amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

Lawrence, Travis J; Carper, Dana L.; Spangler, Margaret K.; Carrell, Alyssa A.; Rush, Tomás A.; Minter, Stephen J.; Weston, David J.; Labbé, Jessy

doi:10.1093/bioinformatics/btaa917

Cited by 52 publications

(23 citation statements)

References 20 publications

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“…Sequences with a length smaller than 100 amino acids were retrieved from the predicted M. complanata proteome using Linux (code repository: https://github.com/ vhelizarraga/Fire_coral_analysis.git, accessed on 2 September 2021). Potential AMPs were identified from these sequences with amPEPpy [64]. The antimicrobial activity was inferred by implementing a random forest classifier using the distribution descriptor set from the Global Protein Sequence Descriptors.…”

Section: Prediction Of Ampsmentioning

confidence: 99%

New Insights into the Toxin Diversity and Antimicrobial Activity of the “Fire Coral” Millepora complanata

Hérnandez-Elizárraga

Ocharán-Mercado

Olguín-López

et al. 2022

Toxins

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To date, few studies have been carried out aimed at characterizing the toxins synthesized by hydrocorals of the genus Millepora. The purpose of this study was to explore the toxin diversity and antibacterial activity of the “fire coral” M. complanata using a transcriptomic data mining approach. In addition, the cytolytic and antibacterial activities of the M. complanata nematocyst proteome were experimentally confirmed. Cytolysins were predicted from the transcriptome by comparing against the Animal Toxin Annotation Project database, resulting in 190 putative toxins, including metalloproteases, hemostasis-impairing toxins, phospholipases, among others. The M. complanata nematocyst proteome was analyzed by 1D and 2D electrophoresis and zymography. The zymograms showed different zones of cytolytic activity: two zones of hemolysis at ~25 and ~205 kDa, two regions corresponding to phospholipase A2 (PLA2) activity around 6 and 25 kDa, and a proteolytic zone was observed between 50 and 205 kDa. The hemolytic activity of the proteome was inhibited in the presence of PLA2 and proteases inhibitors, suggesting that PLA2s, trypsin, chymotrypsin, serine-proteases, and matrix metalloproteases are responsible for the hemolysis. On the other hand, antimicrobial peptide sequences were retrieved from their transcripts with the amPEPpy software. This analysis revealed the presence of homologs to SK84, cgUbiquitin, Ubiquicidin, TroTbeta4, SPINK9-v1, and Histone-related antimicrobials in the transcriptome of this cnidarian. Finally, by employing disk diffusion and microdilution assays, we found that the nematocyst peptidome of M. complanata showed inhibitory activity against both Gram-positive and Gram-negative bacteria including S. enteritidis, P. perfectomarina, E. coli, and C. xerosis, among others. This is the first transcriptomic data mining analysis to explore the diversity of the toxins synthesized by an organism of the genus Millepora. Undoubtedly, this work provides information that will broaden our general understanding of the structural richness of cnidarian toxins.

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Section: Prediction Of Ampsmentioning

confidence: 99%

New Insights into the Toxin Diversity and Antimicrobial Activity of the “Fire Coral” Millepora complanata

Hérnandez-Elizárraga

Ocharán-Mercado

Olguín-López

et al. 2022

Toxins

View full text Add to dashboard Cite

show abstract

“…Additionally, each genome was interrogated by the software tool amPEPpy, which uses a random forest classifier to predict putative antimicrobial peptides based on protein sequence characteristics often attributed to antimicrobials (e.g., small open reading frames, positively charged, etc.) (Lawrence et al, 2020).…”

Section: Genomic Prediction Tools Highlight Natural Product Diversity Across Trichoderma Speciesmentioning

confidence: 99%

“…To date, there were more than 30 computational methods for AMP prediction and identification as of 2021 (Xu et al, 2021). Of those computational methods, amPEPpy is a userfriendly, open-source, portable, multi-threaded command-line application for predicting AMPs through genome-based screening using a random forest classifier (Lawrence et al, 2020). amPEPpy was validated as predicting AMPs more The backbone enzyme type, the function of the predicted metabolite, whether it has been identified in Trichoderma species, and which fungi produce these metabolites are described.…”

Section: Antimicrobial Peptides Exploration Through Genomic Mining and Computational Analysismentioning

confidence: 99%

“…accurately than other approaches (Xu et al, 2021). We used amPEPpy to predict promising antimicrobial agents (Lawrence et al, 2020) within the Trichoderma genomes. We compared predicted AMPs sequences to sequences available on NCBI as of April 2021 because there are no online tools that cross-link data generated from amPEPpy to numerous known AMPs, as antiSMASH does with secondary metabolites.…”

Section: Antimicrobial Peptides Exploration Through Genomic Mining and Computational Analysismentioning

confidence: 99%

See 1 more Smart Citation

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

Rush

Shrestha

Meena

et al. 2021

Front. Fungal Biol.

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Natural products derived from microbes are crucial innovations that would help in reaching sustainability development goals worldwide while achieving bioeconomic growth. Trichoderma species are well-studied model fungal organisms used for their biocontrol properties with great potential to alleviate the use of agrochemicals in agriculture. However, identifying and characterizing effective natural products in novel species or strains as biological control products remains a meticulous process with many known challenges to be navigated. Integration of recent advancements in various “omics” technologies, next generation biodesign, machine learning, and artificial intelligence approaches could greatly advance bioprospecting goals. Herein, we propose a roadmap for assessing the potential impact of already known or newly discovered Trichoderma species for biocontrol applications. By screening publicly available Trichoderma genome sequences, we first highlight the prevalence of putative biosynthetic gene clusters and antimicrobial peptides among genomes as an initial step toward predicting which organisms could increase the diversity of natural products. Next, we discuss high-throughput methods for screening organisms to discover and characterize natural products and how these findings impact both fundamental and applied research fields.

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“…AMPs are one of the major classes of therapeutic peptides that are commonly used to kill microbial pathogens including the drug-resistant strain of pathogens (24,25). In the past, numerous computational resources and methods have been developed for predicting AMPs including chemically modified AMPs (26)(27)(28)(29)(30)(31)(32)(33)(34). In addition to AMPs, a number of methods have been developed to predict peptides for killing a specific class of microorganism which include prediction of antibacterial, antituberculosis, antiviral, antifungal, and antiparasite peptides (35)(36)(37)(38)(39)(40)(41).…”

Section: Introductionmentioning

confidence: 99%

In-Silico Tool for Predicting, Scanning, and Designing Defensins

et al. 2021

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Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service “DefPred” to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

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amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool

Cited by 52 publications

References 20 publications

New Insights into the Toxin Diversity and Antimicrobial Activity of the “Fire Coral” Millepora complanata

New Insights into the Toxin Diversity and Antimicrobial Activity of the “Fire Coral” Millepora complanata

Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications

In-Silico Tool for Predicting, Scanning, and Designing Defensins

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