactions using derived features of physiochemical properties and evolutionary profiles. Artif Intell Med 2017; 78:61-71. [DOI: 10.1016/j.artmed.2017.06.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 06/09/2017] [Accepted: 06/11/2017] [Indexed: 02/09/2023]
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16
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Zhu L, Deng SP, You ZH, Huang DS. Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:345-352. [PMID: 28368812 DOI: 10.1109/tcbb.2015.2407393] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In recent years, a remarkable amount of protein-protein interaction (PPI) data are being available owing to the advance made in experimental high-throughput technologies. However, the experimentally detected PPI data usually contain a large amount of spurious links, which could contaminate the analysis of the biological significance of protein links and lead to incorrect biological discoveries, thereby posing new challenges to both computational and biological scientists. In this paper, we develop a new embedding algorithm called local similarity preserving embedding (LSPE) to rank the interaction possibility of protein links. By going beyond limitations of current geometric embedding methods for network denoising and emphasizing the local information of PPI networks, LSPE can avoid the unstableness of previous methods. We demonstrate experimental results on benchmark PPI networks and show that LSPE was the overall leader, outperforming the state-of-the-art methods in topological false links elimination problems.
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17
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Functional Characterization of EscK (Orf4), a Sorting Platform Component of the Enteropathogenic Escherichia coli Injectisome. J Bacteriol 2016; 199:JB.00538-16. [PMID: 27795324 DOI: 10.1128/jb.00538-16] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/03/2016] [Indexed: 02/07/2023] Open
Abstract
The type III secretion system (T3SS) is a supramolecular machine used by many bacterial pathogens to translocate effector proteins directly into the eukaryotic host cell cytoplasm. Enteropathogenic Escherichia coli (EPEC) is an important cause of infantile diarrheal disease in underdeveloped countries. EPEC virulence relies on a T3SS encoded within a chromosomal pathogenicity island known as the locus of enterocyte effacement (LEE). In this work, we pursued the functional characterization of the LEE-encoded protein EscK (previously known as Orf4). We provide evidence indicating that EscK is crucial for efficient T3S and belongs to the SctK (OrgA/YscK/MxiK) protein family, whose members have been implicated in the formation of a sorting platform for secretion of T3S substrates. Bacterial fractionation studies showed that EscK localizes to the inner membrane independently of the presence of any other T3SS component. Combining yeast two-hybrid screening and pulldown assays, we identified an interaction between EscK and the C-ring/sorting platform component EscQ. Site-directed mutagenesis of conserved residues revealed amino acids that are critical for EscK function and for its interaction with EscQ. In addition, we found that T3S substrate overproduction is capable of compensating for the absence of EscK. Overall, our data suggest that EscK is a structural component of the EPEC T3SS sorting platform, playing a central role in the recruitment of T3S substrates for boosting the efficiency of the protein translocation process. IMPORTANCE The type III secretion system (T3SS) is an essential virulence determinant for enteropathogenic Escherichia coli (EPEC) colonization of intestinal epithelial cells. Multiple EPEC effector proteins are injected via the T3SS into enterocyte cells, leading to diarrheal disease. The T3SS is encoded within a genomic pathogenicity island termed the locus of enterocyte effacement (LEE). Here we unravel the function of EscK, a previously uncharacterized LEE-encoded protein. We show that EscK is central for T3SS biogenesis and function. EscK forms a protein complex with EscQ, the main component of the cytoplasmic sorting platform, serving as a docking site for T3S substrates. Our results provide a comprehensive functional analysis of an understudied component of T3SSs.
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18
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Zhang W, Xu J, Li X, Zou X. A New Method for Identifying Essential Proteins by Measuring Co-Expression and Functional Similarity. IEEE Trans Nanobioscience 2016; 15:939-945. [DOI: 10.1109/tnb.2016.2625460] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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19
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Shatsky M, Dong M, Liu H, Yang LL, Choi M, Singer ME, Geller JT, Fisher SJ, Hall SC, Hazen TC, Brenner SE, Butland G, Jin J, Witkowska HE, Chandonia JM, Biggin MD. Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions. Mol Cell Proteomics 2016; 15:2186-202. [PMID: 27099342 PMCID: PMC5083090 DOI: 10.1074/mcp.m115.057117] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Indexed: 01/18/2023] Open
Abstract
Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.
