1
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Thareja P, Chhillar RS, Dalal S, Simaiya S, Lilhore UK, Alroobaea R, Alsafyani M, Baqasah AM, Algarni S. Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process. Sci Rep 2024; 14:21797. [PMID: 39294330 PMCID: PMC11410825 DOI: 10.1038/s41598-024-72558-x] [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: 03/14/2024] [Accepted: 09/09/2024] [Indexed: 09/20/2024] Open
Abstract
Protein-protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks' weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.
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Affiliation(s)
- Preeti Thareja
- DCSA, Maharshi Dayanand University, Rohtak, Haryana, India
| | | | - Sandeep Dalal
- DCSA, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Sarita Simaiya
- Arba Minch University, Arba Minch, Ethiopia.
- Department of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India.
| | - Umesh Kumar Lilhore
- Department of Computer Science and Engineering, Galgotias University, Greater Noida, UP, India
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
| | - Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944, Taif, Saudi Arabia
| | - Abdullah M Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
| | - Sultan Algarni
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia
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2
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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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3
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Sousa RT, Silva S, Pesquita C. Explaining protein-protein interactions with knowledge graph-based semantic similarity. Comput Biol Med 2024; 170:108076. [PMID: 38308873 DOI: 10.1016/j.compbiomed.2024.108076] [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: 09/25/2023] [Revised: 12/11/2023] [Accepted: 01/27/2024] [Indexed: 02/05/2024]
Abstract
The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data. Given the complexity of the biomedical domain, explanations should be grounded in domain knowledge which can be achieved by using ontologies and knowledge graphs. These knowledge graphs express knowledge about a domain by capturing different perspectives of the representation of real-world entities. However, the most popular way to explore knowledge graphs with machine learning is through using embeddings, which are not explainable. As an alternative, knowledge graph-based semantic similarity offers the advantage of being explainable. Additionally, similarity can be computed to capture different semantic aspects within the knowledge graph and increasing the explainability of predictive approaches. We propose a novel method to generate explainable vector representations, KGsim2vec, that uses aspect-oriented semantic similarity features to represent pairs of entities in a knowledge graph. Our approach employs a set of machine learning models, including decision trees, genetic programming, random forest and eXtreme gradient boosting, to predict relations between entities. The experiments reveal that considering multiple semantic aspects when representing the similarity between two entities improves explainability and predictive performance. KGsim2vec performs better than black-box methods based on knowledge graph embeddings or graph neural networks. Moreover, KGsim2vec produces global models that can capture biological phenomena and elucidate data biases.
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Affiliation(s)
- Rita T Sousa
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal.
| | - Sara Silva
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
| | - Catia Pesquita
- LASIGE, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal
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4
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Zhang F, Zhang Y, Zhu X, Chen X, Lu F, Zhang X. DeepSG2PPI: A Protein-Protein Interaction Prediction Method Based on Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2907-2919. [PMID: 37079417 DOI: 10.1109/tcbb.2023.3268661] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Protein-protein interaction (PPI) plays an important role in almost all life activities. Many protein interaction sites have been confirmed by biological experiments, but these PPI site identification methods are time-consuming and expensive. In this study, a deep learning-based PPI prediction method, named DeepSG2PPI, is developed. First, the protein sequence information is retrieved and the local context information of each amino acid residue is calculated. A two-dimensional convolutional neural network (2D-CNN) model is employed to extract features from a two-channel coding structure, in which an attention mechanism is embedded to assign higher weights to key features. Second, the global statistical information of each amino acid residue and the relationship graph between the protein and GO (Gene Ontology) function annotation are built, and the graph embedding vector is constructed to represent the biological features of the protein. Finally, a 2D-CNN model and two 1D-CNN models are combined for PPI prediction. The comparison analysis with existing algorithms shows that the DeepSG2PPI method has better performance. It provides more accurate and effective PPI site prediction, which will be helpful in reducing the cost and failure rate of biological experiments.
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5
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Upadhyay A, Ekenna C. A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins. Int J Mol Sci 2023; 24:11785. [PMID: 37511544 PMCID: PMC10380747 DOI: 10.3390/ijms241411785] [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: 06/12/2023] [Revised: 07/07/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations.
