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Frantsuzova E, Bogun A, Kopylova O, Vetrova A, Solyanikova I, Streletskii R, Delegan Y. Genomic, Phylogenetic and Physiological Characterization of the PAH-Degrading Strain Gordonia polyisoprenivorans 135. BIOLOGY 2024; 13:339. [PMID: 38785821 PMCID: PMC11117675 DOI: 10.3390/biology13050339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 05/07/2024] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
The strain Gordonia polyisoprenivorans 135 is able to utilize a wide range of aromatic compounds. The aim of this work was to study the features of genetic organization and biotechnological potential of the strain G. polyisoprenivorans 135 as a degrader of aromatic compounds. The study of the genome of the strain 135 and the pangenome of the G. polyisoprenivorans species revealed that some genes, presumably involved in PAH catabolism, are atypical for Gordonia and belong to the pangenome of Actinobacteria. Analyzing the intergenic regions of strain 135 alongside the "panIGRome" of G. polyisoprenivorans showed that some intergenic regions in strain 135 also differ from those located between the same pairs of genes in related strains. The strain G. polyisoprenivorans 135 in our work utilized naphthalene (degradation degree 39.43%) and grew actively on salicylate. At present, this is the only known strain of G. polyisoprenivorans with experimentally confirmed ability to utilize these compounds.
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Affiliation(s)
- Ekaterina Frantsuzova
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
| | - Alexander Bogun
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
| | - Olga Kopylova
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
- Pushchino Branch of Federal State Budgetary Educational Institution of Higher Education “Russian Biotechnology University (ROSBIOTECH)”, 142290 Pushchino, Moscow Region, Russia
| | - Anna Vetrova
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
| | - Inna Solyanikova
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
- Regional Microbiological Center, Belgorod State University, 308015 Belgorod, Russia
| | - Rostislav Streletskii
- Laboratory of Ecological Soil Science, Faculty of Soil Science, Lomonosov Moscow State University, 119991 Moscow, Russia;
| | - Yanina Delegan
- Institute of Biochemistry and Physiology of Microorganisms, Federal Research Center “Pushchino Scientific Center for Biological Research of Russian Academy of Sciences” (FRC PSCBR RAS), 142290 Pushchino, Moscow Region, Russia; (E.F.); (A.B.); (O.K.); (A.V.); (I.S.)
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2
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Zheng L, Shen J, Chen R, Hu Y, Zhao W, Leung ELH, Dai L. Genome engineering of the human gut microbiome. J Genet Genomics 2024; 51:479-491. [PMID: 38218395 DOI: 10.1016/j.jgg.2024.01.002] [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: 10/08/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/15/2024]
Abstract
The human gut microbiome, a complex ecosystem, significantly influences host health, impacting crucial aspects such as metabolism and immunity. To enhance our comprehension and control of the molecular mechanisms orchestrating the intricate interplay between gut commensal bacteria and human health, the exploration of genome engineering for gut microbes is a promising frontier. Nevertheless, the complexities and diversities inherent in the gut microbiome pose substantial challenges to the development of effective genome engineering tools for human gut microbes. In this comprehensive review, we provide an overview of the current progress and challenges in genome engineering of human gut commensal bacteria, whether executed in vitro or in situ. A specific focus is directed towards the advancements and prospects in cargo DNA delivery and high-throughput techniques. Additionally, we elucidate the immense potential of genome engineering methods to enhance our understanding of the human gut microbiome and engineer the microorganisms to enhance human health.
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Affiliation(s)
- Linggang Zheng
- Dr Neher's Biophysics Laboratory for Innovative Drug Discovery/State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China; CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Juntao Shen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ruiyue Chen
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yucan Hu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Zhao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Elaine Lai-Han Leung
- Cancer Center, Faculty of Health Science, University of Macau, Macau 999078, China; MOE Frontiers Science Center for Precision Oncology, University of Macau, Macau 999078, China.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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3
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Gupta S, Pal D. Detection of intrinsic transcription termination sites in bacteria: consensus from hairpin detection approaches. J Biomol Struct Dyn 2024:1-11. [PMID: 38605579 DOI: 10.1080/07391102.2024.2325107] [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: 11/19/2023] [Accepted: 02/23/2024] [Indexed: 04/13/2024]
Abstract
We compare the WebGeSTer and INtrinsic transcription TERmination hairPIN (INTERPIN) databases used for intrinsic transcription termination (ITT) site prediction in bacteria. The former deploys inverted nucleotide repeat detection for identification of RNA hairpin, while the latter a pair-potential function - the hairpin energy score evaluation being identical for both. We find INTERPIN more sensitive than WebGeSTer with about 6% and 51% additional predictions for ITTs in chromosomal and plasmid operons, respectively. INTERPIN hairpins are relatively shorter in length with ungapped stem, and even located in AT-rich segments, compared to GC-rich longer hairpins with a gapped stem in WebGeSTer. The GC%, length, and energy score from INTERPIN transcription units (TUs) are best inter-correlated while the lowest energy single hairpins from WebGeSTer, considered suitable for ITT, being the worst. Around 72% TUs from the two databases overlap, and ∼60% of all alternate ITT sites downstream of TUs overlap, of which 65% are cluster hairpins. This helps highlight hairpin features that can be used to identify termination sites in bacteria across different prediction methods. Overall, the pair-potential-function-based hairpins screened appear to be more consistent with the kinetic and thermodynamics processes of ITT known to date.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Swati Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
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4
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Nikolaev A, Kuzmin A, Markeeva E, Kuznetsova E, Ryzhykau YL, Semenov O, Anuchina A, Remeeva A, Gushchin I. Reengineering of a flavin-binding fluorescent protein using ProteinMPNN. Protein Sci 2024; 33:e4958. [PMID: 38501498 PMCID: PMC10949330 DOI: 10.1002/pro.4958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/12/2024] [Accepted: 02/18/2024] [Indexed: 03/20/2024]
Abstract
Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.
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Affiliation(s)
- Andrey Nikolaev
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Alexander Kuzmin
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Elena Markeeva
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Elizaveta Kuznetsova
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Yury L. Ryzhykau
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
- Frank Laboratory of Neutron PhysicsJoint Institute for Nuclear ResearchDubnaRussia
| | - Oleg Semenov
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Arina Anuchina
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Alina Remeeva
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
| | - Ivan Gushchin
- Research Center for Molecular Mechanisms of Aging and Age‐Related DiseasesMoscow Institute of Physics and TechnologyDolgoprudnyRussia
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5
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Bertsova YV, Serebryakova MV, Bogachev VA, Baykov AA, Bogachev AV. Acrylate Reductase of an Anaerobic Electron Transport Chain of the Marine Bacterium Shewanella woodyi. BIOCHEMISTRY. BIOKHIMIIA 2024; 89:701-710. [PMID: 38831506 DOI: 10.1134/s0006297924040096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 06/05/2024]
Abstract
Many microorganisms are capable of anaerobic respiration in the absence of oxygen, by using different organic compounds as terminal acceptors in electron transport chain. We identify here an anaerobic respiratory chain protein responsible for acrylate reduction in the marine bacterium Shewanella woodyi. When the periplasmic proteins of S. woodyi were separated by ion exchange chromatography, acrylate reductase activity copurified with an ArdA protein (Swoo_0275). Heterologous expression of S. woodyi ardA gene (swoo_0275) in Shewanella oneidensis MR-1 cells did not result in the appearance in them of periplasmic acrylate reductase activity, but such activity was detected when the ardA gene was co-expressed with an ardB gene (swoo_0276). Together, these genes encode flavocytochrome c ArdAB, which is thus responsible for acrylate reduction in S. woodyi cells. ArdAB was highly specific for acrylate as substrate and reduced only methacrylate (at a 22-fold lower rate) among a series of other tested 2-enoates. In line with these findings, acrylate and methacrylate induced ardA gene expression in S. woodyi under anaerobic conditions, which was accompanied by the appearance of periplasmic acrylate reductase activity. ArdAB-linked acrylate reduction supports dimethylsulfoniopropionate-dependent anaerobic respiration in S. woodyi and, possibly, other marine bacteria.