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Affiliation(s)
- Maxim Shatsky
- From the ‡Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Ming Dong
- §Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Haichuan Liu
- ¶OB/GYN Department, University of California San Francisco-Sandler-Moore Mass Spectrometry Core Facility, University of California, San Francisco, California 94143
| | - Lee Lisheng Yang
- ‖Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Megan Choi
- §Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Mary E Singer
- **Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Jil T Geller
- **Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Susan J Fisher
- ¶OB/GYN Department, University of California San Francisco-Sandler-Moore Mass Spectrometry Core Facility, University of California, San Francisco, California 94143
| | - Steven C Hall
- ¶OB/GYN Department, University of California San Francisco-Sandler-Moore Mass Spectrometry Core Facility, University of California, San Francisco, California 94143
| | - Terry C Hazen
- ‡‡Department of Civil and Environmental Engineering, University of Tennessee, Knoxville, Tennessee 37996; §§Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831
| | - Steven E Brenner
- From the ‡Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720; ¶¶Department of Plant and Microbial Biology, University of California, Berkeley, California 94720
| | - Gareth Butland
- ‖‖Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Jian Jin
- ‖Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Ewa Witkowska
- ¶OB/GYN Department, University of California San Francisco-Sandler-Moore Mass Spectrometry Core Facility, University of California, San Francisco, California 94143
| | - John-Marc Chandonia
- From the ‡Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720;
| | - Mark D Biggin
- §Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720;
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20
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Fang Y, Sun M, Dai G, Ramain K. The Intrinsic Geometric Structure of Protein-Protein Interaction Networks for Protein Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:76-85. [PMID: 26886733 DOI: 10.1109/tcbb.2015.2456876] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Recent developments in high-throughput technologies for measuring protein-protein interaction (PPI) have profoundly advanced our ability to systematically infer protein function and regulation. However, inherently high false positive and false negative rates in measurement have posed great challenges in computational approaches for the prediction of PPI. A good PPI predictor should be 1) resistant to high rate of missing and spurious PPIs, and 2) robust against incompleteness of observed PPI networks. To predict PPI in a network, we developed an intrinsic geometry structure (IGS) for network, which exploits the intrinsic and hidden relationship among proteins in network through a heat diffusion process. In this process, all explicit PPIs participate simultaneously to glue local infinitesimal and noisy experimental interaction data to generate a global macroscopic descriptions about relationships among proteins. The revealed implicit relationship can be interpreted as the probability of two proteins interacting with each other. The revealed relationship is intrinsic and robust against individual, local and explicit protein interactions in the original network. We apply our approach to publicly available PPI network data for the evaluation of the performance of PPI prediction. Experimental results indicate that, under different levels of the missing and spurious PPIs, IGS is able to robustly exploit the intrinsic and hidden relationship for PPI prediction with a higher sensitivity and specificity compared to that of recently proposed methods.
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21
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Shabnam CF, Izudheen S. UDoGeC:Essential Protein Prediction Using Domain and Gene Expression Profiles. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.procs.2016.07.300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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22
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Snider J, Kotlyar M, Saraon P, Yao Z, Jurisica I, Stagljar I. Fundamentals of protein interaction network mapping. Mol Syst Biol 2015; 11:848. [PMID: 26681426 PMCID: PMC4704491 DOI: 10.15252/msb.20156351] [Citation(s) in RCA: 180] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.
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Affiliation(s)
- Jamie Snider
- Donnelly Centre, Department of Biochemistry, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Max Kotlyar
- Princess Margaret Cancer Center, IBM Life Sciences Discovery Centre, University Health Network, Ontario, Canada
| | - Punit Saraon
- Donnelly Centre, Department of Biochemistry, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Zhong Yao
- Donnelly Centre, Department of Biochemistry, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Center, IBM Life Sciences Discovery Centre, University Health Network, Ontario, Canada
| | - Igor Stagljar
- Donnelly Centre, Department of Biochemistry, Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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23
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Luo X, Ming Z, You Z, Li S, Xia Y, Leung H. Improving network topology-based protein interactome mapping via collaborative filtering. Knowl Based Syst 2015. [DOI: 10.1016/j.knosys.2015.10.003] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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24
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Prediction of Protein–Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures. J Membr Biol 2015; 249:141-53. [DOI: 10.1007/s00232-015-9856-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 11/03/2015] [Indexed: 12/12/2022]
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25
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Du N, Jiang K, Sawle AD, Frank MB, Wallace CA, Zhang A, Jarvis JN. Dynamic tracking of functional gene modules in treated juvenile idiopathic arthritis. Genome Med 2015; 7:109. [PMID: 26497493 PMCID: PMC4619406 DOI: 10.1186/s13073-015-0227-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 10/01/2015] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND We have previously shown that childhood-onset rheumatic diseases show aberrant patterns of gene expression that reflect pathology-associated co-expression networks. In this study, we used novel computational approaches to examine how disease-associated networks are altered in one of the most common rheumatic diseases of childhood, juvenile idiopathic arthritis (JIA). METHODS Using whole blood gene expression profiles derived from children in a pediatric rheumatology clinical trial, we used a network approach to understanding the impact of therapy and the underlying biology of response/non-response to therapy. RESULTS We demonstrate that therapy for JIA is associated with extensive re-ordering of gene expression networks, even in children who respond inadequately to therapy. Furthermore, we observe distinct differences in the evolution of specific network properties when we compare children who have been treated successfully with those who have inadequate treatment response. CONCLUSIONS Despite the inherent noisiness of whole blood gene expression data, our findings demonstrate how therapeutic response might be mapped and understood in pathologically informative cells in a broad range of human inflammatory diseases.
