1
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Csikász-Nagy A, Fichó E, Noto S, Reguly I. Computational tools to predict context-specific protein complexes. Curr Opin Struct Biol 2024; 88:102883. [PMID: 38986166 DOI: 10.1016/j.sbi.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/21/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
Abstract
Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.
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Affiliation(s)
- Attila Csikász-Nagy
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | | | - Santiago Noto
- Cytocast Hungary Kft, Budapest, Hungary; Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - István Reguly
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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2
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Chen Z, Ge R, Wang C, Elazab A, Fu X, Min W, Qin F, Jia G, Fan X. Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm. Mamm Genome 2024; 35:241-255. [PMID: 38512459 DOI: 10.1007/s00335-024-10034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.
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Affiliation(s)
| | - Ruiquan Ge
- Hangzhou Dianzi University, Hangzhou, China.
- Hangzhou Institute of Advanced Technology, Hangzhou, China.
- Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou, China.
| | - Changmiao Wang
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Ahmed Elazab
- Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt
| | - Xianjun Fu
- School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, China
| | - Wenwen Min
- School of Information Science and Engineering, Yunnan University, Kunming, China
| | - Feiwei Qin
- Hangzhou Dianzi University, Hangzhou, China
| | | | - Xiaopeng Fan
- Hangzhou Institute of Advanced Technology, Hangzhou, China
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3
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Zhao N, Wu T, Wang W, Zhang L, Gong X. Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure. Interdiscip Sci 2024; 16:261-288. [PMID: 38955920 DOI: 10.1007/s12539-024-00626-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/04/2024]
Abstract
Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.
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Affiliation(s)
- Nan Zhao
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Tong Wu
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Wenda Wang
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China
- School of Mathematics, Renmin University of China, Beijing, 100872, China
| | - Lunchuan Zhang
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
| | - Xinqi Gong
- Institute for Mathematical Sciences, Renmin University of China, Beijing, 100872, China.
- School of Mathematics, Renmin University of China, Beijing, 100872, China.
- Beijing Academy of Artificial Intelligence, Beijing, 100084, China.
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4
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Xia S, Li D, Deng X, Liu Z, Zhu H, Liu Y, Li D. Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes. Brief Bioinform 2024; 25:bbae274. [PMID: 38851299 PMCID: PMC11162299 DOI: 10.1093/bib/bbae274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/10/2024] Open
Abstract
Protein-protein interactions (PPIs) are the basis of many important biological processes, with protein complexes being the key forms implementing these interactions. Understanding protein complexes and their functions is critical for elucidating mechanisms of life processes, disease diagnosis and treatment and drug development. However, experimental methods for identifying protein complexes have many limitations. Therefore, it is necessary to use computational methods to predict protein complexes. Protein sequences can indicate the structure and biological functions of proteins, while also determining their binding abilities with other proteins, influencing the formation of protein complexes. Integrating these characteristics to predict protein complexes is very promising, but currently there is no effective framework that can utilize both protein sequence and PPI network topology for complex prediction. To address this challenge, we have developed HyperGraphComplex, a method based on hypergraph variational autoencoder that can capture expressive features from protein sequences without feature engineering, while also considering topological properties in PPI networks, to predict protein complexes. Experiment results demonstrated that HyperGraphComplex achieves satisfactory predictive performance when compared with state-of-art methods. Further bioinformatics analysis shows that the predicted protein complexes have similar attributes to known ones. Moreover, case studies corroborated the remarkable predictive capability of our model in identifying protein complexes, including 3 that were not only experimentally validated by recent studies but also exhibited high-confidence structural predictions from AlphaFold-Multimer. We believe that the HyperGraphComplex algorithm and our provided proteome-wide high-confidence protein complex prediction dataset will help elucidate how proteins regulate cellular processes in the form of complexes, and facilitate disease diagnosis and treatment and drug development. Source codes are available at https://github.com/LiDlab/HyperGraphComplex.