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Affiliation(s)
- Yulia V Bertsova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Marina V Serebryakova
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Vladimir A Bogachev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Alexander A Baykov
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234, Russia
| | - Alexander V Bogachev
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234, Russia.
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6
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Gupta S, Padmashali N, Pal D. INTERPIN: A repository for intrinsic transcription termination hairpins in bacteria. Biochimie 2023; 214:228-236. [PMID: 37499897 DOI: 10.1016/j.biochi.2023.07.018] [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: 03/09/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
The large-scale detection of putative intrinsic transcription terminators is limited to only a few bacteria currently. We discovered a group of hairpins, called cluster hairpins, present within 15 nucleotides from each other. These are expected to work in tandem to cause intrinsic transcription termination (ITT), while the single hairpin can do the same alone. Therefore, exploring these ITT sites and the hairpins across bacterial genomes becomes highly desirable. INTERPIN is the largest archived collection of in silico inferred ITT hairpins in bacteria, covering 12745 bacterial genomes and encompassing ten bacterial phyla for ∼25 million hairpins. Users can obtain details on operons, individual cluster, and single ITT hairpins that were screened therein. Integrated Genome Viewer (IGV) software interactively visualizes hairpin secondary and tertiary structures in the genomic context. We also discuss statistics for the occurrence of cluster or single hairpins and other termination alternatives while showing the validation of predicted hairpins against in vivo detected hairpins. The database is freely available at http://pallab.cds.iisc.ac.in/INTERPIN/. INTERPIN (database and software) can make predictions for both AT and GC-rich genomes, which has not been achieved by any other program so far. It can also be used to improve genome annotation as well as to get predictions to improve the understanding of the ITT pathway by further analysis.
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Affiliation(s)
- Swati Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India
| | - Namrata Padmashali
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, Karnataka, India.
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7
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Charoenkwan P, Waramit S, Chumnanpuen P, Schaduangrat N, Shoombuatong W. TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus. PLoS One 2023; 18:e0290538. [PMID: 37624802 PMCID: PMC10456195 DOI: 10.1371/journal.pone.0290538] [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: 06/07/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand
| | - Sajee Waramit
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand
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8
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Liu S, Liang Y, Li J, Yang S, Liu M, Liu C, Yang D, Zuo Y. Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction. Int J Biol Macromol 2023:124993. [PMID: 37307968 DOI: 10.1016/j.ijbiomac.2023.124993] [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: 03/28/2023] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/14/2023]
Abstract
Copper ion-binding proteins play an essential role in metabolic processes and are critical factors in many diseases, such as breast cancer, lung cancer, and Menkes disease. Many algorithms have been developed for predicting metal ion classification and binding sites, but none have been applied to copper ion-binding proteins. In this study, we developed a copper ion-bound protein classifier, RPCIBP, which integrating the reduced amino acid composition into position-specific score matrix (PSSM). The reduced amino acid composition filters out a large number of useless evolutionary features, improving the operational efficiency and predictive ability of the model (feature dimension from 2900 to 200, ACC from 83 % to 85.1 %). Compared with the basic model using only three sequence feature extraction methods (ACC in training set between 73.8 %-86.2 %, ACC in test set between 69.3 %-87.5 %), the model integrating the evolutionary features of the reduced amino acid composition showed higher accuracy and robustness (ACC in training set between 83.1 %-90.8 %, ACC in test set between 79.1 %-91.9 %). Best copper ion-binding protein classifiers filtered by feature selection progress were deployed in a user-friendly web server (http://bioinfor.imu.edu.cn/RPCIBP). RPCIBP can accurately predict copper ion-binding proteins, which is convenient for further structural and functional studies, and conducive to mechanism exploration and target drug development.
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Affiliation(s)
- Shanghua Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China
| | - Yuchao Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot 010010, China
| | - Jinzhao Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Siqi Yang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Ming Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Chengfang Liu
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China
| | - Dezhi Yang
- Inner Mongolia International Mongolian Hospital, Hohhot 010065, China.
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, School of Life Sciences, Inner Mongolia University, Hohhot 010021, China; Inner Mongolia International Mongolian Hospital, Hohhot 010065, China; Digital College, Inner Mongolia Intelligent Union Big Data Academy, Hohhot 010010, China.
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9
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Yang S, Yang Z, Yang J. 4mCBERT: A computing tool for the identification of DNA N4-methylcytosine sites by sequence- and chemical-derived information based on ensemble learning strategies. Int J Biol Macromol 2023; 231:123180. [PMID: 36646347 DOI: 10.1016/j.ijbiomac.2023.123180] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/26/2022] [Accepted: 12/30/2022] [Indexed: 01/15/2023]
Abstract
N4-methylcytosine (4mC) is an important DNA chemical modification pattern which is a new methylation modification discovered in recent years and plays critical roles in gene expression regulation, defense against invading genetic elements, genomic imprinting, and so on. Identifying 4mC site from DNA sequence segment contributes to discovering more novel modification patterns. In this paper, we present a model called 4mCBERT that encodes DNA sequence segments by sequence characteristics including one-hot, electron-ion interaction pseudopotential, nucleotide chemical property, word2vec and chemical information containing physicochemical properties (PCP), chemical bidirectional encoder representations from transformers (chemical BERT) and employs ensemble learning framework to develop a prediction model. PCP and chemical BERT features are firstly constructed and applied to predict 4mC sites and show positive contributions to identifying 4mC. For the Matthew's Correlation Coefficient, 4mCBERT significantly outperformed other state-of-the-art models on six independent benchmark datasets including A. thaliana, C. elegans, D. melanogaster, E. coli, G. Pickering, and G. subterraneous by 4.32 % to 24.39 %, 2.52 % to 31.65 %, 2 % to 16.49 %, 6.63 % to 35.15, 8.59 % to 61.85 %, and 8.45 % to 34.45 %. Moreover, 4mCBERT is designed to allow users to predict 4mC sites and retrain 4mC prediction models. In brief, 4mCBERT shows higher performance on six benchmark datasets by incorporating sequence- and chemical-driven information and is available at http://cczubio.top/4mCBERT and https://github.com/abcair/4mCBERT.
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Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China; The Affiliated Changzhou No 2 People's Hospital of Nanjing Medical University, Changzhou 213164, China.
| | - Zexi Yang
- School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou 213164, China
| | - Jun Yang
- School of Educational Sciences, Yili Normal University, Yining 835000, China
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10
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Zhu D, Yang W, Xu D, Li H, Zhao Y, Li D. A deep learning based two-layer predictor to identify enhancers and their strength. Methods 2023; 211:23-30. [PMID: 36740001 DOI: 10.1016/j.ymeth.2023.01.007] [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: 11/19/2022] [Revised: 01/03/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The enhancer is a DNA sequence that can increase the activity of promoters and thus speed up the frequency of gene transcription. The enhancer plays an essential role in activating gene expression. Currently, gene sequencing technology has been developed for 30 years from the first generation to the third generation, and a variety of biological sequence data have increased significantly every year. Due to the importance of enhancer functions, it is very expensive to identify enhancers through biochemical experiments. Therefore, we need to study new methods for the identification and classification of enhancers. Based on the K-mer principle this study proposed a feature extraction method that others have not used in convolutional neural networks. Then, we combined it with one-hot encoding to build an efficient one-dimensional convolutional neural network ensemble model for predicting enhancers and their strengths. Finally, we used five commonly used classification problem evaluation indicators to compare with the models proposed by other researchers. The model proposed in this paper has a better performance by using the same independent test dataset as other models.
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Affiliation(s)
- Di Zhu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Dali Xu
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Hongfei Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Yuming Zhao
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
| | - Dan Li
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
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11
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Zulfiqar H, Guo Z, Grace-Mercure BK, Zhang ZY, Gao H, Lin H, Wu Y. Empirical Comparison and Recent Advances of Computational Prediction of Hormone Binding Proteins Using Machine Learning Methods. Comput Struct Biotechnol J 2023; 21:2253-2261. [PMID: 37035551 PMCID: PMC10073991 DOI: 10.1016/j.csbj.2023.03.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/19/2023] Open
Abstract
Hormone binding proteins (HBPs) belong to the group of soluble carrier proteins. These proteins selectively and non-covalently interact with hormones and promote growth hormone signaling in human and other animals. The HBPs are useful in many medical and commercial fields. Thus, the identification of HBPs is very important because it can help to discover more details about hormone binding proteins. Meanwhile, the experimental methods are time-consuming and expensive for hormone binding proteins recognition. Computational prediction methods have played significant roles in the correct recognition of hormone binding proteins with the use of sequence information and ML algorithms. In this review, we compared and assessed the implementation of ML-based tools in recognition of HBPs in a unique way. We hope that this study will give enough awareness and knowledge for research on HBPs.
