Empirical comparison of web-based antimicrobial peptide prediction tools

Ensemble-AMPPred: Robust AMP Prediction and Recognition Using the Ensemble Learning Method with a New Hybrid Feature for Differentiating AMPs

Antimicrobial peptides (AMPs) are natural peptides possessing antimicrobial activities. These peptides are important components of the innate immune system. They are found in various organisms. AMP screening and identification by experimental techniques are laborious and time-consuming tasks. Alternatively, computational methods based on machine learning have been developed to screen potential AMP candidates prior to experimental verification. Although various AMP prediction programs are available, there is still a need for improvement to reduce false positives (FPs) and to increase the predictive accuracy. In this work, several well-known single and ensemble machine learning approaches have been explored and evaluated based on balanced training datasets and two large testing datasets. We have demonstrated that the developed program with various predictive models has high performance in differentiating between AMPs and non-AMPs. Thus, we describe the development of a program for the prediction and recognition of AMPs using MaxProbVote, which is an ensemble model. Moreover, to increase prediction efficiency, the ensemble model was integrated with a new hybrid feature based on logistic regression. The ensemble model integrated with the hybrid feature can effectively increase the prediction sensitivity of the developed program called Ensemble-AMPPred, resulting in overall improvements in terms of both sensitivity and specificity compared to those of currently available programs.

Design and characterization of new antimicrobial peptides derived from aurein 1.2 with enhanced antibacterial activity

Antimicrobial peptides (AMPs) are promising alternative agents for treating multidrug-resistant bacterial infections. Aurein 1.2 is a natural 13-amino acid AMP with antibacterial activity against Gram-positive bacteria. In this study, we designed three novel AMPs: aurein M1 (A10W), aurein M2 (D4K, E11K), and aurein M3 (A10W, D4K, E11K) to analyze the effect of Trp substitution and enhancement of positive charge on the activity of aurein 1.2. The AMP probability, physicochemical properties, secondary and tertiary structures, and amphipathic structure were predicted by various bioinformatics tools. After the synthesis of the peptides, their antibacterial activity, hemolysis, cytotoxicity, and structural analysis were assayed. Compared to the selectivity of aurein 1.2, the selectivity of aurein M2 and M3 with a net positive charge of +5 was improved 11.30- and 8.00-fold against Gram-positive and -negative bacteria, respectively. The hemolytic activity of aurein M2 was lower than that of aurein 1.2 and M3, while the higher percentage of human fibroblast cells were alive in the presence of aurein M3. Also, the MICs of aurein M3 toward Staphylococcus aureus and Escherichia coli at the physiologic salt were ≤16, which is recommended as a promising candidate for clinical investigation. Circular dichroism analysis indicated an alpha-helical structure in the peptide analogs that is similar to aurein 1.2 in the presence of 10 mM SDS. Therefore, increasing positive charge can be used successfully as an approach for improving the potency and selectivity of AMPs. Moreover, the beneficial effect of Trp substitution depends on its position and the sequence of peptides.

Mining, heterologous expression, purification, antibactericidal mechanism, and application of bacteriocins: A review

Bacteriocins are generally considered as low-molecular-weight ribosomal peptides or proteins synthesized by G⁺ and G^- bacteria that inhibit or kill other related or unrelated microorganisms. However, low yield is an important factor restricting the application of bacteriocins. This paper reviews mining methods, heterologous expression in different systems, the purification technologies applied to bacteriocins, and identification methods, as well as the antibacterial mechanism and applications in three different food systems. Bioinformatics improves the efficiency of bacteriocins mining. Bacteriocins can be heterologously expressed in different expression systems (e.g., Escherichia coli, Lactobacillus, and yeast). Ammonium sulfate precipitation, dialysis membrane, pH-mediated cell adsorption/desorption, solvent extraction, macroporous resin column, and chromatography are always used as purification methods for bacteriocins. The bacteriocins are identified through electrophoresis and mass spectrum. Cell envelope (e.g., cell permeabilization and pore formation) and inhibition of gene expression are common antibacterial mechanisms of bacteriocins. Bacteriocins can be added to protect meat products (e.g., beef and sausages), dairy products (e.g., cheese, milk, and yogurt), and vegetables and fruits (e.g., salad, apple juice, and soybean sprouts). The future research directions are also prospected.

Variational Autoencoder for Generation of Antimicrobial Peptides

Over millennia, natural evolution has allowed for the emergence of countless biomolecules with highly specific roles within natural systems. As seen with peptides and proteins, often evolution produces molecules with a similar function but with variable amino acid composition and structure but diverging from a common ancestor, which can limit sequence diversity. Using antimicrobial peptides as a model biomolecule, we train a generative deep learning algorithm on a database of known antimicrobial peptides to generate novel peptide sequences with antimicrobial activity. Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that show dose-responsive antimicrobial activity. These proof-of-concept studies demonstrate the potential for artificial intelligence-directed methods to generate new antimicrobial peptides and motivate their potential application toward peptide and protein design without the need for exhaustive screening of sequence libraries.