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Affiliation(s)
- Simin Xia
- School of Basic Medical Sciences, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei 230032, China
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Dianke Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
- State Key Laboratory of Farm Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, 2 Yuanmingyuan West Road, Haidian District, Beijing 100193, China
| | - Xinru Deng
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Zhongyang Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Huaqing Zhu
- School of Basic Medical Sciences, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei 230032, China
| | - Yuan Liu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
| | - Dong Li
- School of Basic Medical Sciences, Anhui Medical University, 81 Meishan Road, Shushan District, Hefei 230032, China
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, 38 Life Science Park, Changping District, Beijing 102206, China
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5
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Wright SN, Colton S, Schaffer LV, Pillich RT, Churas C, Pratt D, Ideker T. State of the Interactomes: an evaluation of molecular networks for generating biological insights. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.587073. [PMID: 38746239 PMCID: PMC11092493 DOI: 10.1101/2024.04.26.587073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Advancements in genomic and proteomic technologies have powered the use of gene and protein networks ("interactomes") for understanding genotype-phenotype translation. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 46 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP and SIGNOR demonstrate strong interaction prediction performance. These findings provide a benchmark for interactomes across diverse network biology applications and clarify factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.
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Fang T, Szklarczyk D, Hachilif R, von Mering C. Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration. Sci Rep 2024; 14:6009. [PMID: 38472223 PMCID: PMC10933411 DOI: 10.1038/s41598-024-55655-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Protein-protein interactions (PPIs) play essential roles in most biological processes. The binding interfaces between interacting proteins impose evolutionary constraints that have successfully been employed to predict PPIs from multiple sequence alignments (MSAs). To construct MSAs, critical choices have to be made: how to ensure the reliable identification of orthologs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed under distinct clades in the tree of life. Coevolutionary signals are searched separately within these clades, and are only subsequently integrated using machine learning techniques. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Given the recent successes of AlphaFold in predicting direct PPIs at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates-thus reducing false positives as well as computation time.
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Affiliation(s)
- Tao Fang
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Damian Szklarczyk
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Radja Hachilif
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Christian von Mering
- Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
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7
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Sahoo TR, Patra S, Vipsita S. Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks. Comput Biol Chem 2023; 106:107935. [PMID: 37536230 DOI: 10.1016/j.compbiolchem.2023.107935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 06/11/2023] [Accepted: 07/23/2023] [Indexed: 08/05/2023]
Abstract
The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.
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Affiliation(s)
- Tushar Ranjan Sahoo
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
| | - Sabyasachi Patra
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
| | - Swati Vipsita
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
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8
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Shuvo MH, Karim M, Roche R, Bhattacharya D. PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIOINFORMATICS ADVANCES 2023; 3:vbad070. [PMID: 37351310 PMCID: PMC10281963 DOI: 10.1093/bioadv/vbad070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/17/2023] [Accepted: 06/01/2023] [Indexed: 06/24/2023]
Abstract
Motivation Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Results Here, we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementation An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Mohimenul Karim
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA
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9
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Shuvo MH, Karim M, Roche R, Bhattacharya D. PIQLE: protein-protein interface quality estimation by deep graph learning of multimeric interaction geometries. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528528. [PMID: 36824789 PMCID: PMC9949034 DOI: 10.1101/2023.02.14.528528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Accurate modeling of protein-protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Here we present PIQLE, a deep graph learning method for protein-protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of the individual interactions between the interfacial residues using a multihead graph attention network and then probabilistically combines the estimated quality of the interfacial residues for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study reveals that the performance gains are connected to the effectiveness of the multihead graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. An open-source software implementation of PIQLE, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/PIQLE .
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Affiliation(s)
- Md Hossain Shuvo
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Mohimenul Karim
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
| | - Debswapna Bhattacharya
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States of America
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10
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Payra AK, Saha B, Ghosh A. MM-CCNB: Essential protein prediction using MAX-MIN strategies and compartment of common neighboring approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 228:107247. [PMID: 36427433 DOI: 10.1016/j.cmpb.2022.107247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 10/16/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Proteins are indispensable for the flow of the life of living organisms. Protein pairs in interaction exhibit more functional activities than individuals. These activities have been considered an essential measure in predicting their essentiality. Neighborhood approaches have been used frequently in the prediction of essentiality scores. All paired neighbors of the essential proteins are nominated for the suitable candidate seeds for prediction. Still now Jaccard's coefficient is limited to predicting functions, homologous groups, sequence analysis, etc. It really motivate us to predict essential proteins efficiently using different computational approaches. METHODS In our work, we proposed modified Jaccard's coefficient to predict essential proteins. We have proposed a novel methodology for predicting essential proteins using MAX-MIN strategies and modified Jaccard's coefficient approach. RESULTS The performance of our proposed methodology has been analyzed for Saccharomyces cerevisiae datasets with an accuracy of more than 80%. It has been observed that the proposed algorithm is outperforms with an accuracy of 0.78, 0.74, 0.79, and 0.862 for YDIP, YMIPS, YHQ, and YMBD datasets respectivly. CONCLUSIONS There are several computational approaches in the existing state-of-art model of essential protein prediction. It has been noted that our predicted methodology outperforms other existing models viz. different centralities, local interaction density combined with protein complexes, modified monkey algorithm and ortho_sim_loc methods.