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Su W, Xie XQ, Liu XW, Gao D, Ma CY, Zulfiqar H, Yang H, Lin H, Yu XL, Li YW. iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. Int J Biol Macromol 2023; 227:1174-1181. [PMID: 36470433 DOI: 10.1016/j.ijbiomac.2022.11.299] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/10/2022] [Accepted: 11/25/2022] [Indexed: 12/07/2022]
Abstract
RNA N4-acetylcytidine (ac4C) is the acetylation of cytidine at the nitrogen-4 position, which is a highly conserved RNA modification and involves a variety of biological processes. Hence, accurate identification of genome-wide ac4C sites is vital for understanding regulation mechanism of gene expression. In this work, a novel predictor, named iRNA-ac4C, was established to identify ac4C sites in human mRNA based on three feature extraction methods, including nucleotide composition, nucleotide chemical property, and accumulated nucleotide frequency. Subsequently, minimum-Redundancy-Maximum-Relevance combined with incremental feature selection strategies was utilized to select the optimal feature subset. According to the optimal feature subset, the best ac4C classification model was trained by gradient boosting decision tree with 10-fold cross-validation. The results of independent testing set indicated that our proposed method could produce encouraging generalization capabilities. For the convenience of other researchers, we established a user-friendly web server which is freely available at http://lin-group.cn/server/iRNA-ac4C/. We hope that the tool could provide guide for wet-experimental scholars.
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Affiliation(s)
- Wei Su
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xue-Qin Xie
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Wei Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Gao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Cai-Yi Ma
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hasan Zulfiqar
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hui Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hao Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, China.
| | - Yan-Wen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; Key Laboratory of Intelligent Information Processing of Jilin Province, Northeast Normal University, Changchun 130117, China; Institute of Computational Biology, Northeast Normal University, Changchun 130117, China.
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Patra P, B R D, Kundu P, Das M, Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol Adv 2023; 62:108069. [PMID: 36442697 DOI: 10.1016/j.biotechadv.2022.108069] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 10/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.
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Affiliation(s)
- Pradipta Patra
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Disha B R
- B.M.S College of Engineering, Basavanagudi, Bengaluru, Karnataka 560019, India
| | - Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Manali Das
- School of Bioscience, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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14
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Functional Characterization and Structural Modeling of a Novel Glycine Oxidase from Variovorax paradoxus Iso1. Appl Environ Microbiol 2022; 88:e0107722. [PMID: 36377957 PMCID: PMC9746326 DOI: 10.1128/aem.01077-22] [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: 11/16/2022] Open
Abstract
The N-acyl-d-amino acid amidohydrolase (N-d-AAase) of Variovorax paradoxus Iso1 can enantioselectively catalyze the zinc-assisted deacetylation of N-acyl-d-amino acids to yield consistent d-amino acids. A putative FAD-binding glycine/d-amino acid oxidase was located immediately upstream of the N-d-AAase gene. The gene encoding this protein was cloned into Escherichia coli BL21 (DE3)pLysS and overexpressed at 25°C for 6 h with 0.5 mM isopropyl β-d-1-thiogalactopyranoside induction. After purification, the tag-free recombinant protein was obtained. The enzyme could metabolize glycine, sarcosine, and d-alanine, but not l-amino acids or bulky d-amino acids. Protein modeling further supported these results, demonstrating that glycine, sarcosine, and d-alanine could fit into the pocket of the enzyme's activation site, while l-alanine and d-threonine were out of position. Therefore, this protein was proposed as a glycine oxidase, and we designated it VpGO. Interestingly, VpGO showed low sequence similarity to other well-characterized glycine oxidases. We found that VpGO and N-d-AAase were expressed on the same mRNA and could be transcriptionally induced by various N-acetyl-d-amino acids. Western blotting and zymography showed that both proteins had similar expression patterns in response to different types of inducers. Thus, we have identified a novel glycine oxidase that is co-regulated with N-d-AAase in an operon, and metabolizes N-acyl-d-amino acids in the metabolically versatile V. paradoxus Iso1. IMPORTANCE The Gram-negative bacterium Variovorax paradoxus has numerous metabolic capabilities, including the association with important catabolic processes and the promotion of plant growth. We had previously identified and characterized an N-acyl-d-amino-acid amidohydrolase (N-d-AAase) gene from the strain of V. paradoxus Iso1. The aim of this study was to isolate and characterize (both in vitro and in vivo) another potential gene found in the promoter region of this N-d-AAase gene. The protein was identified as a glycine oxidase, and the gene existed in an operon with N-d-AAase. The structural basis for its FAD-binding potential and substrate stereo-specificity were also elucidated. This study first reported a novel glycine oxidase from V. paradoxus. We believe that our study makes a significant contribution to the literature, because this enzyme has great potential for use as an industrial catalysis, as a biosensor, and in agricultural biotechnology.
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Suleman MT, Khan YD. m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence. Comb Chem High Throughput Screen 2022; 25:2473-2484. [PMID: 35718969 DOI: 10.2174/1386207325666220617152743] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND The process of nucleotides modification or methyl groups addition to nucleotides is known as post-transcriptional modification (PTM). 1-methyladenosine (m1A) is a type of PTM formed by adding a methyl group to the nitrogen at the 1st position of the adenosine base. Many human disorders are associated with m1A, which is widely found in ribosomal RNA and transfer RNA. OBJECTIVE The conventional methods such as mass spectrometry and site-directed mutagenesis proved to be laborious and burdensome. Systematic identification of modified sites from RNA sequences is gaining much attention nowadays. Consequently, an extreme gradient boost predictor, m1A-Pred, is developed in this study for the prediction of modified m1A sites. METHODS The current study involves the extraction of position and composition-based properties within nucleotide sequences. The extraction of features helps in the development of the features vector. Statistical moments were endorsed for dimensionality reduction in the obtained features. RESULTS Through a series of experiments using different computational models and evaluation methods, it was revealed that the proposed predictor, m1A-pred, proved to be the most robust and accurate model for the identification of modified sites. AVAILABILITY AND IMPLEMENTATION To enhance the research on m1A sites, a friendly server was also developed, which was the final phase of this research.
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Affiliation(s)
- Muhammad Taseer Suleman
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
| | - Yaser Daanial Khan
- Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
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Fernández MB, Latorre L, Correa-Aragunde N, Cassia R. A putative bifunctional CPD/ (6-4) photolyase from the cyanobacteria Synechococcus sp. PCC 7335 is encoded by a UV-B inducible operon: New insights into the evolution of photolyases. Front Microbiol 2022; 13:981788. [DOI: 10.3389/fmicb.2022.981788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Photosynthetic organisms are continuously exposed to solar ultraviolet radiation-B (UV-B) because of their autotrophic lifestyle. UV-B provokes DNA damage, such as cyclobutane pyrimidine dimers (CPD) or pyrimidine (6-4) pyrimidone photoproducts (6-4 PPs). The cryptochrome/photolyase family (CPF) comprises flavoproteins that can bind damaged or undamaged DNA. Photolyases (PHRs) are enzymes that repair either CPDs or 6-4 PPs. A natural bifunctional CPD/(6-4)- PHR (PhrSph98) was recently isolated from the UV-resistant bacteria Sphingomonas sp. UV9. In this work, phylogenetic studies of bifunctional CPD/(6-4)- photolyases and their evolutionary relationship with other CPF members were performed. Amino acids involved in electron transfer and binding to FAD cofactor and DNA lesions were conserved in proteins from proteobacteria, planctomycete, bacteroidete, acidobacteria and cyanobacteria clades. Genome analysis revealed that the cyanobacteria Synechococcus sp. PCC 7335 encodes a two-gene assembly operon coding for a PHR and a bifunctional CPD/(6-4) PHR- like. Operon structure was validated by RT-qPCR analysis and the polycistronic transcript accumulated after 15 min of UV-B irradiation. Conservation of structure and evolution is discussed. This study provides evidence for a UV-B inducible PHR operon that encodes a CPD/(6-4)- photolyase homolog with a putative bifunctional role in the repair of CPDs and 6-4 PPs damages in oxygenic photosynthetic organisms.