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Affiliation(s)
- Nan Du
- Department of Computer Sciences and Engineering, University at Buffalo, Buffalo, NY, USA.
| | - Kaiyu Jiang
- Department of Pediatrics, Rheumatology Research, University at Buffalo School of Medicine, Buffalo, NY, USA.
| | - Ashley D Sawle
- The Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, 10032, USA.
| | - Mark Barton Frank
- Oklahoma Medical Research Foundation, Clinical Immunology Program, Oklahoma City, OK, USA.
| | - Carol A Wallace
- Department of Pediatrics, University of Washington, Seattle, WA, USA.
| | - Aidong Zhang
- Department of Computer Sciences and Engineering, University at Buffalo, Buffalo, NY, USA.
| | - James N Jarvis
- Department of Pediatrics, Rheumatology Research, University at Buffalo School of Medicine, Buffalo, NY, USA.
- Genetics, Genomics, and Bioinformatics Program, University at Buffalo, Buffalo, NY, USA.
- Pediatric Rheumatology Research, University at Buffalo Clinical & Translational Research Center, 875 Ellicott St, Buffalo, NY, 14203, USA.
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26
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Zhu L, Deng SP, Huang DS. A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks. IEEE Trans Nanobioscience 2015; 14:528-34. [PMID: 25861086 DOI: 10.1109/tnb.2015.2420754] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Protein-protein interactions (PPIs) play essential roles for determining the outcomes of most of the cellular functions of the cell. Although the experimentally detected high-throughput PPI data promise new opportunities for the study of many biological mechanisms including cellular metabolism and protein functions, experimentally detected PPIs have high levels of false positive rate. Therefore, it is of high practical value to develop novel computational tools for pruning low-confidence PPIs. In this paper, we propose a new geometric approach called Leave-One-Out Logistic Metric Embedding (LOO-LME) for assessing the reliability of interactions. Unlike previous approaches which mainly seek to preserve the noisy topological information of the PPI networks in the embedding space, LOO-LME first transforms the learning task into an equivalent discriminant form, then directly deals with the uncertainty in PPI networks using a leave-one-out-style approach. The experimental results show that LOO-LME substantially outperforms previous methods on PPI assessment problems. LOO-LME could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge.
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27
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Next-generation technologies for multiomics approaches including interactome sequencing. BIOMED RESEARCH INTERNATIONAL 2015; 2015:104209. [PMID: 25649523 PMCID: PMC4306365 DOI: 10.1155/2015/104209] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 08/30/2014] [Accepted: 08/31/2014] [Indexed: 11/17/2022]
Abstract
The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research.
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28
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Luo X, You Z, Zhou M, Li S, Leung H, Xia Y, Zhu Q. A highly efficient approach to protein interactome mapping based on collaborative filtering framework. Sci Rep 2015; 5:7702. [PMID: 25572661 PMCID: PMC4287731 DOI: 10.1038/srep07702] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 12/08/2014] [Indexed: 12/17/2022] Open
Abstract
The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.
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Affiliation(s)
- Xin Luo
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Zhuhong You
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Mengchu Zhou
- M. Zhou is with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Shuai Li
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Hareton Leung
- X. Luo, Z. You, S. Li and H. Leung are with the Department of Computing, Hong Kong Polytechnic University, Hong Kong, HK 999077, China
| | - Yunni Xia
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
| | - Qingsheng Zhu
- X. Luo, Y. Xia and Q. Zhu are with the College of Computer Science, Chongqing University, Chongqing, 400044 China
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29
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Rolland T, Taşan M, Charloteaux B, Pevzner SJ, Zhong Q, Sahni N, Yi S, Lemmens I, Fontanillo C, Mosca R, Kamburov A, Ghiassian SD, Yang X, Ghamsari L, Balcha D, Begg BE, Braun P, Brehme M, Broly MP, Carvunis AR, Convery-Zupan D, Corominas R, Coulombe-Huntington J, Dann E, Dreze M, Dricot A, Fan C, Franzosa E, Gebreab F, Gutierrez BJ, Hardy MF, Jin M, Kang S, Kiros R, Lin GN, Luck K, MacWilliams A, Menche J, Murray RR, Palagi A, Poulin MM, Rambout X, Rasla J, Reichert P, Romero V, Ruyssinck E, Sahalie JM, Scholz A, Shah AA, Sharma A, Shen Y, Spirohn K, Tam S, Tejeda AO, Wanamaker SA, Twizere JC, Vega K, Walsh J, Cusick ME, Xia Y, Barabási AL, Iakoucheva LM, Aloy P, De Las Rivas J, Tavernier J, Calderwood MA, Hill DE, Hao T, Roth FP, Vidal M. A proteome-scale map of the human interactome network. Cell 2014; 159:1212-1226. [PMID: 25416956 PMCID: PMC4266588 DOI: 10.1016/j.cell.2014.10.050] [Citation(s) in RCA: 918] [Impact Index Per Article: 91.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 10/21/2014] [Accepted: 10/30/2014] [Indexed: 12/12/2022]
Abstract
Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.