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Affiliation(s)
- Anjan Kumar Payra
- Department of Computer Science & Engineering, Dr. Sudhir Chandra Sur Degree Engineering College, 540, Dum Dum Road, Near Dum Dum Jn. Station, Surermath, Kolkata 700074, India.
| | - Banani Saha
- Department of Computer Science & Engineering, University of Calcutta, Saltlake City Kolkata 700073, India
| | - Anupam Ghosh
- Department of Computer Science & Engineering, Netaji Subhash Engineering College, Techno City, Panchpota, Garia, Kolkata 700152, India.
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11
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Manipur I, Giordano M, Piccirillo M, Parashuraman S, Maddalena L. Community Detection in Protein-Protein Interaction Networks and Applications. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:217-237. [PMID: 34951849 DOI: 10.1109/tcbb.2021.3138142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.
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12
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Mezentsev Y, Ershov P, Yablokov E, Kaluzhskiy L, Kupriyanov K, Gnedenko O, Ivanov A. Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate. Int J Mol Sci 2022; 23:15697. [PMID: 36555337 PMCID: PMC9779103 DOI: 10.3390/ijms232415697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Most proteins function as part of various complexes, forming via stable and dynamic protein-protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters (affinity, molecular-recognition specificity, kinetic rate constants, and thermodynamic-parameters determination); (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors' experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed.
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Affiliation(s)
| | | | | | | | | | | | - Alexis Ivanov
- Institute of Biomedical Chemistry (IBMC), 119121 Moscow, Russia
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13
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Sahoo TR, Vipsita S, Patra S. Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques. Interdiscip Sci 2022:10.1007/s12539-022-00541-z. [PMID: 36306022 DOI: 10.1007/s12539-022-00541-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.
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Affiliation(s)
| | - Swati Vipsita
- CSE, IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, 751003, India
| | - Sabyasachi Patra
- CSE, IIIT Bhubaneswar, Gothapatna, Bhubaneswar, Odisha, 751003, India
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14
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Wang R, Wang C, Ma H. Detecting protein complexes with multiple properties by an adaptive harmony search algorithm. BMC Bioinformatics 2022; 23:414. [PMID: 36207692 PMCID: PMC9541083 DOI: 10.1186/s12859-022-04923-4] [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: 04/22/2022] [Accepted: 09/12/2022] [Indexed: 11/27/2022] Open
Abstract
Background Accurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks. Result To address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically. Conclusions Experimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04923-4.
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Affiliation(s)
- Rongquan Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, 100083, China
| | - Caixia Wang
- School of International Economics, China Foreign Affairs University, 24 Zhanlanguan Road, Xicheng District, Beijing, 100037, China
| | - Huimin Ma
- School of Computer and Communication Engineering, University of Science and Technology Beijing, No. 30 Xueyuan Road, Haidian District, Beijing, 100083, China.
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15
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Lyu J, Yao Z, Liang B, Liu Y, Zhang Y. Small protein complex prediction algorithm based on protein-protein interaction network segmentation. BMC Bioinformatics 2022; 23:405. [PMID: 36180820 PMCID: PMC9524060 DOI: 10.1186/s12859-022-04960-z] [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: 05/05/2022] [Accepted: 09/19/2022] [Indexed: 11/23/2022] Open
Abstract
Background Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes. Results In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar. Conclusions In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research.
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Affiliation(s)
- Jiaqing Lyu
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
| | - Zhen Yao
- School of Chemical Engineering, Dalian University of Technology, Dalian, China
| | - Bing Liang
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China.
| | - Yiwei Liu
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, China
| | - Yijia Zhang
- School of Information Science and Technology, Dalian Maritime University, Dalian, China.