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17
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Cho SG, Song M, Chuon K, Shim JG, Meas S, Jung KH. Heliorhodopsin binds and regulates glutamine synthetase activity. PLoS Biol 2022; 20:e3001817. [PMID: 36190943 PMCID: PMC9529153 DOI: 10.1371/journal.pbio.3001817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 09/06/2022] [Indexed: 11/05/2022] Open
Abstract
Photoreceptors are light-sensitive proteins found in various organisms that respond to light and relay signals into the cells. Heliorhodopsin, a retinal-binding membrane protein, has been recently discovered, however its function remains unknown. Herein, we investigated the relationship between Actinobacteria bacterium IMCC26103 heliorhodopsin (AbHeR) and an adjacent glutamine synthetase (AbGS) in the same operon. We demonstrate that AbHeR binds to AbGS and regulates AbGS activity. More specifically, the dissociation constant (Kd) value of the binding between AbHeR and AbGS is 6.06 μM. Moreover, the absence of positively charged residues within the intracellular loop of AbHeR impacted Kd value as they serve as critical binding sites for AbGS. We also confirm that AbHeR up-regulates the biosynthetic enzyme activity of AbGS both in vitro and in vivo in the presence of light. GS is a key enzyme involved in nitrogen assimilation that catalyzes the conversion of glutamate and ammonia to glutamine. Hence, the interaction between AbHeR and AbGS may be critical for nitrogen assimilation in Actinobacteria bacterium IMCC26103 as it survives in low-nutrient environments. Overall, the findings of our study describe, for the first time, to the best of our knowledge, a novel function of heliorhodopsin as a regulatory rhodopsin with the capacity to bind and regulate enzyme activity required for nitrogen assimilation. A study of heliorhodopsin, an actinobacterial photoreceptor of unknown function, reveals that it interacts with glutamine synthetase, an enzyme involved in nitrogen assimilation, and regulates its activity in the presence of light, highlighting the diverse functions of rhodopsins in different organisms.
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Affiliation(s)
- Shin-Gyu Cho
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea,Research Institute for Basic Science, Sogang University, Seoul, Korea
| | - Myungchul Song
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea
| | - Kimleng Chuon
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea
| | - Jin-gon Shim
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea
| | - Seanghun Meas
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea,Department of Biology, Faculty of Science, Royal University of Phnom Penh, Phnom Penh, Cambodia
| | - Kwang-Hwan Jung
- Department of Life Science and Institute of Biological Interfaces, Sogang University, Seoul, Korea,* E-mail:
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18
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Fan Y, Peng B. StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning. BMC Bioinformatics 2022; 23:272. [PMID: 35820811 PMCID: PMC9277947 DOI: 10.1186/s12859-022-04821-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/01/2022] [Indexed: 11/10/2022] Open
Abstract
Background Understanding the regulatory role of enhancer–promoter interactions (EPIs) on specific gene expression in cells contributes to the understanding of gene regulation, cell differentiation, etc., and its identification has been a challenging task. On the one hand, using traditional wet experimental methods to identify EPIs often means a lot of human labor and time costs. On the other hand, although the currently proposed computational methods have good recognition effects, they generally require a long training time. Results In this study, we studied the EPIs of six human cell lines and designed a cell line-specific EPIs prediction method based on a stacking ensemble learning strategy, which has better prediction performance and faster training speed, called StackEPI. Specifically, by combining different encoding schemes and machine learning methods, our prediction method can extract the cell line-specific effective information of enhancer and promoter gene sequences comprehensively and in many directions, and make accurate recognition of cell line-specific EPIs. Ultimately, the source code to implement StackEPI and experimental data involved in the experiment are available at https://github.com/20032303092/StackEPI.git. Conclusions The comparison results show that our model can deliver better performance on the problem of identifying cell line-specific EPIs and outperform other state-of-the-art models. In addition, our model also has a more efficient computation speed. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04821-9.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Binchao Peng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
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Shi XC, Tremblay PL, Xue M, Song X, Zhang T. Fumarate disproportionation by Geobacter sulfurreducens and its involvement in biocorrosion and interspecies electron transfer. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 827:154251. [PMID: 35245554 DOI: 10.1016/j.scitotenv.2022.154251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/14/2022] [Accepted: 02/26/2022] [Indexed: 06/14/2023]
Abstract
The model electroactive bacterium Geobacter sulfurreducens can acquire electrons directly from solid donors including metals and other species. Reports on this physiology concluding that solid donors are the only electron sources were conducted with fumarate believed to serve exclusively as the terminal electron acceptor (TEA). Here, G. sulfurreducens was repeatedly transferred for adaptation within a growth medium containing only fumarate and no other solid or soluble substrate. The resulting evolved strain grew efficiently with either the C4-dicarboxylate fumarate or malate acting simultaneously as electron donor, carbon source, and electron acceptor via disproportionation. Whole-genome sequencing identified 38 mutations including one in the regulator PilR known to repress the expression of the C4-dicarboxylate antiporter DcuB essential to G. sulfurreducens when growing with fumarate. Futhermore, the PilR mutation was identical to the sole mutation previously reported in an evolved G. sulfurreducens grown in a co-culture assumed to derive energy solely from direct interspecies electron transfer, but cultivated with fumarate as the TEA. When cultivating the fumarate-adapted strain in the presence of stainless steel and fumarate, biocorrosion was observed and bacterial growth was accelerated 2.3 times. These results suggest that G. sulfurreducens can conserve energy concomitantly from C4-dicarboxylate disproportionation and the oxidation of a solid electron donor. This co-metabolic capacity confers an advantage to Geobacter for survival and colonization and explains in part why these microbes are omnipresent in different anaerobic ecosystems.
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Affiliation(s)
- Xiao-Chen Shi
- School of Chemistry, Chemical Engineering, and Life Science, Wuhan University of Technology, Wuhan 430070, PR China; School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, PR China; Wuhan University of Technology Advanced Engineering Technology Research Institute of Zhongshan City, Zhongshan 528437, PR China
| | - Pier-Luc Tremblay
- School of Chemistry, Chemical Engineering, and Life Science, Wuhan University of Technology, Wuhan 430070, PR China; State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Wuhan 430070, PR China; Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572024, PR China
| | - Miao Xue
- Institut WUT-AMU, Wuhan University of Technology, Wuhan 430070, PR China
| | - Xinyi Song
- Institut WUT-AMU, Wuhan University of Technology, Wuhan 430070, PR China
| | - Tian Zhang
- School of Chemistry, Chemical Engineering, and Life Science, Wuhan University of Technology, Wuhan 430070, PR China; School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, PR China; Institut WUT-AMU, Wuhan University of Technology, Wuhan 430070, PR China; State Key Laboratory of Silicate Materials for Architectures, Wuhan University of Technology, Wuhan 430070, PR China; Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572024, PR China.
