Empirical comparison of web-based antimicrobial peptide prediction tools

Designing of a novel chimeric antimicrobial peptide against Acinetobacter baumannii using three different bioinformatics methods and evaluation of its antimicrobial activity in vitro

Background and purpose

The study aimed to design new chimeric antimicrobial peptides targeting Acinetobacter baumannii, a widespread and growing global concern due to antibiotic resistance. Three bioinformatics-based methods were utilized for this purpose.

Experimental approach

To design new chimeric peptides targeting Acinetobacter baumannii, a group of peptides were initially selected and divided into two categories based on their scores and performance. The peptides were then combined through 3 methods: 1. combining sequences based on their secondary structure using GOR IV software; 2. grouping only the amino acid sequences involved in the formation of the target peptide helix structure using Accelrys DS visualizer software; and 3. combining the most similar parts of the peptides in terms of amino acid type and order using online AntiBP2 software. The sequence length was optimized, and some amino acids were substituted.

Findings/Results

The M-CIT peptide was selected for synthesis in the first method, but it did not show significant activity against the target bacteria (MIC = 187.5 μM and MBC = 375 μM). In the second method, no suitable score was observed. However, the M-PEX12 peptide was synthesized in the second method, demonstrating antimicrobial activity against A. baumannii (MIC = 33.1 μM and MBC = 41.4 μM).

Conclusion and implications

Three methods were evaluated for designing new chimeric peptides, and the third method, which involved modifying the number of amino acids in the parental peptides while maintaining their similarity, was found to be the most suitable.

Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation model

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach, for swiftly designing antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 micrograms per milliliter against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.

Exploring Tenebrio molitor as a source of low-molecular-weight antimicrobial peptides using a n in silico approach: correlation of molecular features and molecular docking

BACKGROUND

Yellow mealworm (Tenebrio molitor) larvae are increasingly recognized as a potential source of bioactive peptides due to their high protein content. Antimicrobial peptides from sustainable sources are a research topic of interest. This study aims to characterize the peptidome of T. molitor flour and an Alcalase-derived hydrolysate, and to explore the potential presence of antimicrobial peptides using in silico analyses, including prediction tools, molecular docking and parameter correlations.

RESULTS

T. molitor protein was hydrolysed using Alcalase, resulting in a hydrolysate (TMH10A) with a 10% degree of hydrolysis. The peptidome was analysed using LC-TIMS-MS/MS, yielding over 6000 sequences. These sequences were filtered using the PeptideRanker tool, selecting the top 100 sequences with scores >0.8. Bioactivity predictions indicated that specific peptides, particularly WLNSKGGF and GFIPYEPFLKKMMA, showed significant antimicrobial potential, particularly against bacteria, fungi and viruses. Correlations were found between antifungal activity and physicochemical properties such as net charge, hydrophobicity and isoelectric point.

CONCLUSIONS

The study identified specific T. molitor-derived peptides with strong predicted antimicrobial activity through in silico analysis. These peptides, particularly WLNSKGGF and GFIPYEPFLKKMMA, might offer potential applications in food safety and healthcare. Further experimental validation is required to confirm their efficacy. © 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Human microbiome derived synthetic antimicrobial peptides with activity against Gram-negative, Gram-positive, and antibiotic resistant bacteria

The prevalence of antibacterial resistance has become one of the major health threats of modern times, requiring the development of novel antibacterials. Antimicrobial peptides are a promising source of antibiotic candidates, mostly requiring further optimization to enhance druggability. In this study, a series of new antimicrobial peptides derived from lactomodulin, a human microbiome natural peptide, was designed, synthesized, and biologically evaluated. Within the most active region of the parent peptide, linear peptide LM6 with the sequence LSKISGGIGPLVIPV-NH2 and its cyclic derivatives LM13a and LM13b showed strong antibacterial activity against Gram-positive bacteria, including resistant strains, and Gram-negative bacteria. The peptides were found to have a rapid onset of bactericidal activity and transmission electron microscopy clearly shows the disintegration of the cell membrane, suggesting a membrane-targeting mode of action.

Comprehensive Assessment of BERT-Based Methods for Predicting Antimicrobial Peptides

In recent years, the prediction of antimicrobial peptides (AMPs) has gained prominence due to their high antibacterial activity and reduced susceptibility to drug resistance, making them potential antibiotic substitutes. To advance the field of AMP recognition, an increasing number of natural language processing methods are being applied. These methods exhibit diversity in terms of pretraining models, pretraining data sets, word vector embeddings, feature encoding methods, and downstream classification models. Here, we provide a comprehensive survey of current BERT-based methods for AMP prediction. An independent benchmark test data set is constructed to evaluate the predictive capabilities of the surveyed tools. Furthermore, we compared the predictive performance of these computational methods based on six different AMP public databases. LM_pred (BFD) outperformed all other surveyed tools due to abundant pretraining data set and the unique vector embedding approach. To avoid the impact of varying training data sets used by different methods on prediction performance, we performed the 5-fold cross-validation experiments using the same data set, involving retraining. Additionally, to explore the applicability and generalization ability of the models, we constructed a short peptide data set and an external data set to test the retrained models. Although these prediction methods based on BERT can achieve good prediction performance, there is still room for improvement in recognition accuracy. With the continuous enhancement of protein language model, we proposed an AMP prediction method based on the ESM-2 pretrained model called iAMP-bert. Experimental results demonstrate that iAMP-bert outperforms other approaches. iAMP-bert is freely accessible to the public at http://iamp.aielab.cc/.

AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning

In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.