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Affiliation(s)
- Aakriti Upadhyay
- Department of Computer Science, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA
| | - Chinwe Ekenna
- Department of Computer Science, University at Albany, State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA
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6
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DeepCF-PPI: improved prediction of protein-protein interactions by combining learned and handcrafted features based on attention mechanisms. APPL INTELL 2023. [DOI: 10.1007/s10489-022-04387-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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7
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Alakus TB, Turkoglu I. Prediction of viral-host interactions of COVID-19 by computational methods. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 228:104622. [PMID: 35879939 PMCID: PMC9301933 DOI: 10.1016/j.chemolab.2022.104622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/20/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.
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Affiliation(s)
- Talha Burak Alakus
- Kirklareli University, Department of Software Engineering, Kirklareli, 39000, Turkey
| | - Ibrahim Turkoglu
- Firat University, Department of Software Engineering, Elazig, 23119, Turkey
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8
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Papakonstantinou E, Io Diakou K, Mitsis T, Dragoumani K, Bacopoulou F, Megalooikonomou V, Kossida S, Chrousos GP, Vlachakis D. Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes. EMBNET.JOURNAL 2022; 27:e1004. [PMID: 35464257 PMCID: PMC9029568 DOI: 10.14806/ej.27.0.1004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Molecular fusion events have a prominent role in the initial steps of carcinogenesis. In this study, a bioinformatics analysis was performed between four organisms that are known to induce cancer development in humans: two viruses, Human Herpesvirus 4, and Human T-cell leukaemia virus, one bacterium, Helicobacter Pylori, and one trematode, Schistosoma mansoni. The annotated proteomes from these organisms were analysed using the SAFE software to identify protein fusion events, which may provide insight into protein function similarities and possible merging events during the course of evolution. Based on the results, five fused proteins with very similar functions were detected, whereas proteins with different functions that might act in the same molecular complex or biochemical pathway were not found. Thus, this study analysed the above four well-known cancer-related organisms with de novo bioinformatics programs and provided useful information on protein fusion events, hopefully leading to deeper understanding of carcinogenenesis.
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Affiliation(s)
- Eleni Papakonstantinou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Kalliopi Io Diakou
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Thanasis Mitsis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Konstantina Dragoumani
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Flora Bacopoulou
- University Research Institute of Maternal and Child Health & Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
| | - Vasilis Megalooikonomou
- Computer Engineering and Informatics Department, School of Engineering, University of Patras, Patras. Greece
| | - Sophia Kossida
- IMGT, The International ImMunoGeneTics Information System, Université de Montpellier, Laboratoire d'ImmunoGénétique Moléculaire and Institut de Génétique Humaine, University of Montpellier, Montpellier, France
| | - George P Chrousos
- University Research Institute of Maternal and Child Health & Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
- University Research Institute of Maternal and Child Health & Precision Medicine, and UNESCO Chair on Adolescent Health Care, National and Kapodistrian University of Athens, "Aghia Sophia" Children's Hospital, Athens, Greece
- Division of Endocrinology and Metabolism, Center of Clinical, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
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9
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Cha M, Emre EST, Xiao X, Kim JY, Bogdan P, VanEpps JS, Violi A, Kotov NA. Unifying structural descriptors for biological and bioinspired nanoscale complexes. NATURE COMPUTATIONAL SCIENCE 2022; 2:243-252. [PMID: 38177552 DOI: 10.1038/s43588-022-00229-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/17/2022] [Indexed: 01/06/2024]
Abstract
Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein-protein interactions can serve as a guide for designing protein-nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein-protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein-protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.
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Affiliation(s)
- Minjeong Cha
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Emine Sumeyra Turali Emre
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Xiongye Xiao
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Ji-Young Kim
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Paul Bogdan
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - J Scott VanEpps
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Program in Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
- Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA
| | - Angela Violi
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA
- Biophysics Program, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas A Kotov
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA.