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16
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [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: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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17
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Gao C, Glass KC, Frietze S. Functional networks of the human bromodomain-containing proteins. FRONTIERS IN BIOINFORMATICS 2022; 2:835892. [PMID: 36304339 PMCID: PMC9580951 DOI: 10.3389/fbinf.2022.835892] [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: 12/15/2021] [Accepted: 06/29/2022] [Indexed: 01/22/2023] Open
Abstract
Background: Bromodomains are a structurally conserved epigenetic reader domain that bind to acetylated lysine residues in both histone and non-histone proteins. Bromodomain-containing proteins (BRD proteins) often function as scaffolding proteins in the assembly of multi-protein complexes to regulate diverse biological processes. BRD proteins have been classified based on biological and functional similarity, however the functions of many BRD proteins remains unknown. PPI network analysis is useful for revealing organizational roles, identifying functional clusters, and predicting function for BRD proteins. Results: We used available data to construct protein-protein interaction networks (PPINs) to study the properties of the human bromodomain protein family. The network properties of the BRD PPIN establishes that the BRD proteins serve as hub proteins that are enriched near the global center to form an inter-connected PPIN. We identified dense subgraphs formed by BRD proteins and find that different BRD proteins share topological similarity and functional associations. We explored the functional relationships through clustering and Hallmark pathway gene set enrichment analysis and identify potential biological roles for different BRD proteins. Conclusion: In our network analysis we confirmed that BRD proteins are conserved central nodes in the human PPI network and function as scaffolds to form distinctive functional clusters. Overall, this study provides detailed insight into the predictive functions of BRD proteins in the context of functional complexes and biological pathways.
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Affiliation(s)
- Cong Gao
- Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT, United States
| | - Karen C. Glass
- Department of Pharmacology, Larner College of Medicine, University of Vermont, Burlington, VT, United States,University of Vermont Cancer Center, Burlington, VT, United States
| | - Seth Frietze
- Department of Biomedical and Health Sciences, University of Vermont, Burlington, VT, United States,University of Vermont Cancer Center, Burlington, VT, United States,*Correspondence: Seth Frietze,
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18
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Omranian S, Nikoloski Z, Grimm DG. Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward. Comput Struct Biotechnol J 2022; 20:2699-2712. [PMID: 35685359 PMCID: PMC9166428 DOI: 10.1016/j.csbj.2022.05.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 01/05/2023] Open
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19
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Berahmand K, Nasiri E, Pir Mohammadiani R, Li Y. Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding. Comput Biol Med 2021; 138:104933. [PMID: 34655897 DOI: 10.1016/j.compbiomed.2021.104933] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/20/2021] [Accepted: 10/07/2021] [Indexed: 02/02/2023]
Abstract
The identification of protein complexes in protein-protein interaction networks is the most fundamental and essential problem for revealing the underlying mechanism of biological processes. However, most existing protein complexes identification methods only consider a network's topology structures, and in doing so, these methods miss the advantage of using nodes' feature information. In protein-protein interaction, both topological structure and node features are essential ingredients for protein complexes. The spectral clustering method utilizes the eigenvalues of the affinity matrix of the data to map to a low-dimensional space. It has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of dimensionality reduction. In this paper, a new version of spectral clustering, named text-associated DeepWalk-Spectral Clustering (TADW-SC), is proposed for attributed networks in which the identified protein complexes have structural cohesiveness and attribute homogeneity. Since the performance of spectral clustering heavily depends on the effectiveness of the affinity matrix, our proposed method will use the text-associated DeepWalk (TADW) to calculate the embedding vectors of proteins. In the following, the affinity matrix will be computed by utilizing the cosine similarity between the two low dimensional vectors, which will be considerable to improve the accuracy of the affinity matrix. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in both real protein network datasets and synthetic networks.
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Affiliation(s)
- Kamal Berahmand
- School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.
| | - Elahe Nasiri
- Department of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, Iran.
| | | | - Yuefeng Li
- School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.
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20
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Liu G, Liu B, Li A, Wang X, Yu J, Zhou X. Identifying Protein Complexes With Clear Module Structure Using Pairwise Constraints in Protein Interaction Networks. Front Genet 2021; 12:664786. [PMID: 34512712 PMCID: PMC8430217 DOI: 10.3389/fgene.2021.664786] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 06/23/2021] [Indexed: 02/02/2023] Open
Abstract
The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks.