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Charoenkwan P, Schaduangrat N, Lio' P, Moni MA, Manavalan B, Shoombuatong W. NEPTUNE: A novel computational approach for accurate and large-scale identification of tumor homing peptides. Comput Biol Med 2022; 148:105700. [PMID: 35715261 DOI: 10.1016/j.compbiomed.2022.105700] [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: 04/14/2022] [Revised: 05/31/2022] [Accepted: 06/04/2022] [Indexed: 11/16/2022]
Abstract
Tumor homing peptides (THPs) play a crucial role in recognizing and specifically binding to cancer cells. Although experimental approaches can facilitate the precise identification of THPs, they are usually time-consuming, labor-intensive, and not cost-effective. However, computational approaches can identify THPs by utilizing sequence information alone, thus highlighting their great potential for large-scale identification of THPs. Herein, we propose NEPTUNE, a novel computational approach for the accurate and large-scale identification of THPs from sequence information. Specifically, we constructed variant baseline models from multiple feature encoding schemes coupled with six popular machine learning algorithms. Subsequently, we comprehensively assessed and investigated the effects of these baseline models on THP prediction. Finally, the probabilistic information generated by the optimal baseline models is fed into a support vector machine-based classifier to construct the final meta-predictor (NEPTUNE). Cross-validation and independent tests demonstrated that NEPTUNE achieved superior performance for THP prediction compared with its constituent baseline models and the existing methods. Moreover, we employed the powerful SHapley additive exPlanations method to improve the interpretation of NEPTUNE and elucidate the most important features for identifying THPs. Finally, we implemented an online web server using NEPTUNE, which is available at http://pmlabstack.pythonanywhere.com/NEPTUNE. NEPTUNE could be beneficial for the large-scale identification of unknown THP candidates for follow-up experimental validation.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Mohammad Ali Moni
- Artificial Intelligence & Digital Health, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, QLD, 4072, Australia
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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Charoenkwan P, Schaduangrat N, Hasan MM, Moni MA, Lió P, Shoombuatong W. Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins. EXCLI JOURNAL 2022; 21:554-570. [PMID: 35651661 PMCID: PMC9150013 DOI: 10.17179/excli2022-4723] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/21/2022] [Indexed: 12/15/2022]
Abstract
Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand, 50200
| | - Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, the University of Queensland, St Lucia, QLD 4072, Australia
| | - Pietro Lió
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
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22
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Charoenkwan P, Ahmed S, Nantasenamat C, Quinn JMW, Moni MA, Lio' P, Shoombuatong W. AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning. Sci Rep 2022; 12:7697. [PMID: 35546347 PMCID: PMC9095707 DOI: 10.1038/s41598-022-11897-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 05/03/2022] [Indexed: 12/13/2022] Open
Abstract
Amyloid proteins have the ability to form insoluble fibril aggregates that have important pathogenic effects in many tissues. Such amyloidoses are prominently associated with common diseases such as type 2 diabetes, Alzheimer's disease, and Parkinson's disease. There are many types of amyloid proteins, and some proteins that form amyloid aggregates when in a misfolded state. It is difficult to identify such amyloid proteins and their pathogenic properties, but a new and effective approach is by developing effective bioinformatics tools. While several machine learning (ML)-based models for in silico identification of amyloid proteins have been proposed, their predictive performance is limited. In this study, we present AMYPred-FRL, a novel meta-predictor that uses a feature representation learning approach to achieve more accurate amyloid protein identification. AMYPred-FRL combined six well-known ML algorithms (extremely randomized tree, extreme gradient boosting, k-nearest neighbor, logistic regression, random forest, and support vector machine) with ten different sequence-based feature descriptors to generate 60 probabilistic features (PFs), as opposed to state-of-the-art methods developed by a single feature-based approach. A logistic regression recursive feature elimination (LR-RFE) method was used to find the optimal m number of 60 PFs in order to improve the predictive performance. Finally, using the meta-predictor approach, the 20 selected PFs were fed into a logistic regression method to create the final hybrid model (AMYPred-FRL). Both cross-validation and independent tests showed that AMYPred-FRL achieved superior predictive performance than its constituent baseline models. In an extensive independent test, AMYPred-FRL outperformed the existing methods by 5.5% and 16.1%, respectively, with accuracy and MCC of 0.873 and 0.710. To expedite high-throughput prediction, a user-friendly web server of AMYPred-FRL is freely available at http://pmlabstack.pythonanywhere.com/AMYPred-FRL. It is anticipated that AMYPred-FRL will be a useful tool in helping researchers to identify new amyloid proteins.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Saeed Ahmed
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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23
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He Z, Xu J, Shi H, Wu S. m5CRegpred: Epitranscriptome Target Prediction of 5-Methylcytosine (m5C) Regulators Based on Sequencing Features. Genes (Basel) 2022; 13:genes13040677. [PMID: 35456483 PMCID: PMC9025882 DOI: 10.3390/genes13040677] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/02/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023] Open
Abstract
5-methylcytosine (m5C) is a common post-transcriptional modification observed in a variety of RNAs. m5C has been demonstrated to be important in a variety of biological processes, including RNA structural stability and metabolism. Driven by the importance of m5C modification, many projects focused on the m5C sites prediction were reported before. To better understand the upstream and downstream regulation of m5C, we present a bioinformatics framework, m5CRegpred, to predict the substrate of m5C writer NSUN2 and m5C readers YBX1 and ALYREF for the first time. After features comparison, window lengths selection and algorism comparison on the mature mRNA model, our model achieved AUROC scores 0.869, 0.724 and 0.889 for NSUN2, YBX1 and ALYREF, respectively in an independent test. Our work suggests the substrate of m5C regulators can be distinguished and may help the research of m5C regulators in a special condition, such as substrates prediction of hyper- or hypo-expressed m5C regulators in human disease.
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Affiliation(s)
- Zhizhou He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
- Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Jing Xu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
| | - Haoran Shi
- Research Center for BioSystems, Land Use, and Nutrition (IFZ), Institute of Applied Microbiology, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
- Correspondence: (H.S.); (S.W.)
| | - Shuxiang Wu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China
- Correspondence: (H.S.); (S.W.)
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24
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Ahmad S, Charoenkwan P, Quinn JMW, Moni MA, Hasan MM, Lio' P, Shoombuatong W. SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins. Sci Rep 2022; 12:4106. [PMID: 35260777 PMCID: PMC8904530 DOI: 10.1038/s41598-022-08173-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 03/03/2022] [Indexed: 12/30/2022] Open
Abstract
Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).
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Affiliation(s)
- Saeed Ahmad
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Julian M W Quinn
- Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia
| | - Mohammad Ali Moni
- Faculty of Health and Behavioural Sciences, School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Md Mehedi Hasan
- Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, 70112, USA
| | - Pietro Lio'
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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25
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Chen Z, Jiao S, Zhao D, Zou Q, Xu L, Zhang L, Su X. The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning. Front Cell Dev Biol 2022; 10:845622. [PMID: 35178393 PMCID: PMC8844512 DOI: 10.3389/fcell.2022.845622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 01/17/2022] [Indexed: 11/21/2022] Open
Abstract
Recurrence and new cases of cancer constitute a challenging human health problem. Aquaporins (AQPs) can be expressed in many types of tumours, including the brain, breast, pancreas, colon, skin, ovaries, and lungs, and the histological grade of cancer is positively correlated with AQP expression. Therefore, the identification of aquaporins is an area to explore. Computational tools play an important role in aquaporin identification. In this research, we propose reliable, accurate and automated sequence predictor iAQPs-RF to identify AQPs. In this study, the feature extraction method was 188D (global protein sequence descriptor, GPSD). Six common classifiers, including random forest (RF), NaiveBayes (NB), support vector machine (SVM), XGBoost, logistic regression (LR) and decision tree (DT), were used for AQP classification. The classification results show that the random forest (RF) algorithm is the most suitable machine learning algorithm, and the accuracy was 97.689%. Analysis of Variance (ANOVA) was used to analyse these characteristics. Feature rank based on the ANOVA method and IFS strategy was applied to search for the optimal features. The classification results suggest that the 26th feature (neutral/hydrophobic) and 21st feature (hydrophobic) are the two most powerful and informative features that distinguish AQPs from non-AQPs. Previous studies reported that plasma membrane proteins have hydrophobic characteristics. Aquaporin subcellular localization prediction showed that all aquaporins were plasma membrane proteins with highly conserved transmembrane structures. In addition, the 3D structure of aquaporins was consistent with the localization results. Therefore, these studies confirmed that aquaporins possess hydrophobic properties. Although aquaporins are highly conserved transmembrane structures, the phylogenetic tree shows the diversity of aquaporins during evolution. The PCA showed that positive and negative samples were well separated by 54D features, indicating that the 54D feature can effectively classify aquaporins. The online prediction server is accessible at http://lab.malab.cn/∼acy/iAQP.