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
- Program in Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
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10
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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11
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Li S, Wu S, Wang L, Li F, Jiang H, Bai F. Recent advances in predicting protein-protein interactions with the aid of artificial intelligence algorithms. Curr Opin Struct Biol 2022; 73:102344. [PMID: 35219216 DOI: 10.1016/j.sbi.2022.102344] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 01/02/2022] [Accepted: 01/17/2022] [Indexed: 12/15/2022]
Abstract
Protein-protein interactions (PPIs) are essential in the regulation of biological functions and cell events, therefore understanding PPIs have become a key issue to understanding the molecular mechanism and investigating the design of drugs. Here we highlight the major developments in computational methods developed for predicting PPIs by using types of artificial intelligence algorithms. The first part introduces the source of experimental PPI data. The second part is devoted to the PPI prediction methods based on sequential information. The third part covers representative methods using structural information as the input feature. The last part is methods designed by combining different types of features. For each part, the state-of-the-art computational PPI prediction methods are reviewed in an inclusive view. Finally, we discuss the flaws existing in this area and future directions of next-generation algorithms.
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Affiliation(s)
- Shiwei Li
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Sanan Wu
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Lin Wang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Fenglei Li
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, China
| | - Hualiang Jiang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Pudong, Shanghai, 201203, China
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
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12
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Munjal NS, Sapra D, Parthasarathi KTS, Goyal A, Pandey A, Banerjee M, Sharma J. Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods. Pathogens 2022; 11:pathogens11020259. [PMID: 35215201 PMCID: PMC8874499 DOI: 10.3390/pathogens11020259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/31/2022] [Accepted: 02/08/2022] [Indexed: 01/04/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein–protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.
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Affiliation(s)
- Nupur S. Munjal
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; (N.S.M.); (D.S.); (K.T.S.P.); (A.G.)
| | - Dikscha Sapra
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; (N.S.M.); (D.S.); (K.T.S.P.); (A.G.)
| | - K. T. Shreya Parthasarathi
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; (N.S.M.); (D.S.); (K.T.S.P.); (A.G.)
| | - Abhishek Goyal
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; (N.S.M.); (D.S.); (K.T.S.P.); (A.G.)
| | - Akhilesh Pandey
- Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India;
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Manidipa Banerjee
- Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India;
| | - Jyoti Sharma
- Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; (N.S.M.); (D.S.); (K.T.S.P.); (A.G.)
- Manipal Academy of Higher Education (MAHE), Udupi 576104, India
- Correspondence:
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13
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Song B, Luo X, Luo X, Liu Y, Niu Z, Zeng X. Learning spatial structures of proteins improves protein-protein interaction prediction. Brief Bioinform 2022; 23:6501351. [PMID: 35018418 DOI: 10.1093/bib/bbab558] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/07/2021] [Accepted: 12/07/2021] [Indexed: 01/09/2023] Open
Abstract
Spatial structures of proteins are closely related to protein functions. Integrating protein structures improves the performance of protein-protein interaction (PPI) prediction. However, the limited quantity of known protein structures restricts the application of structure-based prediction methods. Utilizing the predicted protein structure information is a promising method to improve the performance of sequence-based prediction methods. We propose a novel end-to-end framework, TAGPPI, to predict PPIs using protein sequence alone. TAGPPI extracts multi-dimensional features by employing 1D convolution operation on protein sequences and graph learning method on contact maps constructed from AlphaFold. A contact map contains abundant spatial structure information, which is difficult to obtain from 1D sequence data directly. We further demonstrate that the spatial information learned from contact maps improves the ability of TAGPPI in PPI prediction tasks. We compare the performance of TAGPPI with those of nine state-of-the-art sequence-based methods, and TAGPPI outperforms such methods in all metrics. To the best of our knowledge, this is the first method to use the predicted protein topology structure graph for sequence-based PPI prediction. More importantly, our proposed architecture could be extended to other prediction tasks related to proteins.
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Affiliation(s)
- Bosheng Song
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China
| | - Xiaoyan Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China.,MindRank AI ltd., Hangzhou, 311113, Zhejiang, China
| | - Xiaoli Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China.,BioMap, Haidian, 100089, Beijing, China
| | - Yuansheng Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China
| | | | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China
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14
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Dong TN, Brogden G, Gerold G, Khosla M. A multitask transfer learning framework for the prediction of virus-human protein-protein interactions. BMC Bioinformatics 2021; 22:572. [PMID: 34837942 PMCID: PMC8626732 DOI: 10.1186/s12859-021-04484-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/15/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. RESULTS We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. CONCLUSIONS Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer .