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Affiliation(s)
- Guangming Liu
- School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China
| | - Bo Liu
- Hebei Key Laboratory of Agricultural Big Data, College of Information Science and Technology, Hebei Agricultural University, Baoding, China
| | - Aimin Li
- School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China
| | - Xiaofan Wang
- School of Computer Science & Engineering, Xi'an University of Technology, Xi'an, China
| | - Jian Yu
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xuezhong Zhou
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
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21
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Hong Z, Liu J, Chen Y. An interpretable machine learning method for homo-trimeric protein interface residue-residue interaction prediction. Biophys Chem 2021; 278:106666. [PMID: 34418678 DOI: 10.1016/j.bpc.2021.106666] [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: 04/25/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 12/29/2022]
Abstract
Protein-protein interaction plays an important role in life activities. A more fine-grained analysis, such as residues and atoms level, will better benefit us to understand the mechanism for inter-protein interaction and drug design. The development of efficient computational methods to reduce trials and errors, as well as assisting experimental researchers to determine the complex structure are some of the ongoing studies in the field. The research of trimer protein interface, especially homotrimer, has been rarely studied. In this paper, we proposed an interpretable machine learning method for homo-trimeric protein interface residue pairs prediction. The structure, sequence, and physicochemical information are intergraded as feature input fed to model for training. Graph model is utilized to present spatial information for intra-protein. Matrix factorization captures the different features' interactions. Kernel function is designed to auto-acquire the adjacent information of our target residue pairs. The accuracy rate achieves 54.5% in an independent test set. Sequence and structure alignment exhibit the ability of model self-study. Our model indicates the biological significance between sequence and structure, and could be auxiliary for reducing trials and errors in the fields of protein complex determination and protein-protein docking, etc. SIGNIFICANCE: Protein complex structures are significant for understanding protein function and promising functional protein design. With data increasing, some computational tools have been developed for protein complex residue contact prediction, which is one of the most significant steps for complex structure prediction. But for homo-trimeric protein, the sequence-based deep learning predictors are infeasible for homologous sequences, and the algorithm black box prevents us from understanding of each step operation. In this way, we propose an interpreting machine learning method for homo-trimeric protein interface residue-residue interaction prediction, and the predictor shows a good performance. Our work provides a computational auxiliary way for determining the homo-trimeric proteins interface residue pairs which will be further verified by wet experiments, and and gives a hand for the downstream works, such as protein-protein docking, protein complex structure prediction and drug design.
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Affiliation(s)
- Zhonghua Hong
- Jiaxing Hospital of Traditional Chinese Medicine, Jiaxing University, Jiaxing 314001, PR China.
| | - Jiale Liu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, PR China
| | - Yinggao Chen
- Shantou Central Hospital, Shantou 515041, PR China.
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22
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Zimmermann J, Kaleta C, Waschina S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol 2021; 22:81. [PMID: 33691770 PMCID: PMC7949252 DOI: 10.1186/s13059-021-02295-1] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 02/10/2021] [Indexed: 12/21/2022] Open
Abstract
Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.
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Affiliation(s)
- Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Christoph Kaleta
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
| | - Silvio Waschina
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Michaelis-Str. 5, Kiel, 24105 Germany
- Christian-Albrechts-University Kiel, Institute of Human Nutrition and Food Science, Nutriinformatics, Heinrich-Hecht-Platz 10, Kiel, 24118 Germany
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23
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Swamy KBS, Schuyler SC, Leu JY. Protein Complexes Form a Basis for Complex Hybrid Incompatibility. Front Genet 2021; 12:609766. [PMID: 33633780 PMCID: PMC7900514 DOI: 10.3389/fgene.2021.609766] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/20/2021] [Indexed: 12/20/2022] Open
Abstract
Proteins are the workhorses of the cell and execute many of their functions by interacting with other proteins forming protein complexes. Multi-protein complexes are an admixture of subunits, change their interaction partners, and modulate their functions and cellular physiology in response to environmental changes. When two species mate, the hybrid offspring are usually inviable or sterile because of large-scale differences in the genetic makeup between the two parents causing incompatible genetic interactions. Such reciprocal-sign epistasis between inter-specific alleles is not limited to incompatible interactions between just one gene pair; and, usually involves multiple genes. Many of these multi-locus incompatibilities show visible defects, only in the presence of all the interactions, making it hard to characterize. Understanding the dynamics of protein-protein interactions (PPIs) leading to multi-protein complexes is better suited to characterize multi-locus incompatibilities, compared to studying them with traditional approaches of genetics and molecular biology. The advances in omics technologies, which includes genomics, transcriptomics, and proteomics can help achieve this end. This is especially relevant when studying non-model organisms. Here, we discuss the recent progress in the understanding of hybrid genetic incompatibility; omics technologies, and how together they have helped in characterizing protein complexes and in turn multi-locus incompatibilities. We also review advances in bioinformatic techniques suitable for this purpose and propose directions for leveraging the knowledge gained from model-organisms to identify genetic incompatibilities in non-model organisms.
