scholarly journals iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2487
Author(s):  
Haitao Han ◽  
Chenchen Ding ◽  
Xin Cheng ◽  
Xiuzhi Sang ◽  
Taigang Liu
Keyword(s):  
Evolutionary Information ◽  
Support Vector ◽  
Target Cells ◽  
Secretion Systems ◽  
Accurate Identification ◽  
Feature Selection Technique ◽  
Type Iv ◽  
Evolutionary Features ◽  
Wide Range ◽  
Encoding Strategies

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test.

Improving Multi-type Gram-negative Bacterial Secreted Protein Prediction via Protein Evolutionary Information and Feature Ranking

2020 ◽  
Vol 15 (6) ◽  
pp. 538-546
Author(s):  
Liang Kong ◽  
Lichao Zhang ◽  
Shiqian He
Keyword(s):  
Cross Correlation ◽  
Secreted Proteins ◽  
Evolutionary Information ◽  
Support Vector ◽  
Feature Ranking ◽  
Secreted Protein ◽  
Secretion Systems ◽  
Gram Negative ◽  
Protein Prediction ◽  
Wide Range

Background: Gram-negative bacteria interact with their environment by secreting a wide range of particular substrates (such as proteins) across two lipid bilayers from the cytoplasm to the extracellular space. Determining the types of secreted proteins is beneficial for further research on secreted proteins and secretion systems. Objective: As an essential alternative for experimental methods, an accurate machine learningbased multi-type Gram-negative bacterial secreted protein prediction method was proposed in this study. Methods: The main contribution is combining auto-cross-correlation analysis and feature ranking technology to build an effective support vector machine-based multi-type Gram-negative bacterial secreted protein predictor. The specifically designed auto-cross-correlation descriptor can capture evolutionary correlation information between amino acid pairs along protein sequence from position specific scoring matrices. Feature ranking technique was used to analyze and select the most informative features for building prediction model. Results: Several kinds of prediction accuracies obtained by independent dataset test are reported on two benchmark datasets. Compared with the state-of-the-art prediction methods, the proposed method improves overall accuracies by 2.91% and 2.25%, respectively. Conclusion: Our study will provide an important guide to utilize protein evolutionary information for further research on bacterial secreted proteins.

scholarly journals Two type IV secretion systems with different functions in Burkholderia cenocepacia K56-2

Microbiology ◽  
2009 ◽  
Vol 155 (12) ◽  
pp. 4005-4013 ◽  
Author(s):  
Ruifu Zhang ◽  
John J. LiPuma ◽  
Carlos F. Gonzalez
Keyword(s):  
Type Iv Secretion ◽  
Burkholderia Cenocepacia ◽  
Target Cells ◽  
Secretion Systems ◽  
Type Iv ◽  
Plasmid Mobilization ◽  
Type Iv Secretion Systems ◽  
Derivative Plasmid ◽  
Chromosome Ii ◽  
Bacterial Type

Bacterial type IV secretion systems (T4SS) perform two fundamental functions related to pathogenesis: the delivery of effector molecules to eukaryotic target cells, and genetic exchange. Two T4SSs have been identified in Burkholderia cenocepacia K56-2, a representative of the ET12 lineage of the B. cepacia complex (Bcc). The plant tissue watersoaking (Ptw) T4SS encoded on a resident 92 kb plasmid is a chimera composed of VirB/D4 and F-specific subunits, and is responsible for the translocation of effector(s) that have been linked to the Ptw phenotype. The bc-VirB/D4 system located on chromosome II displays homology to the VirB/D4 T4SS of Agrobacterium tumefaciens. In contrast to the Ptw T4SS, the bc-VirB/D4 T4SS was found to be dispensable for Ptw effector(s) secretion, but was found to be involved in plasmid mobilization. The fertility inhibitor Osa did not affect the secretion of Ptw effector(s) via the Ptw system, but did disrupt the mobilization of a RSF1010 derivative plasmid.