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Affiliation(s)
- Thi Ngan Dong
- L3S Research Center, Leibniz University Hannover, Hannover, Germany.
| | - Graham Brogden
- Institute for Biochemistry, University of Veterinary Medicine, Hannover, Germany.,Institute of Experimental Virology, TWINCORE, Center for Experimental and Clinical Infection Research Hannover, Hannover, Germany
| | - Gisa Gerold
- Institute for Biochemistry, University of Veterinary Medicine, Hannover, Germany.,Institute of Experimental Virology, TWINCORE, Center for Experimental and Clinical Infection Research Hannover, Hannover, Germany.,Department of Clinical Microbiology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden
| | - Megha Khosla
- L3S Research Center, Leibniz University Hannover, Hannover, Germany
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15
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ALAKUŞ TB, TÜRKOĞLU İ. Kanser Teşhisinde Protein Haritalama Tekniklerinin Başarımlarının Derin Öğrenme Kullanılarak Karşılaştırılması. FIRAT ÜNIVERSITESI MÜHENDISLIK BILIMLERI DERGISI 2021; 33:547-565. [DOI: 10.35234/fumbd.881228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Kanser, dünya çapında çoğu insanın ölmesine neden olan ve birçok farklı alt tiplerden oluşan heterojen bir hastalıktır. Bir kanser türünün erken teşhisi ve prognozu, hastaların sonraki klinik takibini kolaylaştırabildiği için kanser araştırmalarında bir gereklilik haline gelmiştir. Bunun için en çok kullanılan yöntemlerden birisi histolojik incelemedir. Ancak bu yöntemde çok sayıda gözlemciler arası değişkenlik bulunmakta, bu ise inceleme sürecinin uzun olmasına ve zaman almasına neden olmaktadır. Bu dezavantajın önüne geçmek için araştırmacılar hesaplama-tabanlı yaklaşımlara yönelmişler ve kanserli proteinlerin belirlenmesi için protein-protein etkileşimleri, protein etkileşim ağları ve moleküler parmak izleri yöntemlerinden yararlanmaktadırlar. Bu yöntemler arasında, çeşitli çalışmalar genomik bilgilerden de kanserli hücrelerin tespit edilebildiğini göstermiştir. Kansere ait genlerin dizilimlerine göre belirli kanser türlerinin belirlenebildiği ve bu süreçte yapay öğrenme tabanlı yaklaşımların etkili olduğu görülmüştür. Bu çalışmada, derin öğrenme algoritmalarından birisi olan tekrarlayıcı sinir ağı mimarisi kullanılmış ve insana ait mesane, kolon ve prostat kanserlerinin, protein dizilimlerine göre sınıflandırılması yapılmıştır. Çalışma, verilerin elde edilmesi, protein dizilimlerinin sayısallaştırılması, derin öğrenme model uygulamasının geliştirilmesi ve protein haritalama tekniklerinin başarımının karşılaştırılması olmak üzere dört aşamadan meydana gelmektedir. Protein dizilimlerini sayısallaştırmak için AESNN1, hidrofobiklik, tam sayı, Miyazawa enerjileri ve rastgele kodlama yöntemleri ele alınmıştır. Çalışmanın sonunda, mesane kanseri için en yüksek doğruluk değeri %87.15 ile AESNN1 haritalama yöntemiyle, kolon kanseri ve prostat kanseri için ise en yüksek doğruluk değeri sırasıyla %94.40 ve %75.45 olarak Miyazawa enerjileri ve rastgele kodlama protein haritalama yöntemi ile elde edilmiştir. Bu çalışma ile yapay öğrenme ve protein haritalama tekniklerinin, kanserli protein dizilimlerinin belirlenmesinde etkili olduğu gözlemlenmiştir.