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Affiliation(s)
- Krishna B. S. Swamy
- Division of Biological and Life Sciences, School of Arts and Sciences, Ahmedabad University, Ahmedabad, India
| | - Scott C. Schuyler
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Head and Neck Surgery, Department of Otolaryngology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Jun-Yi Leu
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
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24
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Omranian S, Angeleska A, Nikoloski Z. PC2P: Parameter-free network-based prediction of protein complexes. Bioinformatics 2021; 37:73-81. [PMID: 33416831 PMCID: PMC8034538 DOI: 10.1093/bioinformatics/btaa1089] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 12/17/2020] [Accepted: 12/30/2020] [Indexed: 11/12/2022] Open
Abstract
Motivation Prediction of protein complexes from protein–protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction. Results To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P: (i) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (ii) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (iii,iv) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks. Availability and implementation https://github.com/SaraOmranian/PC2P. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sara Omranian
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.,Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany
| | | | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Germany.,Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.,Centre of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
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25
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Patra S, Mohapatra A. Protein complex prediction in interaction network based on network motif. Comput Biol Chem 2020; 89:107399. [PMID: 33152665 DOI: 10.1016/j.compbiolchem.2020.107399] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 08/07/2020] [Accepted: 10/01/2020] [Indexed: 11/28/2022]
Abstract
The enormous size of Protein-Protein Interaction (PPI) networks demands efficient computational methods to extract biologically significant protein complexes. A wide variety of algorithms have been proposed to predict protein complexes from PPI networks. However, it is still a challenging task to detect protein complexes with high accuracy and manageable sensitivity. In this manuscript, a novel complex prediction algorithm based on Network Motif (CPNM) is proposed. This algorithm addresses the role of proteins in the embeddings of network motif. These roles are used to define feature vectors and feature weights of proteins. Based on these features, a neighborhood search technique predict the protein complexes that consider both the inherent organization of proteins as well as the dense regions in PPI networks. The performance of the proposed algorithm is evaluated using various evaluation metrics like Precision, Recall, F-measure, Sensitivity, PPV, and Accuracy. The research finding indicates that the proposed algorithm outperforms most of the competing algorithms like MCODE, DPClus, RNSC, COACH, ClusterONE, CMC and PROCODE over the PPI network of Saccharomyces cerevisiae and Homo sapiens.
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Affiliation(s)
- Sabyasachi Patra
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
| | - Anjali Mohapatra
- Bioinformatics Lab, Department of Computer Science, IIIT, Bhubaneswar, India.
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26
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Poot Velez AH, Fontove F, Del Rio G. Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes. Int J Mol Sci 2020; 21:E4787. [PMID: 32640745 PMCID: PMC7370293 DOI: 10.3390/ijms21134787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 06/20/2020] [Accepted: 06/28/2020] [Indexed: 01/22/2023] Open
Abstract
Predicting protein-protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to help improve these predictions, including choosing the proper descriptors of proteins to represent these interactions, among others. In the current work, we provide a representative protein structure that is amenable to PPI classification using machine learning approaches, referred to as residue cluster classes. Through sampling and optimization, we identified the best algorithm-parameter pair to classify PPI from more than 360 different training sets. We tested these classifiers against PPI datasets that were not included in the training set but shared sequence similarity with proteins in the training set to reproduce the situation of most proteins sharing sequence similarity with others. We identified a model with almost no PPI error (96-99% of correctly classified instances) and showed that residue cluster classes of protein pairs displayed a distinct pattern between positive and negative protein interactions. Our results indicated that residue cluster classes are structural features relevant to model PPI and provide a novel tool to mathematically model the protein structure/function relationship.
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Affiliation(s)
- Albros Hermes Poot Velez
- Department of biochemistry and structural biology, Instituto de fisiologia celular, UNAM Mexico City 04510, Mexico;
| | | | - Gabriel Del Rio
- Department of biochemistry and structural biology, Instituto de fisiologia celular, UNAM Mexico City 04510, Mexico;
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27
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Liang X, Zhu W, Lv Z, Zou Q. Molecular Computing and Bioinformatics. Molecules 2019; 24:E2358. [PMID: 31247973 PMCID: PMC6651761 DOI: 10.3390/molecules24132358] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 06/25/2019] [Indexed: 02/06/2023] Open
Abstract
Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of-and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions-even trillions-of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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Affiliation(s)
- Xin Liang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Zhibin Lv
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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