An efficient hybrid deep learning architecture for predicting short antimicrobial peptides

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

Characterization of the antibacterial and opsonic functions of the antimicrobial peptide LvCrustinVI from Litopenaeus vannamei

Microbial drug resistance is becoming increasingly severe due to antibiotic abuse. The development and utilization of antimicrobial peptides is one of the important ways to solve this difficult problem. Crustins are a family of antimicrobial peptides that play important roles in the innate immune system of crustaceans. Several types of crustins exist in shrimp and their activities vary greatly. In the present study, we studied the immune function of one newly identified crustin and found that the type VI crustin encoding gene in Litopenaeus vannamei (LvCrustinVI) was mainly expressed in gills. Its expression was significantly up-regulated after Vibrio parahaemolyticus infection and knockdown of the gene promoted Vibrio proliferation in the hepatopancreas of shrimp, indicating that LvCrustinVI was involved in pathogens infection. The recombinant LvCrustinVI (rLvCrustinVI) showed strong inhibitory activities against both Gram-negative and Gram-positive bacteria, and exhibited binding activities with the bacteria and bacterial polysaccharides including Glu, LPS and PGN. In the presence of Ca2+, rLvCrustinVI showed a strong agglutination effect on V. parahaemolyticus and could significantly enhance the phagocytic ability of shrimp hemocytes against V. parahaemolyticus. In conclusion, LvCrustinVI played important roles as antimicrobial peptide and opsonin in the innate immune defense of L. vannamei. The study enriched our understanding of the functional activity of Crustin and provides an important basis for the development and utilization of antimicrobial peptides.

A novel stacking-based predictor for accurate prediction of antimicrobial peptides

Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel STacking-based ensemble learning framework for AntiMicrobial Peptide (STAMP) prediction. First, we constructed 84 different baseline models by using 12 different feature encoding schemes and 7 popular machine learning algorithms. Second, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, based on the feature selection strategy, we determined the optimal probabilistic feature vector, which was further utilized for the construction of our stacked model. Resultantly, the STAMP predictor achieved excellent performance during cross-validation with an accuracy and Matthew's correlation coefficient of 0.930 and 0.860, respectively. The corresponding metrics during the independent test were 0.710 and 0.464, respectively. Overall, STAMP achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, STAMP is expected to assist community-wide efforts in identifying AMPs and will contribute to the development of novel therapeutic methods and drug-design for immunity.

A novel bioinformatic method for the identification of antimicrobial peptides in metagenomes

AIMS

This study aimed to develop a new bioinformatic approach for the identification of novel antimicrobial peptides (AMPs), which did not depend on sequence similarity to known AMPs held within databases, but on structural mimicry of another antimicrobial compound, in this case an ultrashort, synthetic, cationic lipopeptide (C12-OOWW-NH2).

METHODS AND RESULTS

When applied to a collection of metagenomic datasets, our outlined bioinformatic method successfully identified several short (8-10aa) functional AMPs, the activity of which was verified via disk diffusion and minimum inhibitory concentration assays against a panel of 12 bacterial strains. Some peptides had activity comparable to, or in some cases, greater than, those from published studies that identified AMPs using more conventional methods. We also explored the effects of modifications, including extension of the peptides, observing an activity peak at 9-12aa. Additionally, the inclusion of a C-terminal amide enhanced activity in most cases. Our most promising candidate (named PB2-10aa-NH2) was thermally stable, lipid-soluble, and possessed synergistic activity with ethanol but not with a conventional antibiotic (streptomycin).

CONCLUSIONS

While several bioinformatic methods exist to predict AMPs, the approach outlined here is much simpler and can be used to quickly scan huge datasets. Searching for peptide sequences bearing structural similarity to other antimicrobial compounds may present a further opportunity to identify novel AMPs with clinical relevance, and provide a meaningful contribution to the pressing global issue of AMR.

BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier

The use of bacteriocins has emerged as a propitious strategy in the development of new drugs to combat antibiotic resistance, given their ability to kill bacteria with both broad and narrow natural spectra. Hence, a compelling requirement arises for a precise and efficient computational model that can accurately predict novel bacteriocins. Machine learning's ability to learn patterns and features from bacteriocin sequences that are difficult to capture using sequence matching-based methods makes it a potentially superior choice for accurate prediction. A web application for predicting bacteriocin was created in this study, utilizing a machine learning approach. The feature sets employed in the application were chosen using alternating decision tree (ADTree), genetic algorithm (GA), and linear support vector classifier (linear SVC)-based feature evaluation methods. Initially, potential features were extracted from the physicochemical, structural, and sequence-profile attributes of both bacteriocin and non-bacteriocin protein sequences. We assessed the candidate features first using the Pearson correlation coefficient, followed by separate evaluations with ADTree, GA, and linear SVC to eliminate unnecessary features. Finally, we constructed random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), k-nearest neighbors (KNN), and Gaussian naïve Bayes (GNB) models using reduced feature sets. We obtained the overall top performing model using SVM with ADTree-reduced features, achieving an accuracy of 99.11% and an AUC value of 0.9984 on the testing dataset. We also assessed the predictive capabilities of our best-performing models for each reduced feature set relative to our previously developed software solution, a sequence alignment-based tool, and a deep-learning approach. A web application, titled BPAGS (Bacteriocin Prediction based on ADTree, GA, and linear SVC), was developed to incorporate the predictive models built using ADTree, GA, and linear SVC-based feature sets. Currently, the web-based tool provides classification results with associated probability values and has options to add new samples in the training data to improve the predictive efficacy. BPAGS is freely accessible at https://shiny.tricities.wsu.edu/bacteriocin-prediction/.

Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence

The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.

Identification of proteoforms of short open reading frame-encoded peptides in Blautia producta under different cultivation conditions

IMPORTANCE

The identification of short open reading frame-encoded peptides (SEP) and different proteoforms in single cultures of gut microbes offers new insights into a largely neglected part of the microbial proteome landscape. This is of particular importance as SEP provide various predicted functions, such as acting as antimicrobial peptides, maintaining cell homeostasis under stress conditions, or even contributing to the virulence pattern. They are, thus, taking a poorly understood role in structure and function of microbial networks in the human body. A better understanding of SEP in the context of human health requires a precise understanding of the abundance of SEP both in commensal microbes as well as pathogens. For the gut beneficial B. producta, we demonstrate the importance of specific environmental conditions for biosynthesis of SEP expanding previous findings about their role in microbial interactions.