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16
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Martins YC, Ziviani A, Nicolás MF, de Vasconcelos ATR. Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy. FRONTIERS IN BIOINFORMATICS 2021; 1:731345. [PMID: 36303787 PMCID: PMC9581021 DOI: 10.3389/fbinf.2021.731345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022] Open
Abstract
Predicting the physical or functional associations through protein-protein interactions (PPIs) represents an integral approach for inferring novel protein functions and discovering new drug targets during repositioning analysis. Recent advances in high-throughput data generation and multi-omics techniques have enabled large-scale PPI predictions, thus promoting several computational methods based on different levels of biological evidence. However, integrating multiple results and strategies to optimize, extract interaction features automatically and scale up the entire PPI prediction process is still challenging. Most procedures do not offer an in-silico validation process to evaluate the predicted PPIs. In this context, this paper presents the PredPrIn scientific workflow that enables PPI prediction based on multiple lines of evidence, including the structure, sequence, and functional annotation categories, by combining boosting and stacking machine learning techniques. We also present a pipeline (PPIVPro) for the validation process based on cellular co-localization filtering and a focused search of PPI evidence on scientific publications. Thus, our combined approach provides means to extensive scale training or prediction of new PPIs and a strategy to evaluate the prediction quality. PredPrIn and PPIVPro are publicly available at https://github.com/YasCoMa/predprin and https://github.com/YasCoMa/ppi_validation_process.
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Affiliation(s)
- Yasmmin Côrtes Martins
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Artur Ziviani
- Data Extreme Lab (DEXL), National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Marisa Fabiana Nicolás
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
| | - Ana Tereza Ribeiro de Vasconcelos
- Bioinformatics Laboratory, National Laboratory of Scientific Computing, Petrópolis, Brazil
- *Correspondence: Ana Tereza Ribeiro de Vasconcelos,
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17
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Pal A, Pal D, Mitra P. A computational framework for modeling functional protein-protein interactions. Proteins 2021; 89:1353-1364. [PMID: 34076296 DOI: 10.1002/prot.26156] [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: 11/01/2020] [Revised: 04/17/2021] [Accepted: 05/19/2021] [Indexed: 11/06/2022]
Abstract
Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.
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Affiliation(s)
- Abantika Pal
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science Bangalore, Bangalore, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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18
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Jin SC, Kim MH, Choi LY, Nam YK, Yang WM. Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2021; 86:153557. [PMID: 33852976 DOI: 10.1016/j.phymed.2021.153557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Pine nut oil (PNO), a standardized and well-defined extract of Pinus koraiensis (Korean pine), has beneficial effects on wound healing, inflammatory diseases, and cancer. However, the explanation for the mechanism by which PNO reduces body fat remains uncertain. We performed a protein-protein interaction network (PPIN) analysis to explore the genes associated with pinolenic acid using the MEDILINE database from PubChem and PubMed. It was concluded through the PPIN analysis that PNO was involved in a neutral lipid biosynthetic process. PURPOSE This study evaluated the effects of PNO predicted by the network analysis of fat accumulation in chronic obesity mouse models established by feeding a high fat diet (HFD) to C57BL/6J mice and explored potential mechanisms. METHODS HFD mice were fed only HFD or HFD with PNO at 822 and 1644 mg/kg. After an oral administration of 7 weeks, several body weight and body fat-related parameters were examined, including the following: adipose weight, adipocyte size, serum lipid profiles, adipocyte expression of PPAR-γ, sterol regulatory element binding protein (SREBP)-1c, lipoprotein lipase (LPL) and leptin. RESULTS We showed that oral administration of PNO to HFD mice reduces body fat weight, fat in tissue, white adipose tissue weight, and adipocyte size. The serum cholesterol was improved in the HFD mice treated with PNO. Additionally, PNO has significantly attenuated the HFD-induced changes in the adipose tissue expression of PPAR-γ, SREBP-1c, LPL, and leptin. CONCLUSIONS The findings from this study based on the PPIN analysis suggest that PNO has potential as drug to reduce body fat through fat regulatory mechanisms by PPAR-γ and SREBP-1c.
