Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data.

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at http://BioGenies.info/AMPBenchmark.

Testing Antimicrobial Properties of Human Lactoferrin-Derived Fragments.

Lactoferrin, an iron-binding glycoprotein, plays a significant role in the innate immune system, with antibacterial, antivirial, antifungal, anticancer, antioxidant and immunomodulatory functions reported. It is worth emphasizing that not only the whole protein but also its derived fragments possess antimicrobial peptide (AMP) activity. Using AmpGram, a top-performing AMP classifier, we generated three novel human lactoferrin (hLF) fragments: hLF 397-412, hLF 448-464 and hLF 668-683, predicted with high probability as AMPs. For comparative studies, we included hLF 1-11, previously confirmed to kill some bacteria. With the four peptides, we treated three Gram-negative and three Gram-positive bacterial strains. Our results indicate that none of the three new lactoferrin fragments have antimicrobial properties for the bacteria tested, but hLF 1-11 was lethal against Pseudomonas aeruginosa. The addition of serine protease inhibitors with the hLF fragments did not enhance their activity, except for hLF 1-11 against P. aeruginosa, which MIC dropped from 128 to 64 µg/mL. Furthermore, we investigated the impact of EDTA with/without serine protease inhibitors and the hLF peptides on selected bacteria. We stress the importance of reporting non-AMP sequences for the development of next-generation AMP prediction models, which suffer from the lack of experimentally validated negative dataset for training and benchmarking.

Testing Antimicrobial Properties of Selected Short Amyloids.

Amyloids and antimicrobial peptides (AMPs) have many similarities, e.g., both kill microorganisms by destroying their membranes, form aggregates, and modulate the innate immune system. Given these similarities and the fact that the antimicrobial properties of short amyloids have not yet been investigated, we chose a group of potentially antimicrobial short amyloids to verify their impact on bacterial and eukaryotic cells. We used AmpGram, a best-performing AMP classification model, and selected ten amyloids with the highest AMP probability for our experimental research. Our results indicate that four tested amyloids: VQIVCK, VCIVYK, KCWCFT, and GGYLLG, formed aggregates under the conditions routinely used to evaluate peptide antimicrobial properties, but none of the tested amyloids exhibited antimicrobial or cytotoxic properties. Accordingly, they should be included in the negative datasets to train the next-generation AMP prediction models, based on experimentally confirmed AMP and non-AMP sequences. In the article, we also emphasize the importance of reporting non-AMPs, given that only a handful of such sequences have been officially confirmed.

Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram.

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Characterization of signal and transit peptides based on motif composition and taxon-specific patterns.

Targeting peptides or presequences are N-terminal extensions of proteins that encode information about their cellular localization. They include signal peptides (SP), which target proteins to the endoplasmic reticulum, and transit peptides (TP) directing proteins to the organelles of endosymbiotic origin: chloroplasts and mitochondria. TPs were hypothesized to have evolved from antimicrobial peptides (AMPs), which are responsible for the host defence against microorganisms, including bacteria, fungi and viruses. In this study, we performed comprehensive bioinformatic analyses of amino acid motifs of targeting peptides and AMPs using a curated set of experimentally verified proteins. We identified motifs frequently occurring in each type of presequence showing specific patterns associated with their amino acid composition, and investigated their position within the presequence. We also compared motif patterns among different taxonomic groups and identified taxon-specific features, providing some evolutionary insights. Considering the functional relevance and many practical applications of targeting peptides and AMPs, we believe that our analyses will prove useful for their design, and better understanding of protein import mechanism and presequence evolution.

Prediction of protein subplastid localization and origin with PlastoGram.

Due to their complex history, plastids possess proteins encoded in the nuclear and plastid genome. Moreover, these proteins localize to various subplastid compartments. Since protein localization is associated with its function, prediction of subplastid localization is one of the most important steps in plastid protein annotation, providing insight into their potential function. Therefore, we create a novel manually curated data set of plastid proteins and build an ensemble model for prediction of protein subplastid localization. Moreover, we discuss problems associated with the task, e.g. data set sizes and homology reduction. PlastoGram classifies proteins as nuclear- or plastid-encoded and predicts their localization considering: envelope, stroma, thylakoid membrane or thylakoid lumen; for the latter, the import pathway is also predicted. We also provide an additional function to differentiate nuclear-encoded inner and outer membrane proteins. PlastoGram is available as a web server at https://biogenies.info/PlastoGram and as an R package at https://github.com/BioGenies/PlastoGram . The code used for described analyses is available at https://github.com/BioGenies/PlastoGram-analysis .

The Photosynthetic Adventure of Paulinella Spp.

Paulinella photosynthetic species are unicellular, silica shell-forming amoebas classified into the supergroup Rhizaria. They crawl at the bottom of freshwater and brackish environments with the help of filose pseudopodia. These protists have drawn the attention of the scientific community because of two photosynthetic bodies, called chromatophores, that fill up their cells permitting fully photoautotrophic existence. Paulinella chromatophores, similarly to primary plastids of the Archaeplastida supergroup (including glaucophytes, red algae as well as green algae and land plants), evolved from free-living cyanobacteria in the process of endosymbiosis. Interestingly, these both cyanobacterial acquisitions occurred independently, thereby undermining the paradigm of the rarity of endosymbiotic events. Chromatophores were derived from α-cyanobacteria relatively recently 60-140 million years ago, whereas primary plastids originated from ß-cyanobacteria more than 1.5 billion years ago. Since their acquisition, chromatophore genomes have undergone substantial reduction but not to the extent of primary plastid genomes. Consequently, they have also developed mechanisms for transport of metabolites and nuclear-encoded proteins along with appropriate targeting signals. Therefore, chromatophores of Paulinella photosynthetic species, similarly to primary plastids, are true cellular organelles. They not only show that endosymbiotic events might not be so rare but also make a perfect model for studying the process of organellogenesis. In this chapter, we summarize the current knowledge and retrace the fascinating adventure of Paulinella species on their way to become photoautotrophic organisms.

CancerGram: An Effective Classifier for Differentiating Anticancer from Antimicrobial Peptides.

Antimicrobial peptides (AMPs) constitute a diverse group of bioactive molecules that provide multicellular organisms with protection against microorganisms, and microorganisms with weaponry for competition. Some AMPs can target cancer cells; thus, they are called anticancer peptides (ACPs). Due to their small size, positive charge, hydrophobicity and amphipathicity, AMPs and ACPs interact with negatively charged components of biological membranes. AMPs preferentially permeabilize microbial membranes, but ACPs additionally target mitochondrial and plasma membranes of cancer cells. The preference towards mitochondrial membranes is explained by their membrane potential, membrane composition resulting from α-proteobacterial origin and the fact that mitochondrial targeting signals could have evolved from AMPs. Taking into account the therapeutic potential of ACPs and millions of deaths due to cancer annually, it is of vital importance to find new cationic peptides that selectively destroy cancer cells. Therefore, to reduce the costs of experimental research, we have created a robust computational tool, CancerGram, that uses n-grams and random forests for predicting ACPs. Compared to other ACP classifiers, CancerGram is the first three-class model that effectively classifies peptides into: ACPs, AMPs and non-ACPs/non-AMPs, with AU1U amounting to 0.89 and a Kappa statistic of 0.65. CancerGram is available as a web server and R package on GitHub.