Hierarchical machine learning model predicts antimicrobial peptide activity against Staphylococcus aureus

Introduction: Staphylococcus aureus is a dangerous pathogen which causes a vast selection of infections. Antimicrobial peptides have been demonstrated as a new hope for developing antibiotic agents against multi-drug-resistant bacteria such as S. aureus. Yet, most studies on developing classification tools for antimicrobial peptide activities do not focus on any specific species, and therefore, their applications are limited. Methods: Here, by using an up-to-date dataset, we have developed a hierarchical machine learning model for classifying peptides with antimicrobial activity against S. aureus. The first-level model classifies peptides into AMPs and non-AMPs. The second-level model classifies AMPs into those active against S. aureus and those not active against this species. Results: Results from both classifiers demonstrate the effectiveness of the hierarchical approach. A comprehensive set of physicochemical and linguistic-based features has been used, and after feature selection steps, only some physicochemical properties were selected. The final model showed the F1-score of 0.80, recall of 0.86, balanced accuracy of 0.80, and specificity of 0.73 on the test set. Discussion: The susceptibility to a single AMP is highly varied among different target species. Therefore, it cannot be concluded that AMP candidates suggested by AMP/non-AMP classifiers are able to show suitable activity against a specific species. Here, we addressed this issue by creating a hierarchical machine learning model which can be used in practical applications for extracting potential antimicrobial peptides against S. aureus from peptide libraries.

iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.

Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy

Antibacterial peptides can be a potential game changer in the fight against antibiotic resistance. In order for these peptides to become successful antibiotic alternatives, it is essential that they possess high efficacy in addition to just being antibacterial. In this study, we have developed a two-level SVM-based binary classification approach to predict the antibacterial activity of a given peptide (model 1) and thereafter classify its antibacterial efficacy as high/low (model 2) with respect to minimum inhibitory concentration (MIC) values against Staphylococcus aureus, one of the most common pathogens. Based on charge and hydrophobicity of amino acids, we developed a sequence-based combined charge and hydrophobicity-guided triad (CHT) as a new method for obtaining features of any peptide. Model 1 with a combination of CHT and amino acid composition (AAC) as the feature representation method resulted in the highest accuracy of 96.7%. Model 2 with CHT as the feature representation method yielded the highest accuracy of 70.9%. Thus, CHT is found to be a potential feature representation method for classifying antibacterial peptides based on both activity and efficacy. Furthermore, we have also used an explainable machine learning algorithm to extract various insights from these models. These insights are found to be in excellent agreement with experimental findings reported in the literature, thus enhancing the dependability of the proposed models.

In silico characterization of differentially expressed short-read nucleotide sequences identified in dieback stress-induced transcriptomic analysis reveals their role as antimicrobial peptides

We investigated the in silico characterization of short-length nucleotide sequences that were differentially expressed in dieback stress-induced transcriptomic analysis. They displayed homology with C-terminal flanking peptides and defensins-like proteins, revealing their antimicrobial activity. Their predicted fingerprints displayed protein signatures related to antimicrobial peptides. These short-length RGAs have been shown to possess structural motifs such as APLT P-type ATPase, casein kinase II (CK2), protein kinase 3, protein kinase C (PKC), and N-glycosylation site that are the attributes of disease resistance genes. The prediction of arginine and lysine residues in active binding sites in ligand docking analysis prophesied them as antimicrobial peptides due to their strong relation with antimicrobial activity. The in silico structural-functional characterization has predicted their role in resistance against microbial pathogens. Moreover, the predicted antimicrobial peptide regions showed their homology with the signature domain of PR-5-like protein and AMP family Thaumatin.

The design and functional characterization of a novel hybrid antimicrobial peptide from Esculentin-1a and melittin

Antimicrobial agents are one of the most widely used drugs in medicine. In the last fifty years, the misuse of these agents caused the emergence of resistant strains of bacteria that led to an increase in life-threatening infections. The need to develop new agents has become a priority, and antimicrobial peptides attained high consideration. The antimicrobial activities of a novel In-house designed hybrid cationic peptide (BKR1) were studied against different strains of Gram-negative bacteria. This was done using the broth dilution method as outlined by the Clinical and Laboratory Institute (CLSI). Checkerboard assy was employed to investigate the synergistic activity of BKR1 peptide with four antibiotics (Levofloxacin, chloramphenicol, rifampicin, and ampicillin). Finally, the cytotoxicity of BKR1 was evaluated against human blood cells and mammalian kidney cells (Vero cells). BKR1 displayed bactericidal activity against tested strains of Gram-negative bacteria, with zero hemolytic effects. It also acts as a strong adjuvant with levofloxacin, chloramphenicol, and rifampicin against resistant strains of P. aeruginosa and E. coli. This study represents the design and elucidation of the antimicrobial activities of a novel hybrid antimicrobial peptide named (BKR1). Our results indicate thar BKR1 is a promising candidate to treat resistant infectious diseases individually or as an adjuvant with conventional antibiotics.