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Affiliation(s)
- Zheng Chen
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Da Zhao
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lijun Zhang
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
| | - Xi Su
- Foshan Maternal and Child Health Hospital, Foshan, China
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26
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Kabir M, Nantasenamat C, Kanthawong S, Charoenkwan P, Shoombuatong W. Large-scale comparative review and assessment of computational methods for phage virion proteins identification. EXCLI JOURNAL 2022; 21:11-29. [PMID: 35145365 PMCID: PMC8822302 DOI: 10.17179/excli2021-4411] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 11/29/2021] [Indexed: 12/11/2022]
Abstract
Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the in silico identification of PVPs in conjunction with traditional experimental approaches. As a result, a revised and more thorough assessment is extremely desirable. With this purpose in mind, we first conduct a thorough survey and evaluation of a vast array of 13 state-of-the-art PVP predictors. Among these PVP predictors, they can be classified into three groups according to the types of machine learning (ML) algorithms employed (i.e. traditional ML-based methods, ensemble-based methods and deep learning-based methods). Subsequently, we explored which factors are important for building more accurate and stable predictors and this included training/independent datasets, feature encoding algorithms, feature selection methods, core algorithms, performance evaluation metrics/strategies and web servers. Finally, we provide insights and future perspectives for the design and development of new and more effective computational approaches for the detection and characterization of PVPs.
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Affiliation(s)
- Muhammad Kabir
- School of Systems and Technology, Department of Computer Science, University of Management and Technology, Lahore, Pakistan, 54770
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
| | - Sakawrat Kanthawong
- Department of Microbiology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand, 40002
| | - Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand, 50200
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand, 10700
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27
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Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:7493834. [PMID: 35069791 PMCID: PMC8769816 DOI: 10.1155/2022/7493834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 12/16/2021] [Indexed: 11/28/2022]
Abstract
Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.
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28
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Turner D, Adriaenssens EM, Tolstoy I, Kropinski AM. Phage Annotation Guide: Guidelines for Assembly and High-Quality Annotation. PHAGE (NEW ROCHELLE, N.Y.) 2021; 2:170-182. [PMID: 35083439 PMCID: PMC8785237 DOI: 10.1089/phage.2021.0013] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
All sequencing projects of bacteriophages (phages) should seek to report an accurate and comprehensive annotation of their genomes. This article defines 14 questions for those new to phage genomics that should be addressed before submitting a genome sequence to the International Nucleotide Sequence Database Collaboration or writing a publication.
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Affiliation(s)
- Dann Turner
- Department of Applied Sciences, Faculty of Health and Applied Sciences, University of the West of England, Bristol, United Kingdom
| | | | - Igor Tolstoy
- Viral Resources, National Center for Biotechnology Information, U.S. National Library of Medicine, Bethesda, Maryland, USA
| | - Andrew M Kropinski
- Department of Food Science, and University of Guelph, Guelph, Ontario, Canada.,Department of Pathobiology, University of Guelph, Guelph, Ontario, Canada
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29
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Lyu Y, Zhang Z, Li J, He W, Ding Y, Guo F. iEnhancer-KL: A Novel Two-Layer Predictor for Identifying Enhancers by Position Specific of Nucleotide Composition. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2809-2815. [PMID: 33481715 DOI: 10.1109/tcbb.2021.3053608] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
An enhancer is a short region of DNA with the ability to recruit transcription factors and their complexes, increasing the likelihood of the transcription of a particular gene. Considering the importance of enhancers, enhancer identification is a prevailing problem in computational biology. In this paper, we propose a novel two-layer enhancer predictor called iEnhancer-KL, using computational biology algorithms to identify enhancers and then classify these enhancers into strong or weak types. Kullback-Leibler (KL) divergence is creatively taken into consideration to improve the feature extraction method PSTNPss. Then, LASSO is used to reduce the dimension of features and finally helps to get better prediction performance. Furthermore, the selected features are tested on several machine learning models, and the SVM algorithm achieves the best performance. The rigorous cross-validation indicates that our predictor is remarkably superior to the existing state-of-the-art methods with an Acc of 84.23 percent and the MCC of 0.6849 for identifying enhancers. Our code and results can be freely downloaded from https://github.com/Not-so-middle/iEnhancer-KL.git.
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30
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Li J, He S, Guo F, Zou Q. HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching. RNA Biol 2021; 18:1882-1892. [PMID: 33446014 PMCID: PMC8583144 DOI: 10.1080/15476286.2021.1875180] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 12/02/2020] [Accepted: 01/08/2021] [Indexed: 01/21/2023] Open
Abstract
Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine (m 6 A ) based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.html The codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git.
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Affiliation(s)
- Jing Li
- Institute of computational biology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Shida He
- Institute of computational biology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Fei Guo
- Institute of computational biology, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Bioinformatics Laboratory, Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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31
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Fan Y, Wang W. Using multi-layer perceptron to identify origins of replication in eukaryotes via informative features. BMC Bioinformatics 2021; 22:516. [PMID: 34688247 PMCID: PMC8542328 DOI: 10.1186/s12859-021-04431-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 10/04/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The origin is the starting site of DNA replication, an extremely vital part of the informational inheritance between parents and children. More importantly, accurately identifying the origin of replication has great application value in the diagnosis and treatment of diseases related to genetic information errors, while the traditional biological experimental methods are time-consuming and laborious. RESULTS We carried out research on the origin of replication in a variety of eukaryotes and proposed a unique prediction method for each species. Throughout the experiment, we collected data from 7 species, including Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, Kluyveromyces lactis, Pichia pastoris and Schizosaccharomyces pombe. In addition to the commonly used sequence feature extraction methods PseKNC-II and Base-content, we designed a feature extraction method based on TF-IDF. Then the two-step method was utilized for feature selection. After comparing a variety of traditional machine learning classification models, the multi-layer perceptron was employed as the classification algorithm. Ultimately, the data and codes involved in the experiment are available at https://github.com/Sarahyouzi/EukOriginPredict . CONCLUSIONS The prediction accuracy of the training set of the above-mentioned seven species after 100 times fivefold cross validation reach 92.60%, 90.80%, 91.22%, 96.15%, 96.72%, 99.86%, 96.72%, respectively. It denotes that compared with other methods, the methods we designed could accomplish superior performance. In addition, our experiments reveals that the models of multiple species could predict each other with high accuracy, and the results of STREME shows that they have a certain common motif.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
| | - Wanru Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
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32
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Liu T, Chen J, Zhang Q, Hippe K, Hunt C, Le T, Cao R, Tang H. The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite. Curr Med Chem 2021; 29:807-821. [PMID: 34636289 DOI: 10.2174/0929867328666211005140625] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/28/2021] [Accepted: 08/15/2021] [Indexed: 11/22/2022]
Abstract
Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Jiamao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University. United States
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University. United States
| | - Thu Le
- Department of Computer Science, Pacific Lutheran University. United States
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University. United States
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou. China
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33
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Zhao YW, Zhang S, Ding H. Recent development of machine learning methods in sumoylation sites prediction. Curr Med Chem 2021; 29:894-907. [PMID: 34525906 DOI: 10.2174/0929867328666210915112030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/24/2021] [Accepted: 08/07/2021] [Indexed: 11/22/2022]
Abstract
Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.