scholarly journals Peptidomimetic Small Molecules Disrupt Type IV Secretion System Activity in Diverse Bacterial Pathogens

mBio ◽  
2016 ◽  
Vol 7 (2) ◽  
Author(s):  
Carrie L. Shaffer ◽  
James A. D. Good ◽  
Santosh Kumar ◽  
K. Syam Krishnan ◽  
Jennifer A. Gaddy ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Small Molecules ◽  
Bacterial Pathogens ◽  
Effector Proteins ◽  
Type Iv Secretion ◽  
Target Cells ◽  
Effector Protein ◽  
Secretion Systems ◽  
Type Iv ◽  
H Pylori

ABSTRACT Bacteria utilize complex type IV secretion systems (T4SSs) to translocate diverse effector proteins or DNA into target cells. Despite the importance of T4SSs in bacterial pathogenesis, the mechanism by which these translocation machineries deliver cargo across the bacterial envelope remains poorly understood, and very few studies have investigated the use of synthetic molecules to disrupt T4SS-mediated transport. Here, we describe two synthetic small molecules (C10 and KSK85) that disrupt T4SS-dependent processes in multiple bacterial pathogens. Helicobacter pylori exploits a pilus appendage associated with the cag T4SS to inject an oncogenic effector protein (CagA) and peptidoglycan into gastric epithelial cells. In H. pylori , KSK85 impedes biogenesis of the pilus appendage associated with the cag T4SS, while C10 disrupts cag T4SS activity without perturbing pilus assembly. In addition to the effects in H. pylori , we demonstrate that these compounds disrupt interbacterial DNA transfer by conjugative T4SSs in Escherichia coli and impede vir T4SS-mediated DNA delivery by Agrobacterium tumefaciens in a plant model of infection. Of note, C10 effectively disarmed dissemination of a derepressed IncF plasmid into a recipient bacterial population, thus demonstrating the potential of these compounds in mitigating the spread of antibiotic resistance determinants driven by conjugation. To our knowledge, this study is the first report of synthetic small molecules that impair delivery of both effector protein and DNA cargos by diverse T4SSs. IMPORTANCE Many human and plant pathogens utilize complex nanomachines called type IV secretion systems (T4SSs) to transport proteins and DNA to target cells. In addition to delivery of harmful effector proteins into target cells, T4SSs can disseminate genetic determinants that confer antibiotic resistance among bacterial populations. In this study, we sought to identify compounds that disrupt T4SS-mediated processes. Using the human gastric pathogen H. pylori as a model system, we identified and characterized two small molecules that prevent transfer of an oncogenic effector protein to host cells. We discovered that these small molecules also prevented the spread of antibiotic resistance plasmids in E. coli populations and diminished the transfer of tumor-inducing DNA from the plant pathogen A. tumefaciens to target cells. Thus, these compounds are versatile molecular tools that can be used to study and disarm these important bacterial machines.

Abstract 473: Identification of Apolipoproteins Using Feature Selection Technique

2016 ◽  
Vol 36 (suppl_1) ◽  
Author(s):  
Hua Tang ◽  
Hao Lin
Keyword(s):  
Support Vector Machine ◽  
Cross Validation ◽  
Support Vector ◽  
Feature Subset ◽  
Risk Markers ◽  
Dipeptide Composition ◽  
Accurate Identification ◽  
Feature Selection Technique ◽  
Physiological Importance ◽  
Fold Cross Validation