Peptidomics analysis of Jiang-Flavor Daqu from high-temperature fermentation to mature and in different preparation season

Jiang-Flavor Daqu (JFDQ) is a grain-type fermented starter for brewing Chinese liquor. Peptides, the metabolites of proteins in JFDQ, are important for the quality and flavor of JFDQ or even the liquor. The peptide variations in the progress of JFDQ preparation were investigated using RPLC-MS/MS. The JFDQ after high-temperature fermenting (HTF_SU) and after ripening (M_SU), as well as the mature JFDQ prepared in spring (M_SP) and in summer (M_SU), were compared respectively. These two groups were investigated from peptides, precursor proteins, abundance, interactions, and potential antimicrobial peptides (pAMPs). A total of 177, 158, and 262 peptides from HTF_SU, M_SP, and M_SU were identified, respectively. Significant differences (P < 0.01) in the abundance of shared peptides were found in different fermentation stage group (HTF_M), and stronger positive correlations were observed in different preparation season group (MSP_MSU). The interactions of the shared peptides in HTF_M and in MSP_MSU were investigated respectively. In addition, 8 pAMPs in HTF_SU, 5 in M_SP, and 22 in M_SU were predicted using CAMPR3, and their core functional regions were analyzed. This systematic study demonstrated the influences of fermentation stage and preparation season on the peptide profiles in JFDQ, which would provide theoretical guidance and be helpful for JFDQ production. SIGNIFICANCE: Peptidomics analysis showed that the peptide profiles of JFDQ varied in different fermentation stages and different preparation seasons, which mainly resulted from the peptides with high abundance, high interaction degrees, and potential antimicrobial activity, as well as the important precursor proteins such as glutens. This systematic study would benefit for the insufficiency of peptide research of JFDQ till now, and provide theoretical guidance for JFDQ production.

Discovery and Mechanism of Action of a Novel Antimicrobial Peptide from an Earthworm

The robust innate immune system of the earthworm provides a potential source of natural antimicrobial peptides (AMPs). However, the cost and high rediscovery rate of direct separation and purification limits their discovery. Genome sequencing of numerous earthworm species facilitates the discovery of new antimicrobial peptides. Through predicting potential antimicrobial peptides in the open reading frames of the Eisenia andrei genome and sequence optimization, a novel antimicrobial peptide, named EWAMP-R (RIWWSGGWRRWRW), was identified. EWAMP-R demonstrated good activity against various bacteria, including drug-resistant strains. The antibacterial mechanisms of EWAMP-R were explored through molecular simulation and wet-laboratory experiments. These experiments demonstrated that the bacterial membrane may be one of the targets of EWAMP-R but that there may be different interactions with Gram-negative and Gram-positive bacterial membranes. EWAMP-R can disrupt bacterial membrane integrity; however, at low concentrations, it appears that EWAMP-R may get through the membrane of Escherichia coli instead of damaging it directly, implying the existence of a secondary response. Gene expression studies identified that in E. coli, only the apoptosis-like cell death (ALD) pathway was activated, while in Staphylococcus aureus, the MazEF pathway was also upregulated, limiting the influence of the ALD pathway. The different antimicrobial actions against Gram-positive and -negative bacteria can provide important information on the structure-activity relationship of AMPs and facilitate AMP design with higher specificity. This study identified a new source of antibacterial agents that has the potential to address the increasingly serious issue of antibiotic resistance. IMPORTANCE Drug-resistant bacteria are a great threat to public health and drive the search for new antibacterial agents. The living environment of earthworms necessitates a strong immune system, and therefore, they are potentially a rich resource of novel antibiotics. A novel AMP, EWAMP-R, with high antibacterial activity was found through in silico analysis of the Eisenia andrei genome. Molecular analysis investigating the interactions between EWAMP-R and the cell membrane demonstrated the importance of tryptophan and arginine residues to EWAMP-R activity. Additionally, the different secondary responses found between E. coli and S. aureus were in accordance with a common phenomenon where some antibacterial agents only target specific species of bacteria. These results provided useful molecular information to support further AMP research and design. Our study expands the sources of antimicrobial peptides and also helps to explain the adaptability of earthworms to their environment.

Thinking on the Construction of Antimicrobial Peptide Databases: Powerful Tools for the Molecular Design and Screening

With the accelerating growth of antimicrobial resistance (AMR), there is an urgent need for new antimicrobial agents with low or no AMR. Antimicrobial peptides (AMPs) have been extensively studied as alternatives to antibiotics (ATAs). Coupled with the new generation of high-throughput technology for AMP mining, the number of derivatives has increased dramatically, but manual running is time-consuming and laborious. Therefore, it is necessary to establish databases that combine computer algorithms to summarize, analyze, and design new AMPs. A number of AMP databases have already been established, such as the Antimicrobial Peptides Database (APD), the Collection of Antimicrobial Peptides (CAMP), the Database of Antimicrobial Activity and Structure of Peptides (DBAASP), and the Database of Antimicrobial Peptides (dbAMPs). These four AMP databases are comprehensive and are widely used. This review aims to cover the construction, evolution, characteristic function, prediction, and design of these four AMP databases. It also offers ideas for the improvement and application of these databases based on merging the various advantages of these four peptide libraries. This review promotes research and development into new AMPs and lays their foundation in the fields of druggability and clinical precision treatment.

Polypharmacological Cell-Penetrating Peptides from Venomous Marine Animals Based on Immunomodulating, Antimicrobial, and Anticancer Properties

Complex pathological diseases, such as cancer, infection, and Alzheimer's, need to be targeted by multipronged curative. Various omics technologies, with a high rate of data generation, demand artificial intelligence to translate these data into druggable targets. In this study, 82 marine venomous animal species were retrieved, and 3505 cryptic cell-penetrating peptides (CPPs) were identified in their toxins. A total of 279 safe peptides were further analyzed for antimicrobial, anticancer, and immunomodulatory characteristics. Protease-resistant CPPs with endosomal-escape ability in Hydrophis hardwickii, nuclear-localizing peptides in Scorpaena plumieri, and mitochondrial-targeting peptides from Synanceia horrida were suitable for compartmental drug delivery. A broad-spectrum S. horrida-derived antimicrobial peptide with a high binding-affinity to bacterial membranes was an antigen-presenting cell (APC) stimulator that primes cytokine release and naïve T-cell maturation simultaneously. While antibiofilm and wound-healing peptides were detected in Synanceia verrucosa, APC epitopes as universal adjuvants for antiviral vaccination were in Pterois volitans and Conus monile. Conus pennaceus-derived anticancer peptides showed antiangiogenic and IL-2-inducing properties with moderate BBB-permeation and were defined to be a tumor-homing peptide (THP) with the ability to inhibit programmed death ligand-1 (PDL-1). Isoforms of RGD-containing peptides with innate antiangiogenic characteristics were in Conus tessulatus for tumor targeting. Inhibitors of neuropilin-1 in C. pennaceus are proposed for imaging probes or therapeutic delivery. A Conus betulinus cryptic peptide, with BBB-permeation, mitochondrial-targeting, and antioxidant capacity, was a stimulator of anti-inflammatory cytokines and non-inducer of proinflammation proposed for Alzheimer's. Conclusively, we have considered the dynamic interaction of cells, their microenvironment, and proportional-orchestrating-host- immune pathways by multi-target-directed CPPs resembling single-molecule polypharmacology. This strategy might fill the therapeutic gap in complex resistant disorders and increase the candidates' clinical-translation chance.