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Affiliation(s)
- Yi-Wei Zhao
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054. China
| | - Shihua Zhang
- College of Life Science and Health, Wuhan University of Science and Technology, Wuhan 430065. China
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054. China
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34
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Enhanced Prodigiosin Production in Serratia marcescens JNB5-1 by Introduction of a Polynucleotide Fragment into the pigN 3' Untranslated Region and Disulfide Bonds into O-Methyl Transferase (PigF). Appl Environ Microbiol 2021; 87:e0054321. [PMID: 34232745 DOI: 10.1128/aem.00543-21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
In Serratia marcescens JNB5-1, prodigiosin was highly produced at 30°C, but it was noticeably repressed at ≥37°C. Our initial results demonstrated that both the production and the stability of the O-methyl transferase (PigF) and oxidoreductase (PigN) involved in the prodigiosin pathway in S. marcescens JNB5-1 sharply decreased at ≥37°C. Therefore, in this study, we improved mRNA stability and protein production using de novo polynucleotide fragments (PNFs) and the introduction of disulfide bonds, respectively, and observed their effects on prodigiosin production. Our results demonstrate that adding PNFs at the 3' untranslated regions of pigF and pigN significantly improved the mRNA half-lives of these genes, leading to an increase in the transcript and expression levels. Subsequently, the introduction of disulfide bonds in pigF improved the thermal stability, pH stability, and copper ion resistance of PigF. Finally, shake flask fermentation showed that the prodigiosin titer with the engineered S. marcescens was increased by 61.38% from 5.36 to 8.65 g/liter compared to the JNB5-1 strain at 30°C and, significantly, the prodigiosin yield increased 2.05-fold from 0.38 to 0.78 g/liter at 37°C. In this study, we revealed that the introduction of PNFs and disulfide bonds greatly improved the expression and stability of pigF and pigN, hence efficiently enhancing prodigiosin production with S. marcescens at 30 and 37°C. IMPORTANCE This study highlights a promising strategy to improve mRNA/enzyme stability and to increase production using de novo PNF libraries and the introduction of disulfide bonds into the protein. PNFs could increase the half-life of target gene mRNA and effectively prevent its degradation. Moreover, PNFs could increase the relative intensity of target genes without affecting the expression of other genes; as a result, it could alleviate the cellular burden compared to other regulatory elements such as promoters. In addition, we obtained a PigF variant with improved activity and stability by the introduction of disulfide bonds into PigF. Collectively, we demonstrate here a novel approach for improving mRNA/enzyme stability using PNFs, which results in enhanced prodigiosin production in S. marcescens at 30°C.
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35
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iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features. Int J Mol Sci 2021; 22:ijms22168958. [PMID: 34445663 PMCID: PMC8396555 DOI: 10.3390/ijms22168958] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/08/2021] [Accepted: 08/17/2021] [Indexed: 12/19/2022] Open
Abstract
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.
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Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 2021; 21:408-420. [PMID: 30649170 DOI: 10.1093/bib/bby124] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/16/2022] Open
Abstract
Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.
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Affiliation(s)
- Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jie Hu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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Li Y, Pu F, Wang J, Zhou Z, Zhang C, He F, Ma Z, Zhang J. Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review. Curr Pharm Des 2021; 27:2189-2198. [PMID: 33183190 DOI: 10.2174/1381612826666201112142826] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 07/27/2020] [Indexed: 11/22/2022]
Abstract
Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.
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Affiliation(s)
- Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingru Wang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Zhiguo Zhou
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Chunhua Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jingbo Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
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Charoenkwan P, Anuwongcharoen N, Nantasenamat C, Hasan MM, Shoombuatong W. In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review. Curr Pharm Des 2021; 27:2180-2188. [PMID: 33138759 DOI: 10.2174/1381612826666201102105827] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/20/2020] [Indexed: 11/22/2022]
Abstract
In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Nuttapat Anuwongcharoen
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
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Zulfiqar H, Yuan SS, Huang QL, Sun ZJ, Dao FY, Yu XL, Lin H. Identification of cyclin protein using gradient boost decision tree algorithm. Comput Struct Biotechnol J 2021; 19:4123-4131. [PMID: 34527186 PMCID: PMC8346528 DOI: 10.1016/j.csbj.2021.07.013] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/15/2021] [Accepted: 07/15/2021] [Indexed: 12/12/2022] Open
Abstract
Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qin-Lai Huang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Jie Sun
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Long Yu
- School of Materials Science and Engineering, Hainan University, Haikou 570228, China
| | - Hao Lin
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Yang H, Tong F, Qi C, Wang P, Li J, Cheng L. Prioritizing Disease-Related Microbes Based on the Topological Properties of a Comprehensive Network. Front Microbiol 2021; 12:685549. [PMID: 34326821 PMCID: PMC8315281 DOI: 10.3389/fmicb.2021.685549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/10/2021] [Indexed: 01/09/2023] Open
Abstract
Many microbes are parasitic within the human body, engaging in various physiological processes and playing an important role in human diseases. The discovery of new microbe-disease associations aids our understanding of disease pathogenesis. Computational methods can be applied in such investigations, thereby avoiding the time-consuming and laborious nature of experimental methods. In this study, we constructed a comprehensive microbe-disease network by integrating known microbe-disease associations from three large-scale databases (Peryton, Disbiome, and gutMDisorder), and extended the random walk with restart to the network for prioritizing unknown microbe-disease associations. The area under the curve values of the leave-one-out cross-validation and the fivefold cross-validation exceeded 0.9370 and 0.9366, respectively, indicating the high performance of this method. Despite being widely studied diseases, in case studies of inflammatory bowel disease, asthma, and obesity, some prioritized disease-related microbes were validated by recent literature. This suggested that our method is effective at prioritizing novel disease-related microbes and may offer further insight into disease pathogenesis.
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Affiliation(s)
- Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Fan Tong
- Academy of Military Medical Science, Beijing, China
| | - Changlu Qi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jiangyu Li
- Academy of Military Medical Science, Beijing, China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University, Harbin, China
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Min X, Lu F, Li C. Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction. Curr Pharm Des 2021; 27:1847-1855. [PMID: 33234095 DOI: 10.2174/1381612826666201124112710] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/29/2020] [Accepted: 08/06/2020] [Indexed: 11/22/2022]
Abstract
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.
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Affiliation(s)
- Xiaoping Min
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Fengqing Lu
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Chunyan Li
- Graduate School, Yunnan Minzu University, Kunming 650504, China
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42
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Feng P, Feng L, Tang C. Comparison and Analysis of Computational Methods for Identifying N6-Methyladenosine Sites in Saccharomyces cerevisiae. Curr Pharm Des 2021; 27:1219-1229. [PMID: 33167827 DOI: 10.2174/1381612826666201109110703] [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: 05/09/2020] [Accepted: 07/20/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND N6-methyladenosine (m6A) plays critical roles in a broad range of biological processes. Knowledge about the precise location of m6A site in the transcriptome is vital for deciphering its biological functions. Although experimental techniques have made substantial contributions to identify m6A, they are still labor intensive and time consuming. As complement to experimental methods, in the past few years, a series of computational approaches have been proposed to identify m6A sites. METHODS In order to facilitate researchers to select appropriate methods for identifying m6A sites, it is necessary to conduct a comprehensive review and comparison of existing methods. RESULTS Since research works on m6A in Saccharomyces cerevisiae are relatively clear, in this review, we summarized recent progress of computational prediction of m6A sites in S. cerevisiae and assessed the performance of existing computational methods. Finally, future directions of computationally identifying m6A sites are presented. CONCLUSION Taken together, we anticipate that this review will serve as an important guide for computational analysis of m6A modifications.
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Affiliation(s)
- Pengmian Feng
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Lijing Feng
- School of Sciences, North China University of Science and Technology, Tangshan 063000, China
| | - Chaohui Tang
- School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
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43
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Zeng R, Cheng S, Liao M. 4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism. Front Cell Dev Biol 2021; 9:664669. [PMID: 34041243 PMCID: PMC8141656 DOI: 10.3389/fcell.2021.664669] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 03/17/2021] [Indexed: 01/10/2023] Open
Abstract
DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.
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Affiliation(s)
- Rao Zeng
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
| | - Song Cheng
- Department of Thoracic Surgery, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Minghong Liao
- Department of Software Engineering, School of Informatics, Xiamen University, Xiamen, China
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Dong GF, Zheng L, Huang SH, Gao J, Zuo YC. Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities. Front Genet 2021; 12:669328. [PMID: 33959153 PMCID: PMC8093877 DOI: 10.3389/fgene.2021.669328] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 03/23/2021] [Indexed: 02/03/2023] Open
Abstract
Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.
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Affiliation(s)
- Gai-Fang Dong
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Lei Zheng
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Sheng-Hui Huang
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Jing Gao
- Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yong-Chun Zuo
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, China
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Zulfiqar H, Khan RS, Hassan F, Hippe K, Hunt C, Ding H, Song XM, Cao R. Computational identification of N4-methylcytosine sites in the mouse genome with machine-learning method. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:3348-3363. [PMID: 34198389 DOI: 10.3934/mbe.2021167] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
N4-methylcytosine (4mC) is a kind of DNA modification which could regulate multiple biological processes. Correctly identifying 4mC sites in genomic sequences can provide precise knowledge about their genetic roles. This study aimed to develop an ensemble model to predict 4mC sites in the mouse genome. In the proposed model, DNA sequences were encoded by k-mer, enhanced nucleic acid composition and composition of k-spaced nucleic acid pairs. Subsequently, these features were optimized by using minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) and five-fold cross-validation. The obtained optimal features were inputted into random forest classifier for discriminating 4mC from non-4mC sites in mouse. On the independent dataset, our model could yield the overall accuracy of 85.41%, which was approximately 3.8% -6.3% higher than the two existing models, i4mC-Mouse and 4mCpred-EL respectively. The data and source code of the model can be freely download from https://github.com/linDing-groups/model_4mc.