Objective: Apolipoproteins are of great physiological importance and are associated with different diseases such as dyslipidemia, thrombogenesis and angiocardiopathy. Apolipoproteins have therefore emerged as key risk markers and important research targets yet the types of apolipoproteins has not been fully elucidated. Accurate identification of the apoliproproteins is very crucial to the comprehension of cardiovascular diseases and drug design. The aim of this study is to develop a powerful model to precisely identify apolipoproteins. Approach and Results: We manually collected a non-redundant dataset of 53 apoliproproteins and 136 non-apoliproproteins with the sequence identify of less than 40% from UniProt. After formulating the protein sequence samples with g -gap dipeptide composition (here g =1~10), the analysis of various (ANOVA) was adopted to find out the best feature subset which can achieve the best accuracy. Support Vector Machine (SVM) was then used to perform classification. The predictive model was evaluated using a five-fold cross-validation which yielded a sensitivity of 96.2%, a specificity of 99.3%, and an accuracy of 98.4%. The study indicated that the proposed method could be a feasible means of conducting preliminary analyses of apoliproproteins. Conclusion: We demonstrated that apoliproproteins can be predicted from their primary sequences. Also we discovered the special dipeptide distribution in apoliproproteins. These findings open new perspectives to improve apoliproproteins prediction by considering the specific dipeptides. We expect that these findings will help to improve drug development in anti-angiocardiopathy disease. Key words: Apoliproproteins Angiocardiopathy Support Vector Machine

scholarly journals DNA Delivery and Genomic Integration into Mammalian Target Cells through Type IV A and B Secretion Systems of Human Pathogens

2017 ◽  
Vol 8 ◽  
Author(s):  
Dolores L. Guzmán-Herrador ◽  
Samuel Steiner ◽  
Anabel Alperi ◽  
Coral González-Prieto ◽  
Craig R. Roy ◽  
...  
Keyword(s):  
Dna Delivery ◽  
Target Cells ◽  
Human Pathogens ◽  
Secretion Systems ◽  
Genomic Integration ◽  
Type Iv

scholarly journals Using an Optimal Set of Features with a Machine Learning-Based Approach to Predict Effector Proteins forLegionella pneumophila

2018 ◽  
Author(s):  
Zhila Esna Ashari ◽  
Kelly A. Brayton ◽  
Shira L. Broschat
Keyword(s):  
Machine Learning ◽  
Legionella Pneumophila ◽  
De Novo ◽  
Effector Proteins ◽  
Machine Learning Algorithms ◽  
Host Cells ◽  
Support Vector ◽  
Secretion Systems ◽  
Type Iv ◽  
Optimal Set

AbstractType IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This work focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathogenLegionella pneumophilastrain Philadelphia-1, a cause of Legionnaires’ disease, because it has many validated effector proteins and others have developed machine learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, Model 1 predicted 760 effector proteins, more than any other study, 315 of which have been validated. Although the results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate effectors.

i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome

2019 ◽  
Vol 35 (16) ◽  
pp. 2796-2800 ◽  
Author(s):  
Wei Chen ◽  
Hao Lv ◽  
Fulei Nie ◽  
Hao Lin
Keyword(s):  
Dna Sequences ◽  
Rice Genome ◽  
Chemical Properties ◽  
Computational Method ◽  
Accurate Identification ◽  
Feature Selection Technique ◽  
Jackknife Test ◽  
Genome Wide ◽  
Wide Range ◽  
User Friendly

Abstract Motivation DNA N6-methyladenine (6mA) is associated with a wide range of biological processes. Since the distribution of 6mA site in the genome is non-random, accurate identification of 6mA sites is crucial for understanding its biological functions. Although experimental methods have been proposed for this regard, they are still cost-ineffective for detecting 6mA site in genome-wide scope. Therefore, it is desirable to develop computational methods to facilitate the identification of 6mA site. Results In this study, a computational method called i6mA-Pred was developed to identify 6mA sites in the rice genome, in which the optimal nucleotide chemical properties obtained by the using feature selection technique were used to encode the DNA sequences. It was observed that the i6mA-Pred yielded an accuracy of 83.13% in the jackknife test. Meanwhile, the performance of i6mA-Pred was also superior to other methods. Availability and implementation A user-friendly web-server, i6mA-Pred is freely accessible at http://lin-group.cn/server/i6mA-Pred.