Genome-wide identification, evolutionary analysis, and antimicrobial activity prediction of CC chemokines in allotetraploid common carp, Cyprinus carpio

Chemokines are a group of secreted small molecules which are essential for cell migration in physiological and pathological conditions by binding to specific chemokine receptors. They are structurally classified into five groups, namely CXC, CC, CX3C, XC and CX. CC chemokine group is the largest one among them. In this study, we identified and characterized 61 CC chemokines from allotetraploid common carp (Cyprinus carpio). The sequence analyses showed that the majority of CC chemokines had an N-terminal signal peptide, and an SCY domain, and all CC chemokines were located in the extracellular region. Phylogenetic, evolutionary and syntenic analyses confirmed that CC chemokines were annotated as 11 different types (CCL19, CCL20, CCL25, CCL27, CCL32, CCL33, CCL34, CCL35, CCL36, CCL39, and CCL44), which exhibited unique gene arrangement pattern and chromosomal location respectively. Furthermore, genome synteny analyses between common carp and four representative teleost species indicated expansion of common carp CC chemokines resulted from the whole genome duplication (WGD) event. Additionally, the continuous evolution of gene CCL25s in teleost afforded a novel viewpoint to explain the WGD event in teleost. Then, we predicted the three-dimensional structures and probable function regions of common carp CC chemokines. All the CC chemokines core structures were constituted of an N-loop, a three-stranded β-sheet, and a C-terminal helix. Finally, 43 CC chemokines were predicted to have probable general antimicrobial activity. Their tertiary structures, cationic and amphiphilic physicochemical property supported the viewpoint. To verify the prediction, six recombinant CCL19s proteins were prepared and the antibacterial activity against Escherichia coli and Aeromonas hydrophila were verified. The results supported our prediction that rCCL19a.1s (rCCL19a.1_a, rCCL19a.1_b) and rCCL19bs (rCCL19b_a, rCCL19b_b), especially rCCL19bs, exhibited extremely significant inhibition to the growth of both E. coli and A. hydrophila. On the contrary, two rCCL19a.2s had no significant inhibitory effect. These studies suggested that CC chemokines were essential in immune system evolution and not monofunctional during pathogen infection.

Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs

Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.

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.

Prediction and characterization of a novel hemoglobin-derived mutant peptide (mTgHbP7) from Tegillarca granosa

The hemoglobin (Hb) is identified in Tegillarca granosa and its derived peptides have been proved to possess antibacterial activity against gram-positive and gram-negative bacteria. In this study, we identified a series of novel antimicrobial peptides (AMPs) and artificially mutated AMPs derived from subunits of T. granosa Hbs, among which, a mutant T. granosa hemoglobin peptide (mTgHbP) mTgHbP7, was proved to possess predominant antibacterial activity against three bacteria strains (Vibrio alginolyticus, V. parahaemolyticus and Escherichia coli). Besides, mTgHbP7 was predicted to form α-helical structure, which was known to be an important feature of bactericidal AMPs. Furthermore, upon contact with HEK293 cell line, we confirmed that mTgHbP7 had no cytotoxicity to mammalian cell even at a high concentration of 160 μM. Therefore, the findings reported here provide a rationalization for antimicrobial peptide prediction and optimization from mollusk hemoglobin, which will be useful for future development of antimicrobial agents.

Humanizing plant-derived snakins and their encrypted antimicrobial peptides

Due to safety restrictions, plant-derived antimicrobial peptides (AMPs) need optimization to be consumed beyond preservatives. Herein, 175 GASA-domain-containing snakins were analyzed. Factors including charge, hydrophobicity, helicity, hydrophobic moment (μH), folding enthalpy, folding heat capacity, folding free energy, therapeutic index, allergenicity, and bitterness were considered. The most optimal snakins for oral consumption as preservatives were from Cajanus cajan, Cucumis melo, Durio zibethinus, Glycine soja, Herrania umbratica, and Ziziphus jujuba. Virtual digestion of snakins predicted ACE1 and DPPIV inhibitory as dominant effects upon oral use with antihypertensive and antidiabetic properties. To be applied as a therapeutic in parenteral administration, snakins were browsed for short 20-mer encrypted fragments that were non-toxic or with eliminated toxicity using directed mutagenesis yet retaining the AMP property. The most promising 20-mer AMPs were Mr-SNK2-1a in Morella rubra with BBB permeation, Na-SNK2-2a(C¹⁸W), and Na-SNK2-2b(C¹⁶F) from Nicotiana attenuata. These AMPs were cell-penetrating peptides (CPPs), with a charge of +6, a μH of about 0.40, and a Boman-index higher than 2.48 Kcalmol^-1. Na-SNK2-2a(C¹⁸W) had putative activity against gram-negative bacteria with MIC lower than 25 μgml^-1, and Na-SNK2-2b(C¹⁶F) was a potential anti-HIV with an IC₅₀ of 3.04 μM. Other 20-mer AMPs, such as Cc-SNK1-2a from Cajanus cajan displayed an anti-HCV property with an IC₅₀ of 13.91 μM. While Si-SNK2-3a(C¹⁷P) from Sesamum indicum was a cationic anti-angiogenic CPP targeting the acidic microenvironment of tumors, Cme-SNK2-1a(C¹¹F) from Cucumis melo was an immunomodulator CPP applicable as a vaccine adjuvant. Because of combined mechanisms, investigating cysteine-rich peptides can nominate effective biotherapeutics.