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Affiliation(s)
- Seong Chul Jin
- Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Mi Hye Kim
- Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - La Yoon Choi
- Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeon Kyung Nam
- Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Woong Mo Yang
- Department of Convergence Korean Medical Science, College of Korean Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
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19
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Czibula G, Albu AI, Bocicor MI, Chira C. AutoPPI: An Ensemble of Deep Autoencoders for Protein-Protein Interaction Prediction. ENTROPY 2021; 23:e23060643. [PMID: 34064042 PMCID: PMC8223997 DOI: 10.3390/e23060643] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/08/2021] [Accepted: 05/19/2021] [Indexed: 01/06/2023]
Abstract
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled AutoPPI, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. The results indicate good performances of our proposed model, with accuracy and AUC values of over 0.97 in all cases. The best performing model relies on a Siamese architecture in both the encoder and the decoder, which advantageously captures common features in protein pairs. Comparisons with other machine learning techniques applied for the same problem prove that AutoPPI outperforms most of its contenders, for the considered data sets.
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20
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Vedithi SC, Malhotra S, Acebrón-García-de-Eulate M, Matusevicius M, Torres PHM, Blundell TL. Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae. Front Mol Biosci 2021; 8:663301. [PMID: 34026836 PMCID: PMC8138464 DOI: 10.3389/fmolb.2021.663301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/12/2021] [Indexed: 02/02/2023] Open
Abstract
Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.
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Affiliation(s)
- Sundeep Chaitanya Vedithi
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Sundeep Chaitanya Vedithi,
| | - Sony Malhotra
- Rutherford Appleton Laboratory, Science and Technology Facilities Council, Oxon, United Kingdom
| | | | | | - Pedro Henrique Monteiro Torres
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Tom L. Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom,Tom L. Blundell,
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21
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Pei F, Shi Q, Zhang H, Bahar I. Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization. J Chem Inf Model 2021; 61:1670-1682. [PMID: 33831302 DOI: 10.1021/acs.jcim.1c00173] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.
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Affiliation(s)
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing 100084, China
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22
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A novel entropy-based mapping method for determining the protein-protein interactions in viral genomes by using coevolution analysis. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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23
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Su XR, You ZH, Hu L, Huang YA, Wang Y, Yi HC. An Efficient Computational Model for Large-Scale Prediction of Protein-Protein Interactions Based on Accurate and Scalable Graph Embedding. Front Genet 2021; 12:635451. [PMID: 33719344 PMCID: PMC7953052 DOI: 10.3389/fgene.2021.635451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/25/2021] [Indexed: 11/23/2022] Open
Abstract
Protein–protein interaction (PPI) is the basis of the whole molecular mechanisms of living cells. Although traditional experiments are able to detect PPIs accurately, they often encounter high cost and require more time. As a result, computational methods have been used to predict PPIs to avoid these problems. Graph structure, as the important and pervasive data carriers, is considered as the most suitable structure to present biomedical entities and relationships. Although graph embedding is the most popular approach for graph representation learning, it usually suffers from high computational and space cost, especially in large-scale graphs. Therefore, developing a framework, which can accelerate graph embedding and improve the accuracy of embedding results, is important to large-scale PPIs prediction. In this paper, we propose a multi-level model LPPI to improve both the quality and speed of large-scale PPIs prediction. Firstly, protein basic information is collected as its attribute, including positional gene sets, motif gene sets, and immunological signatures. Secondly, we construct a weighted graph by using protein attributes to calculate node similarity. Then GraphZoom is used to accelerate the embedding process by reducing the size of the weighted graph. Next, graph embedding methods are used to learn graph topology features from the reconstructed graph. Finally, the linear Logistic Regression (LR) model is used to predict the probability of interactions of two proteins. LPPI achieved a high accuracy of 0.99997 and 0.9979 on the PPI network dataset and GraphSAGE-PPI dataset, respectively. Our further results show that the LPPI is promising for large-scale PPI prediction in both accuracy and efficiency, which is beneficial to other large-scale biomedical molecules interactions detection.