scholarly journals Biological Diversity of Prokaryotic Type IV Secretion Systems

2009 ◽  
Vol 73 (4) ◽  
pp. 775-808 ◽  
Author(s):  
Cristina E. Alvarez-Martinez ◽  
Peter J. Christie
Keyword(s):  
Target Cell ◽  
Biological Diversity ◽  
Cell Interaction ◽  
Type Iv Secretion ◽  
Target Cells ◽  
Secretion Systems ◽  
Extracellular Milieu ◽  
Type Iv ◽  
Type Iv Secretion Systems ◽  
Cell Envelopes

SUMMARY Type IV secretion systems (T4SS) translocate DNA and protein substrates across prokaryotic cell envelopes generally by a mechanism requiring direct contact with a target cell. Three types of T4SS have been described: (i) conjugation systems, operationally defined as machines that translocate DNA substrates intercellularly by a contact-dependent process; (ii) effector translocator systems, functioning to deliver proteins or other macromolecules to eukaryotic target cells; and (iii) DNA release/uptake systems, which translocate DNA to or from the extracellular milieu. Studies of a few paradigmatic systems, notably the conjugation systems of plasmids F, R388, RP4, and pKM101 and the Agrobacterium tumefaciens VirB/VirD4 system, have supplied important insights into the structure, function, and mechanism of action of type IV secretion machines. Information on these systems is updated, with emphasis on recent exciting structural advances. An underappreciated feature of T4SS, most notably of the conjugation subfamily, is that they are widely distributed among many species of gram-negative and -positive bacteria, wall-less bacteria, and the Archaea. Conjugation-mediated lateral gene transfer has shaped the genomes of most if not all prokaryotes over evolutionary time and also contributed in the short term to the dissemination of antibiotic resistance and other virulence traits among medically important pathogens. How have these machines adapted to function across envelopes of distantly related microorganisms? A survey of T4SS functioning in phylogenetically diverse species highlights the biological complexity of these translocation systems and identifies common mechanistic themes as well as novel adaptations for specialized purposes relating to the modulation of the donor-target cell interaction.

scholarly journals Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhehan Shen ◽  
Taigang Liu ◽  
Ting Xu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Evolutionary Features ◽  
Cross Covariance ◽  
Antioxidant Proteins

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

scholarly journals Rhizobium sp. Strain NGR234 Possesses a Remarkable Number of Secretion Systems

2009 ◽  
Vol 75 (12) ◽  
pp. 4035-4045 ◽  
Author(s):  
Christel Schmeisser ◽  
Heiko Liesegang ◽  
Dagmar Krysciak ◽  
Nadia Bakkou ◽  
Antoine Le Quéré ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Cellular Growth ◽  
Type I ◽  
Secretion Systems ◽  
Type Iv ◽  
Symbiotic Plasmid ◽  
Wide Range ◽  
Twin Arginine Translocase ◽  
Type V ◽  
Quorum Sensing Signals

ABSTRACT Rhizobium sp. strain NGR234 is a unique alphaproteobacterium (order Rhizobiales) that forms nitrogen-fixing nodules with more legumes than any other microsymbiont. We report here that the 3.93-Mbp chromosome (cNGR234) encodes most functions required for cellular growth. Few essential functions are encoded on the 2.43-Mbp megaplasmid (pNGR234b), and none are present on the second 0.54-Mbp symbiotic plasmid (pNGR234a). Among many striking features, the 6.9-Mbp genome encodes more different secretion systems than any other known rhizobia and probably most known bacteria. Altogether, 132 genes and proteins are linked to secretory processes. Secretion systems identified include general and export pathways, a twin arginine translocase secretion system, six type I transporter genes, one functional and one putative type III system, three type IV attachment systems, and two putative type IV conjugation pili. Type V and VI transporters were not identified, however. NGR234 also carries genes and regulatory networks linked to the metabolism of a wide range of aromatic and nonaromatic compounds. In this way, NGR234 can quickly adapt to changing environmental stimuli in soils, rhizospheres, and plants. Finally, NGR234 carries at least six loci linked to the quenching of quorum-sensing signals, as well as one gene (ngrI) that possibly encodes a novel type of autoinducer I molecule.

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