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

Characterization of the Dual Functions of LvCrustinVII from Litopenaeus vannamei as Antimicrobial Peptide and Opsonin

Crustin are a family of antimicrobial peptides that play an important role in protecting against pathogens infection in the innate immune system of crustaceans. Previously, we identified several novel types of crustins, including type VI and type VII crustins. However, their immune functions were still unclear. In the present study, the immune function of type VII crustin LvCrustinVII were investigated in Litopenaeus vannamei. LvCrustinVII was wildly expressed in all tested tissues, with relatively high expression levels in hepatopancreas, epidermis and lymphoid organ. Upon Vibrio parahaemolyticus infection, LvCrustinVII was significantly upregulated in hepatopancreas. Recombinant LvCrustinVII (rLvCrustinVII) showed strong inhibitory activities against Gram-negative bacteria Vibrio harveyi and V. parahaemolyticus, while weak activities against the Gram-positive bacteria Staphylococcus aureus. Binding assay showed that rLvCrustinVII could bind strongly to V. harveyi and V. parahaemolyticus, as well as the cell wall components Glu, LPS and PGN. In the presence of Ca²⁺, rLvCrustinVII could agglutinate V. parahaemolyticus and enhance hemocyte phagocytosis. The present data partially illustrate the immune function of LvCrustinVII, which enrich our understanding on the functional mechanisms of crustins and provide useful information for application of this kind of antimicrobial peptides.

AMP0: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning

Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemical assays. Computational techniques can be used for rapid screening of promising antimicrobial peptide candidates prior to testing in the wet lab. The vast majority of existing antimicrobial peptide predictors are non-targeted in nature, i.e., they can predict whether a given peptide sequence is antimicrobial, but they are unable to predict whether the sequence can target a particular microbial species. In this work, we have used zero and few shot machine learning to develop a targeted antimicrobial peptide activity predictor called AMP₀. The proposed predictor takes the sequence of a peptide and any N/C-termini modifications together with the genomic sequence of a microbial species to generate targeted predictions. Cross-validation results show that the proposed scheme is particularly effective for targeted antimicrobial prediction in comparison to existing approaches and can be used for screening potential antimicrobial peptides in a targeted manner with only a small number of training examples for novel species. AMP₀ webserver is available at http://ampzero.pythonanywhere.com.

Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective

The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among UniProt-derived protein sequences from different bacterial taxa, while a resulting parameter ("Die" score) suggested which taxa would prevail in a defined microbiome. T he working hypothesis was examined by correlating the calculated Die scores, to the abundance of bacterial taxa from gut microbiomes from different states of health and disease. Eleven publicly available 16S rRNA datasets and a dataset from a full shotgun metagenomics served for the analysis. The overall conclusion was that AMPs encrypted within bacterial proteomes affected the predominance of bacterial taxa in chemospheres.

sAMP-PFPDeep: Improving accuracy of short antimicrobial peptides prediction using three different sequence encodings and deep neural networks

Short antimicrobial peptides (sAMPs) belong to a significant repertoire of antimicrobial agents and are known to possess enhanced antimicrobial activity, higher stability and less toxicity to human cells, as well as less complex than other large biological drugs. As these molecules are significantly important, herein, a prediction method for sAMPs (with a sequence length ≤ 30 residues) is proposed for accurate and efficient prediction of sAMPs instead of laborious and costly experimental approaches. Benchmark dataset was collected from a recently reported study and sequences were converted into three channel images comprising information related to the position, frequency and sum of 12 physiochemical features as the first, second and third channels, respectively. Two image-based deep neural networks (DNNs), i.e. RESNET-50 and VGG-16 were trained and evaluated using various metrics while a comparative analysis with previous techniques was also performed. Validation of sAMP-PFPDeep was also performed by using molecular docking based analysis. The results showed that VGG-16 provided more accurate results, i.e. 98.30% training accuracy and 87.37% testing accuracy for predicting sAMPs as compared to those of RESNET-50 having 96.14% training accuracy and 83.87% testing accuracy. However, the comparative analysis revealed that both these models outperformed previously reported state-of-the-art methods. Based on the results, it is concluded that sAMP-PFPDeep can help identify antimicrobial peptides with promising accuracy and efficiency. It can help biologists and scientists to identify antimicrobial peptides, by further aiding the computer-aided drug design and discovery, as well as virtual screening protocols against various pathologies. sAMP-PFPDeep is available at (https://github.com/WaqarHusain/sAMP-PFPDeep).