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Affiliation(s)
- Thomas Rolland
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Murat Taşan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada
| | - Benoit Charloteaux
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Samuel J Pevzner
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA; Boston University School of Medicine, Boston, MA 02118, USA
| | - Quan Zhong
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Biological Sciences, Wright State University, Dayton, OH 45435, USA
| | - Nidhi Sahni
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Song Yi
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Irma Lemmens
- Department of Medical Protein Research, VIB, 9000 Ghent, Belgium
| | - Celia Fontanillo
- Cancer Research Center (Centro de Investigación del Cancer), University of Salamanca and Consejo Superior de Investigaciones Científicas, Salamanca 37008, Spain
| | - Roberto Mosca
- Joint IRB-BSC Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain
| | - Atanas Kamburov
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Susan D Ghiassian
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Xinping Yang
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Lila Ghamsari
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Dawit Balcha
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Bridget E Begg
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Pascal Braun
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Marc Brehme
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Martin P Broly
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Anne-Ruxandra Carvunis
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Dan Convery-Zupan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Roser Corominas
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jasmin Coulombe-Huntington
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Elizabeth Dann
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Matija Dreze
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Amélie Dricot
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Changyu Fan
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Eric Franzosa
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Fana Gebreab
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Bryan J Gutierrez
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Madeleine F Hardy
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Mike Jin
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Shuli Kang
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Ruth Kiros
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Guan Ning Lin
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Katja Luck
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Andrew MacWilliams
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jörg Menche
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Ryan R Murray
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alexandre Palagi
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Matthew M Poulin
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Xavier Rambout
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Protein Signaling and Interactions Lab, GIGA-R, University of Liege, 4000 Liege, Belgium
| | - John Rasla
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Patrick Reichert
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Viviana Romero
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Elien Ruyssinck
- Department of Medical Protein Research, VIB, 9000 Ghent, Belgium
| | - Julie M Sahalie
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Annemarie Scholz
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Akash A Shah
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Amitabh Sharma
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA 02115, USA
| | - Yun Shen
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Kerstin Spirohn
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Stanley Tam
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Alexander O Tejeda
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Shelly A Wanamaker
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jean-Claude Twizere
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Protein Signaling and Interactions Lab, GIGA-R, University of Liege, 4000 Liege, Belgium
| | - Kerwin Vega
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Jennifer Walsh
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Michael E Cusick
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Yu Xia
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Bioengineering, McGill University, Montreal, QC H3A 0C3, Canada
| | - Albert-László Barabási
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Complex Network Research (CCNR) and Department of Physics, Northeastern University, Boston, MA 02115, USA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Lilia M Iakoucheva
- Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA
| | - Patrick Aloy
- Joint IRB-BSC Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), Barcelona 08028, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona 08010, Spain
| | - Javier De Las Rivas
- Cancer Research Center (Centro de Investigación del Cancer), University of Salamanca and Consejo Superior de Investigaciones Científicas, Salamanca 37008, Spain
| | - Jan Tavernier
- Department of Medical Protein Research, VIB, 9000 Ghent, Belgium
| | - Michael A Calderwood
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - David E Hill
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Tong Hao
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Frederick P Roth
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON M5G 1X5, Canada; Canadian Institute for Advanced Research, Toronto M5G 1Z8, Canada.
| | - Marc Vidal
- Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
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AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between Salmonella and human proteins. PLoS One 2014; 9:e110488. [PMID: 25330226 PMCID: PMC4212833 DOI: 10.1371/journal.pone.0110488] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2014] [Accepted: 09/19/2014] [Indexed: 11/23/2022] Open
Abstract
Pathogen-host protein-protein interaction (PPI) plays an important role in revealing the underlying pathogenesis of viruses and bacteria. The need of rapidly mapping proteome-wide pathogen-host interactome opens avenues for and imposes burdens on computational modeling. For Salmonella typhimurium, only 62 interactions with human proteins are reported to date, and the computational modeling based on such a small training data is prone to yield model overfitting. In this work, we propose a multi-instance transfer learning method to reconstruct the proteome-wide Salmonella-human PPI networks, wherein the training data is augmented by homolog knowledge transfer in the form of independent homolog instances. We use AdaBoost instance reweighting to counteract the noise from homolog instances, and deliberately design three experimental settings to validate the assumption that the homolog instances are effective to address the problems of data scarcity and data unavailability. The experimental results show that the proposed method outperforms the existing models and some predictions are validated by the findings from recent literature. Lastly, we conduct gene ontology based clustering analysis of the predicted networks to provide insights into the pathogenesis of Salmonella.
