scholarly journals Predicting Cell Wall Lytic Enzymes Using Combined Features

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiao-Yang Jing ◽  
Feng-Min Li
Keyword(s):  
Cell Wall ◽  
Protein Function ◽  
Support Vector ◽  
Improved Method ◽  
Classification Problems ◽  
Lytic Enzymes ◽  
Jackknife Test ◽  
Imbalanced Data Classification ◽  
Auto Covariance ◽  
Cell Wall Lytic Enzymes

Due to the overuse of antibiotics, people are worried that existing antibiotics will become ineffective against pathogens with the rapid rise of antibiotic-resistant strains. The use of cell wall lytic enzymes to destroy bacteria has become a viable alternative to avoid the crisis of antimicrobial resistance. In this paper, an improved method for cell wall lytic enzymes prediction was proposed and the amino acid composition (AAC), the dipeptide composition (DC), the position-specific score matrix auto-covariance (PSSM-AC), and the auto-covariance average chemical shift (acACS) were selected to predict the cell wall lytic enzymes with support vector machine (SVM). In order to overcome the imbalanced data classification problems and remove redundant or irrelevant features, the synthetic minority over-sampling technique (SMOTE) was used to balance the dataset. The F-score was used to select features. The Sn, Sp, MCC, and Acc were 99.35%, 99.02%, 0.98, and 99.19% with jackknife test using the optimized combination feature AAC+DC+acACS+PSSM-AC. The Sn, Sp, MCC, and Acc of cell wall lytic enzymes in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

scholarly journals Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiao-Yang Jing ◽  
Feng-Min Li
Keyword(s):  
Heat Shock ◽  
Protein Function ◽  
Protein Function Prediction ◽  
Imbalanced Data ◽  
Support Vector ◽  
Main Function ◽  
Classification Problems ◽  
Growth And Survival ◽  
Jackknife Test ◽  
Living Organisms

Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and mass, HSPs are categorized into six different families: HSP20 (small HSPS), HSP40 (J-proteins), HSP60, HSP70, HSP90, and HSP100. In this paper, improved methods for HSP prediction are proposed—the split amino acid composition (SAAC), the dipeptide composition (DC), the conjoint triad feature (CTF), and the pseudoaverage chemical shift (PseACS) were selected to predict the HSPs with a support vector machine (SVM). In order to overcome the imbalance data classification problems, the syntactic minority oversampling technique (SMOTE) was used to balance the dataset. The overall accuracy was 99.72% with a balanced dataset in the jackknife test by using the optimized combination feature SAAC+DC+CTF+PseACS, which was 4.81% higher than the imbalanced dataset with the same combination feature. The Sn, Sp, Acc, and MCC of HSP families in our predictive model were higher than those in existing methods. This improved method may be helpful for protein function prediction.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

Searching for the Evolutionary Design of the Pneumococcal Cell Wall Lytic Enzymes

1993 ◽  
pp. 253-259
Author(s):  
Rubens López ◽  
José L. García ◽  
Eduardo Díaz ◽  
Jesús M. Sanz ◽  
José M. Sánchez-Puelles ◽  
...  
Keyword(s):  
Cell Wall ◽  
Evolutionary Design ◽  
Lytic Enzymes ◽  
Cell Wall Lytic Enzymes

scholarly journals Roles of LysM and LytM domains in resuscitation-promoting factor (Rpf) activity and Rpf-mediated peptidoglycan cleavage and dormant spore reactivation

2020 ◽  
Vol 295 (27) ◽  
pp. 9171-9182 ◽  
Author(s):  
Danielle L. Sexton ◽  
Francesca A. Herlihey ◽  
Ashley S. Brott ◽  
David A. Crisante ◽  
Evan Shepherdson ◽  
...  
Keyword(s):  
Cell Wall ◽  
Spore Germination ◽  
Lytic Enzymes ◽  
Lytic Transglycosylases ◽  
Signaling Function ◽  
Dormant Spore ◽  
The Subject ◽  
Resuscitation Promoting Factors ◽  
Insight Into ◽  
Cell Wall Lytic Enzymes