Antimicrobial Activity of Small Synthetic Peptides Based on the Marine Peptide Turgencin A: Prediction of Antimicrobial Peptide Sequences in a Natural Peptide and Strategy for Optimization of Potency

Turgencin A, a potent antimicrobial peptide isolated from the Arctic sea squirt Synoicum turgens, consists of 36 amino acid residues and three disulfide bridges, making it challenging to synthesize. The aim of the present study was to develop a truncated peptide with an antimicrobial drug lead potential based on turgencin A. The experiments consisted of: (1) sequence analysis and prediction of antimicrobial potential of truncated 10-mer sequences; (2) synthesis and antimicrobial screening of a lead peptide devoid of the cysteine residues; (3) optimization of in vitro antimicrobial activity of the lead peptide using an amino acid replacement strategy; and (4) screening the synthesized peptides for cytotoxic activities. In silico analysis of turgencin A using various prediction software indicated an internal, cationic 10-mer sequence to be putatively antimicrobial. The synthesized truncated lead peptide displayed weak antimicrobial activity. However, by following a systematic amino acid replacement strategy, a modified peptide was developed that retained the potency of the original peptide. The optimized peptide StAMP-9 displayed bactericidal activity, with minimal inhibitory concentrations of 7.8 µg/mL against Staphylococcus aureus and 3.9 µg/mL against Escherichia coli, and no cytotoxic effects against mammalian cells. Preliminary experiments indicate the bacterial membranes as immediate and primary targets.

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.

An automatic representation of peptides for effective antimicrobial activity classification

Antimicrobial peptides (AMPs) are a promising alternative to small-molecules-based antibiotics. These peptides are part of most living organisms' innate defense system. In order to computationally identify new AMPs within the peptides these organisms produce, an automatic AMP/non-AMP classifier is required. In order to have an efficient classifier, a set of robust features that can capture what differentiates an AMP from another that is not, has to be selected. However, the number of candidate descriptors is large (in the order of thousands) to allow for an exhaustive search of all possible combinations. Therefore, efficient and effective feature selection techniques are required. In this work, we propose an efficient wrapper technique to solve the feature selection problem for AMPs identification. The method is based on a Genetic Algorithm that uses a variable-length chromosome for representing the selected features and uses an objective function that considers the Mathew Correlation Coefficient and the number of selected features. Computational experiments show that the proposed method can produce competitive results regarding sensitivity, specificity, and MCC. Furthermore, the best classification results are achieved by using only 39 out of 272 molecular descriptors.

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

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.

Progress in the development of antimicrobial peptide prediction tools

Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Antimicrobial peptide identification using multi-scale convolutional network

Background

Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem.

Results

In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy.

Conclusions

Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

Characterization and identification of antimicrobial peptides with different functional activities

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Encodings and models for antimicrobial peptide classification for multi-resistant pathogens

Antimicrobial peptides (AMPs) are part of the inherent immune system. In fact, they occur in almost all organisms including, e.g., plants, animals, and humans. Remarkably, they show effectivity also against multi-resistant pathogens with a high selectivity. This is especially crucial in times, where society is faced with the major threat of an ever-increasing amount of antibiotic resistant microbes. In addition, AMPs can also exhibit antitumor and antiviral effects, thus a variety of scientific studies dealt with the prediction of active peptides in recent years. Due to their potential, even the pharmaceutical industry is keen on discovering and developing novel AMPs. However, AMPs are difficult to verify in vitro, hence researchers conduct sequence similarity experiments against known, active peptides. Unfortunately, this approach is very time-consuming and limits potential candidates to sequences with a high similarity to known AMPs. Machine learning methods offer the opportunity to explore the huge space of sequence variations in a timely manner. These algorithms have, in principal, paved the way for an automated discovery of AMPs. However, machine learning models require a numerical input, thus an informative encoding is very important. Unfortunately, developing an appropriate encoding is a major challenge, which has not been entirely solved so far. For this reason, the development of novel amino acid encodings is established as a stand-alone research branch. The present review introduces state-of-the-art encodings of amino acids as well as their properties in sequence and structure based aggregation. Moreover, albeit a well-chosen encoding is essential, performant classifiers are required, which is reflected by a tendency towards specifically designed models in the literature. Furthermore, we introduce these models with a particular focus on encodings derived from support vector machines and deep learning approaches. Albeit a strong focus has been set on AMP predictions, not all of the mentioned encodings have been elaborated as part of antimicrobial research studies, but rather as general protein or peptide representations.