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Tognetti S, Riera A, Speck C. Switch on the engine: how the eukaryotic replicative helicase MCM2-7 becomes activated. Chromosoma 2014; 124:13-26. [PMID: 25308420 DOI: 10.1007/s00412-014-0489-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2014] [Revised: 09/24/2014] [Accepted: 09/25/2014] [Indexed: 12/17/2022]
Abstract
A crucial step during eukaryotic initiation of DNA replication is the correct loading and activation of the replicative DNA helicase, which ensures that each replication origin fires only once. Unregulated DNA helicase loading and activation, as it occurs in cancer, can cause severe DNA damage and genomic instability. The essential mini-chromosome maintenance proteins 2-7 (MCM2-7) represent the core of the eukaryotic replicative helicase that is loaded at DNA replication origins during G1-phase of the cell cycle. The MCM2-7 helicase activity, however, is only triggered during S-phase once the holo-helicase Cdc45-MCM2-7-GINS (CMG) has been formed. A large number of factors and several kinases interact and contribute to CMG formation and helicase activation, though the exact mechanisms remain unclear. Crucially, upon DNA damage, this reaction is temporarily halted to ensure genome integrity. Here, we review the current understanding of helicase activation; we focus on protein interactions during CMG formation, discuss structural changes during helicase activation, and outline similarities and differences of the prokaryotic and eukaryotic helicase activation process.
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Affiliation(s)
- Silvia Tognetti
- DNA Replication Group, Institute of Clinical Science, Imperial College, London, W12 0NN, UK
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Mei S, Zhu H. Computational reconstruction of proteome-wide protein interaction networks between HTLV retroviruses and Homo sapiens. BMC Bioinformatics 2014; 15:245. [PMID: 25037487 PMCID: PMC4133621 DOI: 10.1186/1471-2105-15-245] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2014] [Accepted: 07/14/2014] [Indexed: 11/15/2022] Open
Abstract
Background Human T-cell leukemia viruses (HTLV) tend to induce some fatal human diseases like Adult T-cell Leukemia (ATL) by targeting human T lymphocytes. To indentify the protein-protein interactions (PPI) between HTLV viruses and Homo sapiens is one of the significant approaches to reveal the underlying mechanism of HTLV infection and host defence. At present, as biological experiments are labor-intensive and expensive, the identified part of the HTLV-human PPI networks is rather small. Although recent years have witnessed much progress in computational modeling for reconstructing pathogen-host PPI networks, data scarcity and data unavailability are two major challenges to be effectively addressed. To our knowledge, no computational method for proteome-wide HTLV-human PPI networks reconstruction has been reported. Results In this work we develop Multi-instance Adaboost method to conduct homolog knowledge transfer for computationally reconstructing proteome-wide HTLV-human PPI networks. In this method, the homolog knowledge in the form of gene ontology (GO) is treated as auxiliary homolog instance to address the problems of data scarcity and data unavailability, while the potential negative knowledge transfer is automatically attenuated by AdaBoost instance reweighting. The cross validation experiments show that the homolog knowledge transfer in the form of independent homolog instances can effectively enrich the feature information and substitute for the missing GO information. Moreover, the independent tests show that the method can validate 70.3% of the recently curated interactions, significantly exceeding the 2.1% recognition rate by the HT-Y2H experiment. We have used the method to reconstruct the proteome-wide HTLV-human PPI networks and further conducted gene ontology based clustering of the predicted networks for further biomedical research. The gene ontology based clustering analysis of the predictions provides much biological insight into the pathogenesis of HTLV retroviruses. Conclusions The Multi-instance AdaBoost method can effectively address the problems of data scarcity and data unavailability for the proteome-wide HTLV-human PPI interaction networks reconstruction. The gene ontology based clustering analysis of the predictions reveals some important signaling pathways and biological modules that HTLV retroviruses are likely to target. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-245) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Suyu Mei
- Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
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Mina M, Guzzi PH. Improving the Robustness of Local Network Alignment: Design and Extensive Assessment of a Markov Clustering-Based Approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:561-572. [PMID: 26356023 DOI: 10.1109/tcbb.2014.2318707] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The analysis of protein behavior at the network level had been applied to elucidate the mechanisms of protein interaction that are similar in different species. Published network alignment algorithms proved to be able to recapitulate known conserved modules and protein complexes, and infer new conserved interactions confirmed by wet lab experiments. In the meantime, however, a plethora of continuously evolving protein-protein interaction (PPI) data sets have been developed, each featuring different levels of completeness and reliability. For instance, algorithms performance may vary significantly when changing the data set used in their assessment. Moreover, existing papers did not deeply investigate the robustness of alignment algorithms. For instance, some algorithms performances vary significantly when changing the data set used in their assessment. In this work, we design an extensive assessment of current algorithms discussing the robustness of the results on the basis of input networks. We also present AlignMCL, a local network alignment algorithm based on an improved model of alignment graph and Markov Clustering. AlignMCL performs better than other state-of-the-art local alignment algorithms over different updated data sets. In addition, AlignMCL features high levels of robustness, producing similar results regardless the selected data set.