Bacterial dormancy can take many forms, including formation of Bacillus endospores, Streptomyces exospores, and metabolically latent Mycobacterium cells. In the actinobacteria, including the streptomycetes and mycobacteria, the rapid resuscitation from a dormant state requires the activities of a family of cell-wall lytic enzymes called resuscitation-promoting factors (Rpfs). Whether Rpf activity promotes resuscitation by generating peptidoglycan fragments (muropeptides) that function as signaling molecules for spore germination or by simply remodeling the dormant cell wall has been the subject of much debate. Here, to address this question, we used mutagenesis and peptidoglycan binding and cleavage assays to first gain broader insight into the biochemical function of diverse Rpf enzymes. We show that their LysM and LytM domains enhance Rpf enzyme activity; their LytM domain and, in some cases their LysM domain, also promoted peptidoglycan binding. We further demonstrate that the Rpfs function as endo-acting lytic transglycosylases, cleaving within the peptidoglycan backbone. We also found that unlike in other systems, Rpf activity in the streptomycetes is not correlated with peptidoglycan-responsive Ser/Thr kinases for cell signaling, and the germination of rpf mutant strains could not be stimulated by the addition of known germinants. Collectively, these results suggest that in Streptomyces, Rpfs have a structural rather than signaling function during spore germination, and that in the actinobacteria, any signaling function associated with spore resuscitation requires the activity of additional yet to be identified enzymes.

Lysis ofPediococcusandBrevibacteriumCells by Cell-Wall Lytic Enzymes, L3, L11, ALE and Lysozyme

1966 ◽  
Vol 30 (11) ◽  
pp. 1097-1101
Author(s):  
Kenji Sakaguchi ◽  
Shozo Kotani ◽  
Hidekazu Suginaka ◽  
Yoshiyuki Hirachi ◽  
Shuzo Kashiba ◽  
...  
Keyword(s):  
Cell Wall ◽  
Lytic Enzymes ◽  
Cell Wall Lytic Enzymes

scholarly journals Random Mutagenesis Identifies Novel Genes Involved in the Secretion of Antimicrobial, Cell Wall-Lytic Enzymes by Lactococcus lactis

2008 ◽  
Vol 74 (24) ◽  
pp. 7490-7496 ◽  
Author(s):  
Yu Pei Tan ◽  
Philip M. Giffard ◽  
Daniel G. Barry ◽  
Wilhelmina M. Huston ◽  
Mark S. Turner
Keyword(s):  
Cell Wall ◽  
Lactococcus Lactis ◽  
Transmembrane Protein ◽  
Random Mutagenesis ◽  
Lytic Enzyme ◽  
Control Strain ◽  
Lytic Enzymes ◽  
Positive Bacterium ◽  
Peptidoglycan Biosynthesis ◽  
Cell Wall Lytic Enzymes

ABSTRACT Lactococcus lactis is a gram-positive bacterium that is widely used in the food industry and is therefore desirable as a candidate for the production and secretion of recombinant proteins. Previously, we generated a L. lactis strain that expressed and secreted the antimicrobial cell wall-lytic enzyme lysostaphin. To identify lactococcal gene products that affect the production of lysostaphin, we isolated and characterized mutants generated by random transposon mutagenesis that had altered lysostaphin activity. Out of 35,000 mutants screened, only one with no lysostaphin activity was identified, and it was found to contain an insertion in the lysostaphin expression cassette. Ten mutants with higher lysostaphin activity contained insertions in only four different genes, which encode an uncharacterized putative transmembrane protein (llmg_0609) (three mutants), an enzyme catalyzing the first step in peptidoglycan biosynthesis (murA2) (five mutants), a putative regulator of peptidoglycan modification (trmA) (one mutant), and an uncharacterized enzyme possibly involved in ubiquinone biosynthesis (llmg_2148) (one mutant). These mutants were found to secrete larger amounts of lysostaphin than the control strain (MG1363[lss]), and the greatest increase in secretion was 9.8- to 16.1-fold, for the llmg_0609 mutants. The lysostaphin-oversecreting llmg_0609, murA2, and trmA mutants were also found to secrete larger amounts of another cell wall-lytic enzyme (the Listeria monocytogenes bacteriophage endolysin Ply511) than the control strain, indicating that the phenotype is not limited to lysostaphin.

scholarly journals Lysis of Pediococcus and Brevibacterium Cells by Cell-Wall Lytic Enzymes, L3, L11, ALE and Lysozyme

1966 ◽  
Vol 30 (11) ◽  
pp. 1097-1101
Author(s):  
Kenji SAKAGUCHI ◽  
Shozo KOTANI ◽  
Hidekazu SUGINAKA ◽  
Yoshiyuki HIRACHI ◽  
Shuzo KASHIBA ◽  
...  
Keyword(s):  
Cell Wall ◽  
Lytic Enzymes ◽  
Cell Wall Lytic Enzymes
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