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform

It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolution of protein‒protein interactions (PPIs) and cellular functions. Owing to the limitation of the experimental identification of self-interacting proteins, it is more and more significant to develop a useful biological tool for the prediction of SIPs from protein sequence information. Therefore, we propose a novel prediction model called RP-FFT that merges the Random Projection (RP) model and Fast Fourier Transform (FFT) for detecting SIPs. First, each protein sequence was transformed into a Position Specific Scoring Matrix (PSSM) using the Position Specific Iterated BLAST (PSI-BLAST). Second, the features of protein sequences were extracted by the FFT method on PSSM. Lastly, we evaluated the performance of RP-FFT and compared the RP classifier with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the human and yeast datasets; after the five-fold cross-validation, the RP-FFT model can obtain high average accuracies of 96.28% and 91.87% on the human and yeast datasets, respectively. The experimental results demonstrated that our RP-FFT prediction model is reasonable and robust.

Impact of human milk pasteurization on the kinetics of peptide release during in vitro dynamic digestion at the preterm newborn stage

Holder pasteurization (62.5 °C, 30 min) of human milk denatures beneficial proteins. The present paper aimed to assess whether this can affect the kinetics of peptide release during digestion at the preterm stage. Raw (RHM) or pasteurized (PHM) human milk were digested in triplicates using an in vitro dynamic system. Mass spectrometry and multivariate statistics were conducted. Pre-proteolysis occurred mostly on β-casein, for which cumulative peptide abundance was significantly greater in PHM over 28% of the hydrolysed sequence. Eight clusters resumed the kinetics of peptide release during digestion, which differed on seven clusters (69% of the 1134 peptides). Clusters associated to the heat-denaturated proteins, lactoferrin and bile salt-stimulated lipase, presented different kinetics of release during digestion, unlike that for β-casein. Some bioactive peptides from β-casein presented significant different abundances between PHM and RHM before digestion (1-18, 185-211) or in during intestinal digestion (154-160, 161-166). Further physiological consequences should be investigated.

Comment on: ‘Empirical comparison of web-based antimicrobial peptide prediction tools’

Supplementary information: Supplementary data are available at Bioinformatics online.

Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries

Background

Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.

Results

By generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.

Conclusions

We developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2514-6) contains supplementary material, which is available to authorized users.

Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach

Background

Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored.

Results

We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides.

Conclusions

The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5030-1) contains supplementary material, which is available to authorized users.

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

Recent insights into structure-function relationships of antimicrobial peptides

The application of antimicrobial peptides (AMPs) in food preservation presents a promising alternative and offers many benefits, such as reducing the use of chemical preservatives, reducing food losses due to spoilage, and development of health-promoting food supplements. The biological activity of AMPs largely dependent on several physicochemical features including charge, the degree of helicity, hydrophobicity, and sequence. The present review provides an overview of the structural classification of AMPs emphasizing the importance of their structural features for biological activity, followed by the description of some antimicrobial mechanism of action. Despite the several hurdles that must be overcome for the exploitation of food-derived AMPs in drug discovery and food systems, the developments discussed in this review offer a taste of future trends in food and pharmaceutical applications of these intriguing molecules. PRACTICAL APPLICATIONS: Numerous AMPs have been reported in recent years as naturally present or released from food proteins upon enzymatic digestion during food processing, fermentation, or gastrointestinal transit. Particularly, food-released AMPs is a promising alternative to satisfy consumer demands for safe, ready-to-eat, extended shelf-life, fresh-tasting, and minimally processed foods, without chemical additives. The potential of several AMPs to inhibit foodborne pathogens is increasingly studied in various food matrices including dairy products, meat, fruits, and beverages. Although extensive progress has been made with respect to our understanding of AMPs structure/function, additional thorough investigation of the factors influencing peptide activity is required. The time has now come for the development of nutraceuticals and pharmaceutical products containing food-derived AMPs. Despite the several hurdles that must be overcome for the exploitation of AMPs, the features and developments discussed in this review offer a taste of future trends in food and pharmaceutical applications of these intriguing molecules.

Comparative peptidomic profile between human hypertrophic scar tissue and matched normal skin for identification of endogenous peptides involved in scar pathology

Endogenous peptides recently attract increasing attention for their participation in various biological processes. Their roles in the pathogenesis of human hypertrophic scar remains poorly understood. In this study, we used liquid chromatography-tandem mass spectrometry to construct a comparative peptidomic profiling between human hypertrophic scar tissue and matched normal skin. A total of 179 peptides were significantly differentially expressed in human hypertrophic scar tissue, with 95 upregulated and 84 downregulated peptides between hypertrophic scar tissue and matched normal skin. Further bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis) indicated that precursor proteins of these differentially expressed peptides correlate with cellular process, biological regulation, cell part, binding and structural molecule activity ribosome, and PPAR signaling pathway occurring during pathological changes of hypertrophic scar. Based on prediction database, we found that 78 differentially expressed peptides shared homology with antimicrobial peptides and five matched known immunomodulatory peptides. In conclusion, our results show significantly altered expression profiles of peptides in human hypertrophic scar tissue. These peptides may participate in the etiology of hypertrophic scar and provide beneficial scheme for scar evaluation and treatments.

