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Muslu O, Hoyt CT, Lacerda M, Hofmann-Apitius M, Frohlich H. GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:491-500. [PMID: 32750869 DOI: 10.1109/tcbb.2020.3003830] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.
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Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes. Methods Mol Biol 2021. [PMID: 34709630 DOI: 10.1007/978-1-0716-1720-5_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Computational tool composites alternative way to identify essential genes and it is low-cost and time-efficient. Based on experimental essentiality sets deposited in the databases DEG and OGEE as reference, we developed an automatically computational tool named Geptop to select essential genes from the set of protein-coding genes in a prokaryotic genome, which utilizes the strategy of reciprocally best hit for homology search and evolutionary distance for weight assigning. The latest version of Geptop is 2.0 ( http://guolab.whu.edu.cn/geptop ), which can predict gene essentiality with the mean AUC 0f 0.84 in prokaryotes and is more stable. The chapter is to briefly introduce the tool and tell how to use it.
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Alveolar Regeneration in COVID-19 Patients: A Network Perspective. Int J Mol Sci 2021; 22:ijms222011279. [PMID: 34681944 PMCID: PMC8538208 DOI: 10.3390/ijms222011279] [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: 08/17/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 12/12/2022] Open
Abstract
A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.
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Gupta SK, Ponte-Sucre A, Bencurova E, Dandekar T. An Ebola, Neisseria and Trypanosoma human protein interaction census reveals a conserved human protein cluster targeted by various human pathogens. Comput Struct Biotechnol J 2021; 19:5292-5308. [PMID: 34745452 PMCID: PMC8531761 DOI: 10.1016/j.csbj.2021.09.017] [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: 03/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 12/28/2022] Open
Abstract
Filovirus ebolavirus (ZE; Zaire ebolavirus, Bundibugyo ebolavirus), Neisseria meningitidis (NM), and Trypanosoma brucei (Tb) are serious infectious pathogens, spanning viruses, bacteria and protists and all may target the blood and central nervous system during their life cycle. NM and Tb are extracellular pathogens while ZE is obligatory intracellular, targetting immune privileged sites. By using interactomics and comparative evolutionary analysis we studied whether conserved human proteins are targeted by these pathogens. We examined 2797 unique pathogen-targeted human proteins. The information derived from orthology searches of experimentally validated protein-protein interactions (PPIs) resulted both in unique and shared PPIs for each pathogen. Comparing and analyzing conserved and pathogen-specific infection pathways for NM, TB and ZE, we identified human proteins predicted to be targeted in at least two of the compared host-pathogen networks. However, four proteins were common to all three host-pathogen interactomes: the elongation factor 1-alpha 1 (EEF1A1), the SWI/SNF complex subunit SMARCC2 (matrix-associated actin-dependent regulator of chromatin subfamily C), the dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1 (RPN1), and the tubulin beta-5 chain (TUBB). These four human proteins all are also involved in cytoskeleton and its regulation and are often addressed by various human pathogens. Specifically, we found (i) 56 human pathogenic bacteria and viruses that target these four proteins, (ii) the well researched new pandemic pathogen SARS-CoV-2 targets two of these four human proteins and (iii) nine human pathogenic fungi (yet another evolutionary distant organism group) target three of the conserved proteins by 130 high confidence interactions.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- Evolutionary Genomics Group, Center for Computational and Theoretical Biology, University of Würzburg, 97078 Würzburg, Germany
| | - Alicia Ponte-Sucre
- Laboratorio de Fisiología Molecular, Instituto de Medicina Experimental, Escuela Luis Razetti, Universidad Central de Venezuela, Caracas, Venezuela
- Medical Mission Institute, Hermann-Schell-Str. 7, 97074 Würzburg, Germany
| | - Elena Bencurova
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, University of Würzburg, 97074 Würzburg, Germany
- EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117 Heidelberg, Germany
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Pathogen and Host-Pathogen Protein Interactions Provide a Key to Identify Novel Drug Targets. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11607-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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Chand Y, Alam MA, Singh S. Pan-genomic analysis of the species Salmonella enterica: Identification of core essential and putative essential genes. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100669] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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Gupta SK, Srivastava M, Osmanoglu Ö, Dandekar T. Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs. Sci Rep 2020; 10:2334. [PMID: 32047225 PMCID: PMC7012867 DOI: 10.1038/s41598-020-59344-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 01/22/2020] [Indexed: 12/18/2022] Open
Abstract
Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.
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Affiliation(s)
- Shishir K Gupta
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany.,Department of Microbiology, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Mugdha Srivastava
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Özge Osmanoglu
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany
| | - Thomas Dandekar
- Functional Genomics and Systems Biology Group, Department of Bioinformatics, Biocenter, Am Hubland, D-97074, Würzburg, Germany. .,EMBL Heidelberg, BioComputing Unit, Meyerhofstraße 1, 69117, Heidelberg, Germany.