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Affiliation(s)
- Xiao-Rui Su
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Yu-An Huang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Yi Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
| | - Hai-Cheng Yi
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China.,University of Chinese Academy of Sciences, Beijing, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Ürümqi, China
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24
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Sharma A, Kumar R, Ranjta S, Varadwaj PK. SMILES to Smell: Decoding the Structure-Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach. J Chem Inf Model 2021; 61:676-688. [PMID: 33449694 DOI: 10.1021/acs.jcim.0c01288] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Finding the relationship between the structure of an odorant molecule and its associated smell has always been an extremely challenging task. The major limitation in establishing the structure-odor relation is the vague and ambiguous nature of the descriptor-labeling, especially when the sources of odorant molecules are different. With the advent of deep networks, data-driven approaches have been substantiated to achieve more accurate linkages between the chemical structure and its smell. In this study, the deep neural network (DNN) with physiochemical properties and molecular fingerprints (PPMF) and the convolution neural network (CNN) with chemical-structure images (IMG) are developed to predict the smells of chemicals using their SMILES notations. A data set of 5185 chemical compounds with 104 smell percepts was used to develop the multilabel prediction models. The accuracies of smell prediction from DNN + PPMF and CNN + IMG (Xception based) were found to be 97.3 and 98.3%, respectively, when applied on an independent test set of chemicals. The deep learning architecture combining both DNN + PPMF and CNN + IMG prediction models is proposed, which classifies smells and may help understand the generic mechanism underlying the relationship between chemical structure and smell perception.
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Affiliation(s)
- Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad 211012, Uttar Pradesh, India.,Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus 226010, Uttar Pradesh, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus 226010, Uttar Pradesh, India
| | - Shabnam Ranjta
- Department of Chemistry, SGGS College, Chandigarh 160019, India
| | - Pritish Kumar Varadwaj
- Department of Applied Science, Indian Institute of Information Technology, Allahabad 211012, Uttar Pradesh, India
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25
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Alakus TB, Turkoglu I. A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning. Interdiscip Sci 2021; 13:44-60. [PMID: 33433784 PMCID: PMC7801232 DOI: 10.1007/s12539-020-00405-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 11/23/2020] [Accepted: 11/28/2020] [Indexed: 12/11/2022]
Abstract
The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions of new types of corona virus is vital in clinical studies, drug therapy, identification of preclinical compounds and protein functions. Protein–protein interactions are important to examine protein functions and pathways involved in various biological processes and to determine the cause and progression of diseases. Various high-throughput experimental methods have been used to identify protein–protein interactions in organisms, yet, there is still a huge gap in specifying all possible protein interactions in an organism. In addition, since the experimental methods used include cloning, labeling, affinity purification mass spectrometry, the processes take a long time. Determining these interactions with artificial intelligence-based methods rather than experimental approaches may help to identify protein functions faster. Thus, protein–protein interaction prediction using deep-learning algorithms has been employed in conjunction with experimental method to explore new protein interactions. However, to predict protein interactions with artificial intelligence techniques, protein sequences need to be mapped. There are various types and numbers of protein-mapping methods in the literature. In this study, we wanted to contribute to the literature by proposing a novel protein-mapping method based on the AVL tree. The proposed method was inspired by the fast search performance on the dictionary structure of AVL tree and was used to verify the protein interactions between SARS-COV-2 virus and human. First, protein sequences were mapped by both the proposed method and various protein-mapping methods. Then, the mapped protein sequences were normalized and classified by bidirectional recurrent neural networks. The performance of the proposed method was evaluated with accuracy, f1-score, precision, recall, and AUC scores. Our results indicated that our mapping method predicts the protein interactions between SARS-COV-2 virus proteins and human proteins at an accuracy of 97.76%, precision of 97.60%, recall of 98.33%, f1-score of 79.42%, and with AUC 89% in average.
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Affiliation(s)
- Talha Burak Alakus
- Faculty of Engineering, Department of Software Engineering, Kirklareli University, 39000, Kirklareli, Turkey.