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Affiliation(s)
- Hasan Zulfiqar
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Rida Sarwar Khan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Kyle Hippe
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Cassandra Hunt
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
| | - Hui Ding
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Xiao-Ming Song
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Sciences, North China University of Science and Technology, Tangshan, Hebei 063210, China
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma 98447, USA
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ANPrAod: Identify Antioxidant Proteins by Fusing Amino Acid Clustering Strategy and N-Peptide Combination. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5518209. [PMID: 33927782 PMCID: PMC8049822 DOI: 10.1155/2021/5518209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/02/2021] [Accepted: 03/10/2021] [Indexed: 11/18/2022]
Abstract
Antioxidant proteins perform significant functions in disease control and delaying aging which can prevent free radicals from damaging organisms. Accurate identification of antioxidant proteins has important implications for the development of new drugs and the treatment of related diseases, as they play a critical role in the control or prevention of cancer and aging-related conditions. Since experimental identification techniques are time-consuming and expensive, many computational methods have been proposed to identify antioxidant proteins. Although the accuracy of these methods is acceptable, there are still some challenges. In this study, we developed a computational model called ANPrAod to identify antioxidant proteins based on a support vector machine. In order to eliminate potential redundant features and improve prediction accuracy, 673 amino acid reduction alphabets were calculated by us to find the optimal feature representation scheme. The final model could produce an overall accuracy of 87.53% with the ROC of 0.7266 in five-fold cross-validation, which was better than the existing methods. The results of the independent dataset also demonstrated the excellent robustness and reliability of ANPrAod, which could be a promising tool for antioxidant protein identification and contribute to hypothesis-driven experimental design.
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Sun W, Han Y, Yang S, Zhuang H, Zhang J, Cheng L, Fu L. The Assessment of Interleukin-18 on the Risk of Coronary Heart Disease. Med Chem 2021; 16:626-634. [PMID: 31584380 DOI: 10.2174/1573406415666191004115128] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 05/13/2019] [Accepted: 08/23/2019] [Indexed: 12/31/2022]
Abstract
BACKGROUND Observational studies support the inflammation hypothesis in coronary heart disease (CHD). As a pleiotropic proinflammatory cytokine, Interleukin-18 (IL-18), has also been found to be associated with the risk of CHD. However, to our knowledge, the method of Mendelian Randomization has not been used to explore the causal effect of IL-18 on CHD. OBJECTIVE To assess the causal effect of IL-18 on the risk of CHD. METHODS AND RESULTS Genetic variant instruments for IL-18 were obtained from information of the CHS and InCHIANTI cohort, and consisted of the per-allele difference in mean IL-18 for 16 independent variants that reached genome-wide significance. The per-allele difference in log-odds of CHD for each of these variants was estimated from CARDIoGRAMplusC4D, a two-stage meta -analysis. Two-sample Mendelian Randomization (MR) was then performed. Various MR analyses were used, including weighted inverse-variance, MR-Egger regression, robust regression, and penalized regression. The OR of elevated IL-18 associated with CHD was only 0.005 (95%CI -0.105~0.095; P-value=0.927). Similar results were obtained with the use of MR-Egger regression, suggesting that directional pleiotropy was unlikely biasing these results (intercept -0.050, P-value=0.220). Moreover, results from the robust regression and penalized regression analyses also revealed essentially similar findings. CONCLUSION Our findings indicate that, by itself, IL-18 is unlikely to represent even a modest causal factor for CHD risk.
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Affiliation(s)
- Weiju Sun
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ying Han
- Cardiovascular Department, the Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shuo Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - He Zhuang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingwen Zhang
- Department of Physiology and Biology, University of Mississippi Medical Center, United States
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Lu Fu
- Cardiovascular Department, The First Affiliated Hospital of Harbin Medical University, Harbin, China
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Zhang ZM, Guan ZX, Wang F, Zhang D, Ding H. Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families. Med Chem 2021; 16:594-604. [PMID: 31584374 DOI: 10.2174/1573406415666191004125551] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 06/18/2019] [Accepted: 08/23/2019] [Indexed: 11/22/2022]
Abstract
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs are classified and assigned the following seven subfamilies or eight subfamilies: (1) NR1: thyroid hormone like (thyroid hormone, retinoic acid, RAR-related orphan receptor, peroxisome proliferator activated, vitamin D3- like), (2) NR2: HNF4-like (hepatocyte nuclear factor 4, retinoic acid X, tailless-like, COUP-TFlike, USP), (3) NR3: estrogen-like (estrogen, estrogen-related, glucocorticoid-like), (4) NR4: nerve growth factor IB-like (NGFI-B-like), (5) NR5: fushi tarazu-F1 like (fushi tarazu-F1 like), (6) NR6: germ cell nuclear factor like (germ cell nuclear factor), and (7) NR0: knirps like (knirps, knirpsrelated, embryonic gonad protein, ODR7, trithorax) and DAX like (DAX, SHP), or dividing NR0 into (7) NR7: knirps like and (8) NR8: DAX like. Different NRs families have different structural features and functions. Since the function of a NR is closely correlated with which subfamily it belongs to, it is highly desirable to identify NRs and their subfamilies rapidly and effectively. The knowledge acquired is essential for a proper understanding of normal and abnormal cellular mechanisms. With the advent of the post-genomics era, huge amounts of sequence-known proteins have increased explosively. Conventional methods for accurately classifying the family of NRs are experimental means with high cost and low efficiency. Therefore, it has created a greater need for bioinformatics tools to effectively recognize NRs and their subfamilies for the purpose of understanding their biological function. In this review, we summarized the application of machine learning methods in the prediction of NRs from different aspects. We hope that this review will provide a reference for further research on the classification of NRs and their families.
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Affiliation(s)
- Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fang Wang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021; 20:174-180. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic
| | - Shihu Jiao
- College of Chemistry, Sichuan University, Sichuan, China
| | - Dandan Zhang
- Departments of Obstetrics and Gynecology, First Affiliated Hospital of Harbin Medical University
| | - Song Wu
- Preventive Treatment of Disease Centre of Qinhuangdao Hospital of Traditional Chinese Medicine
| | - Haihong Zhang
- First Affiliated Hospital of Harbin Medical University
| | - Bo Gao
- Second Affiliated Hospital, Harbin Medical University, Harbin, China
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50
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Qi C, Wang P, Fu T, Lu M, Cai Y, Chen X, Cheng L. A comprehensive review for gut microbes: technologies, interventions, metabolites and diseases. Brief Funct Genomics 2021; 20:42-60. [PMID: 33554248 DOI: 10.1093/bfgp/elaa029] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 12/17/2020] [Accepted: 12/18/2020] [Indexed: 12/13/2022] Open
Abstract
Gut microbes have attracted much more attentions in the recent decade since their essential roles in the development of metabolic diseases, cancer and neurological diseases. Considerable evidence indicates that the metabolism of gut microbes exert influences on intestinal homeostasis and human diseases. Here, we first reviewed two mainstream sequencing technologies involving 16s rRNA sequencing and metagenomic sequencing for gut microbes, and data analysis methods assessing alpha and beta diversity. Next, we introduced some observational studies reflecting that many factors, such as lifestyle and intake of diets, drugs, contribute to gut microbes' quantity and diversity. Then, metabolites produced by gut microbes were presented to understand that gut microbes exert on host homeostasis in the intestinal epithelium and immune system. Finally, we focused on the molecular mechanism of gut microbes on the occurrence and development of several common diseases. In-depth knowledge of the relationship among interventions, gut microbes and diseases might provide new insights in to disease prevention and treatment.
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