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Ohashi H, Miyamoto-Sato E. Towards personalized medicine mediated by in vitro virus-based interactome approaches. Int J Mol Sci 2014; 15:6717-24. [PMID: 24756093 PMCID: PMC4013657 DOI: 10.3390/ijms15046717] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Revised: 03/24/2014] [Accepted: 04/09/2014] [Indexed: 01/31/2023] Open
Abstract
We have developed a simple in vitro virus (IVV) selection system based on cell-free co-translation, using a highly stable and efficient mRNA display method. The IVV system is applicable to the high-throughput and comprehensive analysis of proteins and protein–ligand interactions. Huge amounts of genomic sequence data have been generated over the last decade. The accumulated genetic alterations and the interactome networks identified within cells represent a universal feature of a disease, and knowledge of these aspects can help to determine the optimal therapy for the disease. The concept of the “integrome” has been developed as a means of integrating large amounts of data. We have developed an interactome analysis method aimed at providing individually-targeted health care. We also consider future prospects for this system.
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Affiliation(s)
- Hiroyuki Ohashi
- Division of Interactome Medical Sciences, the Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
| | - Etsuko Miyamoto-Sato
- Division of Interactome Medical Sciences, the Institute of Medical Science, the University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
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Janjić V, Sharan R, Pržulj N. Modelling the yeast interactome. Sci Rep 2014; 4:4273. [PMID: 24589662 PMCID: PMC3940977 DOI: 10.1038/srep04273] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2013] [Accepted: 02/14/2014] [Indexed: 11/25/2022] Open
Abstract
The topology behind biological interaction networks has been studied for over a decade. Yet, there is no definite agreement on the theoretical models which best describe protein-protein interaction (PPI) networks. Such models are critical to quantifying the significance of any empirical observation regarding those networks. Here, we perform a comprehensive analysis of yeast PPI networks in order to gain insights into their topology and its dependency on interaction-screening technology. We find that: (1) interaction-detection technology has little effect on the topology of PPI networks; (2) topology of these interaction networks differs in organisms with different cellular complexity (human and yeast); (3) clear topological difference is present between PPI networks, their functional sub-modules, and their inter-functional “linkers”; (4) high confidence PPI networks have more “geometrical” topology compared to predicted, incomplete, or noisy PPI networks; and (5) inter-functional “linker” proteins serve as mediators in signal transduction, transport, regulation and organisational cellular processes.
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Affiliation(s)
- Vuk Janjić
- Department of Computing, Imperial College London, London, United Kingdom
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel
| | - Nataša Pržulj
- Department of Computing, Imperial College London, London, United Kingdom
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Hulovatyy Y, Solava RW, Milenković T. Revealing missing parts of the interactome via link prediction. PLoS One 2014; 9:e90073. [PMID: 24594900 PMCID: PMC3940777 DOI: 10.1371/journal.pone.0090073] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 01/29/2014] [Indexed: 12/20/2022] Open
Abstract
Protein interaction networks (PINs) are often used to "learn" new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses the existing PIN topology to predict missing and spurious links. Many of existing LP methods rely on shared immediate neighborhoods of the nodes to be linked. As such, they have limitations. Thus, in order to comprehensively study what are the topological properties of nodes in PINs that dictate whether the nodes should be linked, we introduce novel sensitive LP measures that are expected to overcome the limitations of the existing methods. We systematically evaluate the new and existing LP measures by introducing "synthetic" noise into PINs and measuring how accurate the measures are in reconstructing the original PINs. Also, we use the LP measures to de-noise the original PINs, and we measure biological correctness of the de-noised PINs with respect to functional enrichment of the predicted interactions. Our main findings are: 1) LP measures that favor nodes which are both "topologically similar" and have large shared extended neighborhoods are superior; 2) using more network topology often though not always improves LP accuracy; and 3) LP improves biological correctness of the PINs, plus we validate a significant portion of the predicted interactions in independent, external PIN data sources. Ultimately, we are less focused on identifying a superior method but more on showing that LP improves biological correctness of PINs, which is its ultimate goal in computational biology. But we note that our new methods outperform each of the existing ones with respect to at least one evaluation criterion. Alarmingly, we find that the different criteria often disagree in identifying the best method(s), which has important implications for LP communities in any domain, including social networks.
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Affiliation(s)
- Yuriy Hulovatyy
- Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Ryan W. Solava
- Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Tijana Milenković
- Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
- * E-mail:
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Protein-protein interaction detection: methods and analysis. INTERNATIONAL JOURNAL OF PROTEOMICS 2014; 2014:147648. [PMID: 24693427 PMCID: PMC3947875 DOI: 10.1155/2014/147648] [Citation(s) in RCA: 375] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/26/2013] [Revised: 12/05/2013] [Accepted: 12/20/2013] [Indexed: 12/24/2022]
Abstract
Protein-protein interaction plays key role in predicting the protein function of target protein and drug ability of molecules. The majority of genes and proteins realize resulting phenotype functions as a set of interactions. The in vitro and in vivo methods like affinity purification, Y2H (yeast 2 hybrid), TAP (tandem affinity purification), and so forth have their own limitations like cost, time, and so forth, and the resultant data sets are noisy and have more false positives to annotate the function of drug molecules. Thus, in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, gene fusion, in silico 2 hybrid, phylogenetic tree, phylogenetic profile, and gene expression-based approaches were developed. Elucidation of protein interaction networks also contributes greatly to the analysis of signal transduction pathways. Recent developments have also led to the construction of networks having all the protein-protein interactions using computational methods for signaling pathways and protein complex identification in specific diseases.
