AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest

Bhadra, Pratiti; Yan, Jielu; Li, Jinyan; Fong, Simon; Siu, Shirley W. I.

doi:10.1038/s41598-018-19752-w

Cited by 237 publications

(205 citation statements)

References 35 publications

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“…Larger values of importance indicate stronger predictors, and values close to zero suggest the variable is not a good predictor. RF are popular because of their ability to deal with large numbers of covariates, non-linear associations, complex interactions and correlations between variables; RF have been used in many biomedical research fields [ 23 – 27 ]. In our RF variable importance (RFVI) analysis we converted all predictor variables into binomials, to avoid reported possible bias of RF when used with categorical variables with multiple levels, or correlated predictors [ 28 , 29 ].…”

Section: Methodsmentioning

confidence: 99%

Outcomes of early NIH-funded investigators: Experience of the National Institute of Allergy and Infectious Diseases

Haggerty¹,

Fenton²

2018

PLoS ONE

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Survival of junior scientists in academic biomedical research is difficult in today’s highly competitive funding climate. National Institute of Health (NIH) data on first-time R01 grantees indicate the rate at which early investigators drop out from a NIH-supported research career is most rapid 4 to 5 years from the first R01 award. The factors associated with a high risk of dropping out, and whether these factors impact all junior investigators equally, are unclear. We identified a cohort of 1,496 investigators who received their first R01-equivalent (R01-e) awards from the National Institute of Allergy and Infectious Diseases between 2003 and 2010, and studied all their subsequent NIH grant applications through 2016. Ultimately, 57% of the cohort were successful in obtaining new R01-e funding, despite highly competitive conditions. Among those investigators who failed to compete successfully for new funding (43%), the average time to dropping out was 5 years. Investigators who successfully obtained new grants showed remarkable within-person consistency across multiple grant submission behaviors, including submitting more applications per year, more renewal applications, and more applications to multiple NIH Institutes. Funded investigators appeared to have two advantages over their unfunded peers at the outset: they had better scores on their first R01-e grants and they demonstrated an early ability to write applications that would be scored, not triaged. The cohort rapidly segregated into two very different groups on the basis of PI consistency in the quality and frequency of applications submitted after their first R01-e award. Lastly, we identified a number of specific demographic factors, intitutional characteristics, and grant submission behaviors that were associated with successful outcomes, and assessed their predictive value and relative importance for the likelihood of obtaining additional NIH funding.

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Section: Methodsmentioning

confidence: 99%

Outcomes of early NIH-funded investigators: Experience of the National Institute of Allergy and Infectious Diseases

Haggerty¹,

Fenton²

2018

PLoS ONE

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“…AMP prediction has also been performed by RF methods, which are based on ensemble learning algorithms and work by multiple decision trees built on training data (Schierz, 2009). In terms of AMP prediction, studies have proposed a new tool called AmPEP (Table 1), as an attempt to develop a highly accurate RF classifier for AMP prediction based on pattern distribution and physicochemical properties (Bhadra et al, 2018). Its performance was comparable with other predictive tools and it showed higher values for particular parameters of comparison, even with a reduced number of features.…”

Section: Antibp2mentioning

confidence: 99%

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

et al. 2020

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Computer-Aided Design of Antimicrobial Peptides http://crdd.osdd.net/raghava/antibp2/ ClassAMP Uses RF and SVM to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity http://www.bicnirrh.res.in/classamp/ AMPA Web tool for assessing the antimicrobial domains of proteins, with a focus on the design on new antimicrobial drugs http://tcoffee.crg.cat/apps/ampa/do DBAASP Provides users with information on detailed structure (chemical, 3D) and activity for those peptides, for which antimicrobial activity against particular target species have been evaluated experimentally https://dbaasp.org/home Joker An algorithm to design antimicrobial peptides using their language https://github.com/williamfp7/Joker

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“…Previous studies have organized amino acids into several physicochemical property groups [13,17]. As shown in Appendix A Table A3, seven physicochemical properties were used in the grouping: (1) charge, (2) hydrophobicity, (3) polarity, (4) polarizability, (5) secondary structure, (6) normalized van der Waals volume, and (7) solvent accessibility.…”

Section: Feature Constructionsmentioning

confidence: 99%

“…Several studies have been dedicated to the prediction of AMPs, such as AntiBP [5], AntiBP2 [6], CAMP [7], ClassAMP [8], AVPpred [9], AMPER [10], iAMP-2L [11], iAMPred [12], AmPEP [13], Figure 1A demonstrates the average AACs of AMPs and non-AMPs. Specifically, "L", "G", and "K" were abundant amino acids for AMPs, while "L", "A", and "G" were abundant amino acids for non-AMPs.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

Chung

Jhong

Wang

et al. 2020

IJMS

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Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.

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AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest

Cited by 237 publications

References 35 publications

Outcomes of early NIH-funded investigators: Experience of the National Institute of Allergy and Infectious Diseases

Outcomes of early NIH-funded investigators: Experience of the National Institute of Allergy and Infectious Diseases

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

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