Ab initio Designed Antimicrobial Peptides Against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) are ubiquitous amongst living organisms and are part of the innate immune system with the ability to kill pathogens directly or indirectly by modulating the immune system. AMPs have potential as a novel therapeutic against bacteria due to their quick-acting mechanism of action that prevents bacteria from developing resistance. Additionally, there is a dire need for therapeutics with activity specifically against Gram-negative bacterial infections that are intrinsically difficult to treat, with or without acquired drug resistance. Development of new antibiotics has slowed in recent years and novel therapeutics (like AMPs) with a focus against Gram-negative bacteria are needed. We designed eight novel AMPs, termed PHNX peptides, using ab initio computational design (database filtering technology combined with the novel positional analysis on APD3 dataset of AMPs with activity against Gram-negative bacteria) and assessed their theoretical function using published machine learning algorithms, and finally, validated their activity in our laboratory. These AMPs were tested to establish their minimum inhibitory concentration (MIC) and half-maximal effective concentration (EC₅₀) under CLSI methodology against antibiotic resistant and antibiotic susceptible Escherichia coli and Staphylococcus aureus. Laboratory-based experimental results were compared to computationally predicted activities for each of the peptides to ascertain the accuracy of the computational tools used. PHNX-1 demonstrated antibacterial activity (under high and low-salt conditions) against antibiotic resistant and susceptible strains of Gram-positive and Gram-negative bacteria and PHNX-4 to -8 demonstrated low-salt antibacterial activity only. The AMPs were then evaluated for cytotoxicity using hemolysis against human red blood cells and demonstrated some hemolysis which needs to be further evaluated. In this study, we successfully developed a design methodology to create synthetic AMPs with a narrow spectrum of activity where the PHNX AMPs demonstrated higher antibacterial activity against Gram-negative bacteria compared to Gram-positive bacteria. Thus, these peptides present novel synthetic peptides with a potential for therapeutic use. Based on our findings, we propose upfront selection of the peptide dataset for analysis, an additional step of positional analysis to add to the ab initio database filtering technology (DFT) method, and we present laboratory data on the novel, synthetically designed AMPs to validate the results of the computational approach. We aim to conduct future in vivo studies which could establish these AMPs for clinical use.

PepVAE: Variational Autoencoder Framework for Antimicrobial Peptide Generation and Activity Prediction

New methods for antimicrobial design are critical for combating pathogenic bacteria in the post-antibiotic era. Fortunately, competition within complex communities has led to the natural evolution of antimicrobial peptide (AMP) sequences that have promising bactericidal properties. Unfortunately, the identification, characterization, and production of AMPs can prove complex and time consuming. Here, we report a peptide generation framework, PepVAE, based around variational autoencoder (VAE) and antimicrobial activity prediction models for designing novel AMPs using only sequences and experimental minimum inhibitory concentration (MIC) data as input. Sampling from distinct regions of the learned latent space allows for controllable generation of new AMP sequences with minimal input parameters. Extensive analysis of the PepVAE-generated sequences paired with antimicrobial activity prediction models supports this modular design framework as a promising system for development of novel AMPs, demonstrating controlled production of AMPs with experimental validation of predicted antimicrobial activity.

Effects of Local and Systemic Immune Challenges on the Expression of Selected Salivary Genes in the Malaria Mosquito Anopheles coluzzii

Salivary glands play a crucial tripartite role in mosquito physiology. First, they secrete factors that greatly facilitate both sugar and blood meal acquisition. Second, the transmission of pathogens (parasites, bacteria and viruses) to the vertebrate host requires both the recognition and invasion of the salivary glands. Third, they produce immune factors that both protect the organ from invading pathogens and are also able to exert their activity in the crop and the midgut when saliva is re-ingested during feeding. Studies on mosquito sialomes have revealed the presence of several female and/or male salivary gland-specific or enriched genes whose function is completely unknown so far. We focused our attention on these orphan genes, and we selected, according to sequence and structural features, a shortlist of 11 candidates with potential antimicrobial properties. Afterwards, using qPCR, we investigated their expression profile at 5 and 24 h after an infectious sugar meal (local challenge) or thoracic microinjection (systemic challenge) of Gram-negative (Escherichia coli, EC) or Gram-positive (Staphylococcus aureus, SA) bacteria. We observed a general increase in the transcript abundance of our salivary candidates between 5 and 24 h after local challenge. Moreover, transcriptional modulation was determined by the nature of the stimulus, with salivary gland-enriched genes (especially hyp15 upon SA stimulus) upregulated shortly after the local challenge and later after the systemic challenge. Overall, this work provides one of the first contributions to the understanding of the immune role of mosquito salivary glands. Further characterization of salivary candidates whose expression is modulated by immune challenge may help in the identification of possible novel antimicrobial peptides.

Taxonomic bias in AMP prediction of invertebrate peptides

Invertebrate antimicrobial peptides (AMPs) are at the forefront in the search for agents of therapeutic utility against multi-resistant microbial pathogens, and in recent years substantial advances took place in the in silico prediction of antimicrobial function of amino acid sequences. A yet neglected aspect is taxonomic bias in the performance of these tools. Owing to differences in the prediction algorithms and used training data sets between tools, and phylogenetic differences in sequence diversity, physicochemical properties and evolved biological functions of AMPs between taxa, notable discrepancies may exist in performance between the currently available prediction tools. Here we tested if there is taxonomic bias in the prediction power in 10 tools with a total of 20 prediction algorithms in 19 invertebrate taxa, using a data set containing 1525 AMP and 3050 non-AMP sequences. We found that most of the tools exhibited considerable variation in performance between tested invertebrate groups. Based on the per-taxa performances and on the variation in performances across taxa we provide guidance in choosing the best-performing prediction tool for all assessed taxa, by listing the highest scoring tool for each of them.

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at https://github.com/guofei-tju/Ensemble-classifier-chain-model.

Computational Methods and Tools in Antimicrobial Peptide Research

The evolution of antibiotic-resistant bacteria is an ongoing and troubling development that has increased the number of diseases and infections that risk going untreated. There is an urgent need to develop alternative strategies and treatments to address this issue. One class of molecules that is attracting significant interest is that of antimicrobial peptides (AMPs). Their design and development has been aided considerably by the applications of molecular models, and we review these here. These methods include the use of tools to explore the relationships between their structures, dynamics, and functions and the increasing application of machine learning and molecular dynamics simulations. This review compiles resources such as AMP databases, AMP-related web servers, and commonly used techniques, together aimed at aiding researchers in the area toward complementing experimental studies with computational approaches.