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Wen QF, Liu S, Dong C, Guo HX, Gao YZ, Guo FB. Geptop 2.0: An Updated, More Precise, and Faster Geptop Server for Identification of Prokaryotic Essential Genes. Front Microbiol 2019; 10:1236. [PMID: 31214154 PMCID: PMC6558110 DOI: 10.3389/fmicb.2019.01236] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Accepted: 05/17/2019] [Indexed: 12/16/2022] Open
Abstract
Geptop has performed effectively in the identification of prokaryotic essential genes since its first release in 2013. It estimates gene essentiality for prokaryotes based on orthology and phylogeny. Genome-scale essentiality data of more prokaryotic species are available, and the information has been collected into public essential gene repositories such as DEG and OGEE. A faster and more accurate toolkit is needed to meet the increasing prokaryotic genome data. We updated Geptop by supplementing more validated essentiality data into reference set (from 19 to 37 species), and introducing multi-process technology to accelerate the computing speed. Compared with Geptop 1.0 and other gene essentiality prediction models, Geptop 2.0 can generate more stable predictions and finish the computation in a shorter time. The software is available both as an online server and a downloadable standalone application. We hope that the improved Geptop 2.0 will facilitate researches in gene essentiality and the development of novel antibacterial drugs. The gene essentiality prediction tool is available at http://cefg.uestc.cn/geptop.
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Affiliation(s)
- Qing-Feng Wen
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuo Liu
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chuan Dong
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hai-Xia Guo
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yi-Zhou Gao
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng-Biao Guo
- School of Life Sciences and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Chernov VM, Chernova OA, Mouzykantov AA, Lopukhov LL, Aminov RI. Omics of antimicrobials and antimicrobial resistance. Expert Opin Drug Discov 2019; 14:455-468. [DOI: 10.1080/17460441.2019.1588880] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Vladislav M. Chernov
- Kazan Institute of Biochemistry and Biophysics, FRC Kazan Scientific Center of RAS, Kazan, Russian Federation
- Institute of Fundamental Medicine and Biology, Kazan (Volga region) Federal University, Kazan, Russian Federation
| | - Olga A. Chernova
- Kazan Institute of Biochemistry and Biophysics, FRC Kazan Scientific Center of RAS, Kazan, Russian Federation
- Institute of Fundamental Medicine and Biology, Kazan (Volga region) Federal University, Kazan, Russian Federation
| | - Alexey A. Mouzykantov
- Kazan Institute of Biochemistry and Biophysics, FRC Kazan Scientific Center of RAS, Kazan, Russian Federation
- Institute of Fundamental Medicine and Biology, Kazan (Volga region) Federal University, Kazan, Russian Federation
| | - Leonid L. Lopukhov
- Institute of Fundamental Medicine and Biology, Kazan (Volga region) Federal University, Kazan, Russian Federation
| | - Rustam I. Aminov
- Institute of Fundamental Medicine and Biology, Kazan (Volga region) Federal University, Kazan, Russian Federation
- Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
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Identification of Antifungal Targets Based on Computer Modeling. J Fungi (Basel) 2018; 4:jof4030081. [PMID: 29973534 PMCID: PMC6162656 DOI: 10.3390/jof4030081] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 06/24/2018] [Accepted: 06/29/2018] [Indexed: 01/07/2023] Open
Abstract
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
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Ahmad S, Raza S, Uddin R, Azam SS. Comparative subtractive proteomics based ranking for antibiotic targets against the dirtiest superbug: Acinetobacter baumannii. J Mol Graph Model 2018; 82:74-92. [PMID: 29705560 DOI: 10.1016/j.jmgm.2018.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/06/2018] [Accepted: 04/09/2018] [Indexed: 11/27/2022]
Abstract
Multidrug-resistant Acinetobacter baumannii is indeed to be the most successful nosocomial pathogen responsible for myriad infections in modern health care system. Computational methodologies based on genomics and proteomics proved to be powerful tools for providing substantial information about different aspects of A. baumannii biology that made it possible to design new approaches for treating multi, extensive and total drug resistant isolates of A. baumannii. In this current approach, 35 completely annotated proteomes of A. bauamnnii were filtered through a comprehensive subtractive proteomics pipeline for broad-spectrum drug candidates. In total, 10 proteins (KdsA, KdsB, LpxA, LpxC, LpxD, GpsE, PhoB, UvrY, KdpE and OmpR) could serve as ideal candidates for designing novel antibiotics. The work was extended with KdsA enzyme for structure information, prediction of intrinsic disorders, active site details, and structure based virtual screening of library containing natural product-like scaffolds. Most of the enzyme structure has fixed three-dimensional conformation. The selection of inhibitor for KdsA enzyme was based on druglikeness, pharmacokinetics and docking scores. Compound-4636 (5-((3-chloro-5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)methoxy)-2-(((1-hydroxy-4-methylpentan-2-yl)amino)methyl)phenol) was revealed as the most potent inhibitor against A. baumannii KdsA enzyme having Gold fitness score of 77.68 and Autodock binding energy of -6.2 kcal/mol. The inhibitor completely follows Lipinski rule of five, Ghose rule, and Egan rule. Molecular dynamics simulation for KdsA and KdsA-4636 complex was performed for 100 ns to unveil what conformational changes the enzyme underwent in the absence and presence of the inhibitor, respectively. The average root means square deviation (RMSD) for both systems was found 3.5 Å, which signifies stable structure of the enzyme in both bounded and unbounded states. Absolute binding energy using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) reflected high affinity and vigorous interactions of the inhibitor with enzyme active residues. Findings of the current study could open up new avenues for experimentalists to design new potent antibiotics by targeting the targets screened in this study.
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Affiliation(s)
- Sajjad Ahmad
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Saad Raza
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Reaz Uddin
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Syed Sikander Azam
- Computational Biology Lab, National Center for Bioinformatics, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
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