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Rid R, Strasser W, Siegl D, Frech C, Kommenda M, Kern T, Hintner H, Bauer JW, Önder K. PRIMOS: an integrated database of reassessed protein-protein interactions providing web-based access to in silico validation of experimentally derived data. Assay Drug Dev Technol 2014; 11:333-46. [PMID: 23772554 DOI: 10.1089/adt.2013.506] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Steady improvements in proteomics present a bioinformatic challenge to retrieve, store, and process the accumulating and often redundant amount of information. In particular, a large-scale comparison and analysis of protein-protein interaction (PPI) data requires tools for data interpretation as well as validation. At this juncture, the Protein Interaction and Molecule Search (PRIMOS) platform represents a novel web portal that unifies six primary PPI databases (BIND, Biomolecular Interaction Network Database; DIP, Database of Interacting Proteins; HPRD, Human Protein Reference Database; IntAct; MINT, Molecular Interaction Database; and MIPS, Munich Information Center for Protein Sequences) into a single consistent repository, which currently includes more than 196,700 redundancy-removed PPIs. PRIMOS supports three advanced search strategies centering on disease-relevant PPIs, on inter- and intra-organismal crosstalk relations (e.g., pathogen-host interactions), and on highly connected protein nodes analysis ("hub" identification). The main novelties distinguishing PRIMOS from other secondary PPI databases are the reassessment of known PPIs, and the capacity to validate personal experimental data by our peer-reviewed, homology-based validation. This article focuses on definite PRIMOS use cases (presentation of embedded biological concepts, example applications) to demonstrate its broad functionality and practical value. PRIMOS is publicly available at http://primos.fh-hagenberg.at.
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Affiliation(s)
- Raphaela Rid
- Division of Molecular Dermatology, Department of Dermatology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Abstract
Rapid development of genomic and proteomic methodologies has provided a wealth of data for deciphering the biomolecular circuitry of a living cell. The main areas of computational research of proteomes outlined in this review are: understanding the system, its features and parameters to help plan the experiments; data integration, to help produce more reliable data sets; visualization and other forms of data representation to simplify interpretation; modeling of the functional regulation; and systems biology. With false-positive rates reaching 50% even in the more reliable data sets, handling the experimental error remains one of the most challenging tasks. Integrative approaches, incorporating results of various genome- and proteome-wide experiments, allow for minimizing the error and bring with them significant predictive power.
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Zahiri J, Bozorgmehr JH, Masoudi-Nejad A. Computational Prediction of Protein-Protein Interaction Networks: Algo-rithms and Resources. Curr Genomics 2014; 14:397-414. [PMID: 24396273 PMCID: PMC3861891 DOI: 10.2174/1389202911314060004] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 08/07/2013] [Accepted: 08/26/2013] [Indexed: 01/15/2023] Open
Abstract
Protein interactions play an important role in the discovery of protein functions and pathways in biological processes. This is especially true in case of the diseases caused by the loss of specific protein-protein interactions in the organism. The accuracy of experimental results in finding protein-protein interactions, however, is rather dubious and high throughput experimental results have shown both high false positive beside false negative information for protein interaction. Computational methods have attracted tremendous attention among biologists because of the ability to predict protein-protein interactions and validate the obtained experimental results. In this study, we have reviewed several computational methods for protein-protein interaction prediction as well as describing major databases, which store both predicted and detected protein-protein interactions, and the tools used for analyzing protein interaction networks and improving protein-protein interaction reliability.
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Affiliation(s)
- Javad Zahiri
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Joseph Hannon Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
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Ahmed MH, Habtemariam M, Safo MK, Scarsdale JN, Spyrakis F, Cozzini P, Mozzarelli A, Kellogg GE. Unintended consequences? Water molecules at biological and crystallographic protein–protein interfaces. Comput Biol Chem 2013; 47:126-41. [DOI: 10.1016/j.compbiolchem.2013.08.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2013] [Revised: 08/27/2013] [Accepted: 08/27/2013] [Indexed: 01/31/2023]
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Zhu L, You ZH, Huang DS. Increasing the reliability of protein–protein interaction networks via non-convex semantic embedding. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.04.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins. PLoS One 2013; 8:e79606. [PMID: 24260261 PMCID: PMC3832534 DOI: 10.1371/journal.pone.0079606] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 09/24/2013] [Indexed: 11/20/2022] Open
Abstract
Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.