Designing Antibacterial Peptides with Enhanced Killing Kinetics

Antimicrobial peptides (AMPs) are gaining attention as substitutes for antibiotics in order to combat the risk posed by multi-drug resistant pathogens. Several research groups are engaged in design of potent anti-infective agents using natural AMPs as templates. In this study, a library of peptides with high sequence similarity to Myeloid Antimicrobial Peptide (MAP) family were screened using popular online prediction algorithms. These peptide variants were designed in a manner to retain the conserved residues within the MAP family. The prediction algorithms were found to effectively classify peptides based on their antimicrobial nature. In order to improve the activity of the identified peptides, molecular dynamics (MD) simulations, using bilayer and micellar systems could be used to design and predict effect of residue substitution on membranes of microbial and mammalian cells. The inference from MD simulation studies well corroborated with the wet-lab observations indicating that MD-guided rational design could lead to discovery of potent AMPs. The effect of the residue substitution on membrane activity was studied in greater detail using killing kinetic analysis. Killing kinetics studies on Gram-positive, negative and human erythrocytes indicated that a single residue change has a drastic effect on the potency of AMPs. An interesting outcome was a switch from monophasic to biphasic death rate constant of Staphylococcus aureus due to a single residue mutation in the peptide.

Recurrent Neural Network Model for Constructive Peptide Design

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

Endogenous peptides profiles of human infantile hemangioma tissue and their clinical significance for treatment

Endogenous peptides play crucial roles in various biological processes. Their effects on the pathogenesis of human infantile hemangioma remains poorly understood. In this study, we construct a comparative peptidomic profiling between human infantile hemangioma tissue and matched normal skin using liquid chromatography-tandem mass spectrometry. A total of 192 peptides were significantly differentially expressed in human infantile hemangioma tissue, with 182 upregulated, and 10 downregulated peptides between infantile hemangioma tissue and matched normal skin. Performing bioinformatics analysis (Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis), we found that precursor proteins of these differentially expressed peptides correlate with metabolic process, biological regulation, binding, catalytic activity, and pathways in cancer occurring during pathological changes of infantile hemangioma. Furthermore, 89 differentially expressed peptides shared homology with antimicrobial peptides and 13 matched known immunomodulatory peptides based on prediction database. In conclusion, our results reveal significantly altered expression profiles of peptides in human infantile hemangioma tissue. These peptides may participate in the etiology of infantile hemangioma and provide beneficial scheme for clinical treatments.

Identification of bacteriocins secreted by the probiotic Lactococcus lactis following microwave-assisted acid hydrolysis (MAAH), amino acid content analysis, and bioinformatics

A novel, generally applicable method of identifying peptides using HPLC, microwave-assisted acid hydrolysis (MAAH), and bioinformatics is described. Method validation was performed on bacteriocins-antibacterial peptides produced by probiotic bacteria-using nine different bacteriocin isolates secreted by the probiotic Lactococcus lactis. Calibration curves were constructed for 23 amino acid PTH derivatives, and analysis was performed using norleucine as the internal standard. Validation of amino acid analysis performed in the range 2.5-100 nmol/mL indicated excellent method linearity, while the LODs ranged from 0.17 to 2.88 nmol/mL and the LOQs from 0.51 to 8.75 nmol/mL. The MAAH method was developed by irradiating nisaplin for various durations at 700 W, with 7 min providing the best results. The amino acid content of each sample was estimated following the application of MAAH to ten different samples. The bacteriocins in our samples were identified using the UniProt database. Eight of nine peptides were identified as UniProt entries: nisin A (P13068), nisin Z (P29559), I4DSZ9, OB7236, P36499, OB7237, A0A0M7BH60, and T2C9F0. The phylogenetic tree was constructed for nisin A and nisin Z using the multiple sequence aligning tool Clustal Ω. The identified nisin types presented excellent correlation with their ModBase-predicted structures. The present method gives true, precise, and rapid results, and requires only standard technical equipment. Our results suggest that the present approach can facilitate the discovery of novel bacteriocins and provide useful information on not only the amino acid contents of peptides but also the evolution of protein biology. Graphical abstract Identification of eight bacteriocins secreted by the probiotic L. lactis, following microwave assisted acid hydrolysis (MAAH), amino acid content analysis of each sample with HPLC-DAD and bioinformatics analysis using Uniprot, Clustal Ω and ModBase.