| | - Ibrahim Turkoglu
- Faculty of Technology, Department of Software Engineering, Firat University, 23119, Elazig, Turkey
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Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020; 21:454-463. [PMID: 33093807 PMCID: PMC7536797 DOI: 10.2174/1389202921999200625103936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/19/2020] [Accepted: 05/27/2020] [Indexed: 12/22/2022] Open
Abstract
Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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Zhao L, Wang J, Hu Y, Cheng L. Conjoint Feature Representation of GO and Protein Sequence for PPI Prediction Based on an Inception RNN Attention Network. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:198-208. [PMID: 33230427 PMCID: PMC7515979 DOI: 10.1016/j.omtn.2020.08.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
Protein-protein interactions (PPIs) are pivotal for cellular functions and biological processes. In the past years, computational methods using amino acid sequences and gene ontology (GO) annotations of proteins for prioritizing PPIs have provided important references for biological experiments in the wet lab. Despite the current success, sequence information and ontological annotation in semantic representation have not been integrated into current methods. We propose a deep-learning-based PPI prediction methodology conjointly featuring sequence information and GO annotation. First, we adopt a word-embedding tool, the NCBI-blueBERT model pre-trained on PubMed, to map the GO terms into their semantic vectors. Then, the GO semantic vectors and protein sequence vector serve as the input of the proposed inception recurrent neural network (RNN) attention network (IRAN). The IRAN captures the spatial relationship and the potential sequential feature of the protein sequence and ontological annotation semantics. The extensive experimental results on 12 benchmarks demonstrate that our method achieves superiority over state-of-the-art baselines. In the yeast dataset of a binary PPI prediction, our method improved the performance with the Matthews correlation coefficient increasing from 94.2% to 98.2% and the accuracy from 97.1% to 98.2%. The analogous results were also obtained in other comparison evaluations.
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Affiliation(s)
- Lingling Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Junjie Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Yang Hu
- Department of Computer Science, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Liang Cheng
- NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin 150028, Heilongjiang, China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, Heilongjiang, China
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Hao Shi, Yan KK, Ding L, Qian C, Chi H, Yu J. Network Approaches for Dissecting the Immune System. iScience 2020; 23:101354. [PMID: 32717640 PMCID: PMC7390880 DOI: 10.1016/j.isci.2020.101354] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/21/2020] [Accepted: 07/08/2020] [Indexed: 02/06/2023] Open
Abstract
The immune system is a complex biological network composed of hierarchically organized genes, proteins, and cellular components that combat external pathogens and monitor the onset of internal disease. To meet and ultimately defeat these challenges, the immune system orchestrates an exquisitely complex interplay of numerous cells, often with highly specialized functions, in a tissue-specific manner. One of the major methodologies of systems immunology is to measure quantitatively the components and interaction levels in the immunologic networks to construct a computational network and predict the response of the components to perturbations. The recent advances in high-throughput sequencing techniques have provided us with a powerful approach to dissecting the complexity of the immune system. Here we summarize the latest progress in integrating omics data and network approaches to construct networks and to infer the underlying signaling and transcriptional landscape, as well as cell-cell communication, in the immune system, with a focus on hematopoiesis, adaptive immunity, and tumor immunology. Understanding the network regulation of immune cells has provided new insights into immune homeostasis and disease, with important therapeutic implications for inflammation, cancer, and other immune-mediated disorders.
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Affiliation(s)
- Hao Shi
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Koon-Kiu Yan
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Liang Ding
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Chenxi Qian
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jiyang Yu
- Departments of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
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Manzoni C, Lewis PA, Ferrari R. Network Analysis for Complex Neurodegenerative Diseases. CURRENT GENETIC MEDICINE REPORTS 2020. [DOI: 10.1007/s40142-020-00181-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Abstract
Purpose of Review
Biomedicine is witnessing a paradigm shift in the way complex disorders are investigated. In particular, the need for big data interpretation has led to the development of pipelines that require the cooperation of different fields of expertise, including medicine, functional biology, informatics, mathematics and systems biology. This review sits at the crossroad of different disciplines and surveys the recent developments in the use of graph theory (in the form of network analysis) to interpret large and different datasets in the context of complex neurodegenerative diseases. It aims at a professional audience with different backgrounds.
Recent Findings
Biomedicine has entered the era of big data, and this is actively changing the way we approach and perform research. The increase in size and power of biomedical studies has led to the establishment of multi-centre, international working groups coordinating open access platforms for data generation, storage and analysis. Particularly, pipelines for data interpretation are under development, and network analysis is gaining momentum since it represents a versatile approach to study complex systems made of interconnected multiple players.
Summary
We will describe the era of big data in biomedicine and survey the major freely accessible multi-omics datasets. We will then introduce the principles of graph theory and provide examples of network analysis applied to the interpretation of complex neurodegenerative disorders.
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