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Rid R, Herzog J, Maier RH, Hundsberger H, Eger A, Hintner H, Bauer JW, Onder K. Real-time monitoring of relative peptide-protein interaction strengths in the yeast two-hybrid system. Assay Drug Dev Technol 2013; 11:269-75. [PMID: 23679850 DOI: 10.1089/adt.2012.496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The yeast two-hybrid (Y2H) system is one of the most technically straightforward, effective, and widely used tools for the discovery of the binary peptide or protein interactions. However, its exceptional detection sensitivity poses a serious challenge for affinity ranking and hence prioritizing the resultant large number of putative interactors for follow-up analyses. To overcome this apparent bottleneck, we describe here a novel yeast growth curve-based interaction-monitoring approach that permits semiautomatic quantification, comparison, and statistically ascertained scoring of a large collection of Y2H interactions under real-time conditions. Initially, we conducted a proof-of-concept test of five literature-validated peptide-protein interactions with known affinities in the low μM range, and subsequently used the method to classify 88 novel vitamin D receptor-binding peptides derived from high-throughput screening of a highly diverse artificial peptide aptamer library. Based on our in-depth data evaluation, we conclude that real-time monitoring of clone growth as a measure of relative binding strength offers a facile, cost-effective, accurate, reproducible, and further adaptable complement to standard Y2H-derived clone management.
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Affiliation(s)
- Raphaela Rid
- Division of Molecular Dermatology, Department of Dermatology, Paracelsus Private Medical University Salzburg, Salzburg, Austria
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Karagoz K, Arga KY. Assessment of high-confidence protein-protein interactome in yeast. Comput Biol Chem 2013; 45:1-8. [PMID: 23608186 DOI: 10.1016/j.compbiolchem.2013.03.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2013] [Revised: 03/15/2013] [Accepted: 03/15/2013] [Indexed: 12/28/2022]
Abstract
The identification of protein-protein interactions (PPIs) and their networks is vitally important to systemically define and understand the roles of proteins in biological systems. In spite of development of numerous experimental systems to detect PPIs and diverse research on assessment of the quality of the obtained data, a consensus--highly reliable, almost complete--interactome of Saccharomyces cerevisiae is not presented yet. In this work, we proposed an unsupervised statistical approach to create a high-confidence yeast PPI network. For this, we assembled databases of interacting protein pairs for yeast and obtained an extremely large PPI dataset which comprises of 135,154 non-redundant interactions between 6191 yeast proteins. A scoring scheme considering eight heterogeneous biological features resulted with a broad score distribution and a highly reliable network consisting of 29,046 physical interactions with scores higher than the threshold value of 0.85, for which sensitivity, specificity and coverage were 86%, 68%, and 72%, respectively. We evaluated our method by comparing it with other scoring schemes and showed that reducing the noise inherent in experimental PPIs via our scoring scheme further increased the accuracy. Current study is expected to increase the efficiency of the methodologies in biological research which make use of protein interaction networks.
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Affiliation(s)
- Kubra Karagoz
- Department of Bioengineering, Marmara University, Goztepe 34722, Istanbul, Turkey
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46
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Lei YK, You ZH, Dong T, Jiang YX, Yang JA. Increasing reliability of protein interactome by fast manifold embedding. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2012.09.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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47
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Zhu Y, Zhang XF, Dai DQ, Wu MY. Identifying spurious interactions and predicting missing interactions in the protein-protein interaction networks via a generative network model. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:219-225. [PMID: 23702559 DOI: 10.1109/tcbb.2012.164] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
With the rapid development of high-throughput experiment techniques for protein-protein interaction (PPI) detection, a large amount of PPI network data are becoming available. However, the data produced by these techniques have high levels of spurious and missing interactions. This study assigns a new reliably indication for each protein pairs via the new generative network model (RIGNM) where the scale-free property of the PPI network is considered to reliably identify both spurious and missing interactions in the observed high-throughput PPI network. The experimental results show that the RIGNM is more effective and interpretable than the compared methods, which demonstrate that this approach has the potential to better describe the PPI networks and drive new discoveries.
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Affiliation(s)
- Yuan Zhu
- Department of Mathematics, Guangdong University of Business Studies, Guangzhou, China.
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Yong CH, Liu G, Chua HN, Wong L. Supervised maximum-likelihood weighting of composite protein networks for complex prediction. BMC SYSTEMS BIOLOGY 2012; 6 Suppl 2:S13. [PMID: 23281936 PMCID: PMC3521185 DOI: 10.1186/1752-0509-6-s2-s13] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI) data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. RESULTS We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. CONCLUSIONS Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to discover novel complexes. We show improved performance over previous approaches in terms of precision, recall, and number and quality of novel predictions. We present and visualize two novel predicted complexes in yeast and human, and find external evidence supporting these predictions.
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Affiliation(s)
- Chern Han Yong
- Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - Guimei Liu
- School of Computing, National University of Singapore, Singapore
| | | | - Limsoon Wong
- School of Computing, National University of Singapore, Singapore
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Quantitative real-time PCR as a sensitive protein–protein interaction quantification method and a partial solution for non-accessible autoactivator and false-negative molecule analysis in the yeast two-hybrid system. Methods 2012; 58:376-84. [DOI: 10.1016/j.ymeth.2012.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2011] [Revised: 09/03/2012] [Accepted: 09/06/2012] [Indexed: 12/15/2022] Open
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50
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Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system. Microbiol Mol Biol Rev 2012; 76:331-82. [PMID: 22688816 DOI: 10.1128/mmbr.05021-11] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.
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