[Antibacterial effects of peptides synthesized based on the sequence of ribosome protein S1]

Antibiotic resistance of bacteria is a topical problem on a global scale. Sometimes vigorous human activity leads to an increase in the number of bacteria carrying resistance genes in the environment. Antimicrobial peptides (AMPs) and similar compounds are potential candidates for combating antibiotic-resistant bacteria. Previously, we proposed and successfully tested on Thermus thermophilus a new mechanism of AMP action. This mechanism of directed coaggregation is based on the interaction of a peptide capable of forming fibrils with a target protein. In this work, we discuss the criteria for choosing a target for the targeted action of AMP, describe the features of the "parental" S1 ribosomal proteins T. thermophilus and Escherichia coli and the studied peptides using bioinformatic analysis methods, assess the antimicrobial effect of the synthesized peptides on a model organism of E. coli and cytotoxicity on cells of human fibroblasts. The obtained results will be important for the creation of new AMPs for pathogenic organisms.

Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set

In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.

A novel antibacterial peptide recognition algorithm based on BERT

As the best substitute for antibiotics, antimicrobial peptides (AMPs) have important research significance. Due to the high cost and difficulty of experimental methods for identifying AMPs, more and more researches are focused on using computational methods to solve this problem. Most of the existing calculation methods can identify AMPs through the sequence itself, but there is still room for improvement in recognition accuracy, and there is a problem that the constructed model cannot be universal in each dataset. The pre-training strategy has been applied to many tasks in natural language processing (NLP) and has achieved gratifying results. It also has great application prospects in the field of AMP recognition and prediction. In this paper, we apply the pre-training strategy to the model training of AMP classifiers and propose a novel recognition algorithm. Our model is constructed based on the BERT model, pre-trained with the protein data from UniProt, and then fine-tuned and evaluated on six AMP datasets with large differences. Our model is superior to the existing methods and achieves the goal of accurate identification of datasets with small sample size. We try different word segmentation methods for peptide chains and prove the influence of pre-training steps and balancing datasets on the recognition effect. We find that pre-training on a large number of diverse AMP data, followed by fine-tuning on new data, is beneficial for capturing both new data's specific features and common features between AMP sequences. Finally, we construct a new AMP dataset, on which we train a general AMP recognition model.

On the optimistic performance evaluation of newly introduced bioinformatic methods

Most research articles presenting new data analysis methods claim that "the new method performs better than existing methods," but the veracity of such statements is questionable. Our manuscript discusses and illustrates consequences of the optimistic bias occurring during the evaluation of novel data analysis methods, that is, all biases resulting from, for example, selection of datasets or competing methods, better ability to fix bugs in a preferred method, and selective reporting of method variants. We quantitatively investigate this bias using an example from epigenetic analysis: normalization methods for data generated by the Illumina HumanMethylation450K BeadChip microarray.

Engineering Selectively Targeting Antimicrobial Peptides

The rise of antibiotic-resistant strains of bacterial pathogens has necessitated the development of new therapeutics. Antimicrobial peptides (AMPs) are a class of compounds with potentially attractive therapeutic properties, including the ability to target specific groups of bacteria. In nature, AMPs exhibit remarkable structural and functional diversity, which may be further enhanced through genetic engineering, high-throughput screening, and chemical modification strategies. In this review, we discuss the molecular mechanisms underlying AMP selectivity and highlight recent computational and experimental efforts to design selectively targeting AMPs. While there has been an extensive effort to find broadly active and highly potent AMPs, it remains challenging to design targeting peptides to discriminate between different bacteria on the basis of physicochemical properties. We also review approaches for measuring AMP activity, point out the challenges faced in assaying for selectivity, and discuss the potential for increasing AMP diversity through chemical modifications.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides

Antimicrobial peptides (AMPs) are a unique and diverse group of molecules that play a crucial role in a myriad of biological processes and cellular functions. AMP-related studies have become increasingly popular in recent years due to antimicrobial resistance, which is becoming an emerging global concern. Systematic experimental identification of AMPs faces many difficulties due to the limitations of current methods. Given its significance, more than 30 computational methods have been developed for accurate prediction of AMPs. These approaches show high diversity in their data set size, data quality, core algorithms, feature extraction, feature selection techniques and evaluation strategies. Here, we provide a comprehensive survey on a variety of current approaches for AMP identification and point at the differences between these methods. In addition, we evaluate the predictive performance of the surveyed tools based on an independent test data set containing 1536 AMPs and 1536 non-AMPs. Furthermore, we construct six validation data sets based on six different common AMP databases and compare different computational methods based on these data sets. The results indicate that amPEPpy achieves the best predictive performance and outperforms the other compared methods. As the predictive performances are affected by the different data sets used by different methods, we additionally perform the 5-fold cross-validation test to benchmark different traditional machine learning methods on the same data set. These cross-validation results indicate that random forest, support vector machine and eXtreme Gradient Boosting achieve comparatively better performances than other machine learning methods and are often the algorithms of choice of multiple AMP prediction tools.

Addition of Dairy Lipids and Probiotic Lactobacillus fermentum in Infant Formulas Modulates Proteolysis and Lipolysis With Moderate Consequences on Gut Physiology and Metabolism in Yucatan Piglets

Breast milk is the gold standard in neonatal nutrition, but most infants are fed infant formulas in which lipids are usually of plant origin. The addition of dairy lipids and/or milk fat globule membrane extracts in formulas improves their composition with beneficial consequences on protein and lipid digestion. The probiotic Lactobacillus fermentum (Lf) was reported to reduce transit time in rat pups, which may also improve digestion. This study aimed to investigate the effects of the addition of dairy lipids in formulas, with or without Lf, on protein and lipid digestion and on gut physiology and metabolism. Piglets were suckled from postnatal days 2 to 28, with formulas containing either plant lipids (PL), a half-half mixture of plant and dairy lipids (DL), or this mixture supplemented with Lf (DL+Lf). At day 28, piglets were euthanized 90 min after their last feeding. Microstructure of digesta did not differ among formulas. Gastric proteolysis was increased (P < 0.01) in DL and DL+Lf (21.9 ± 2.1 and 22.6 ± 1.3%, respectively) compared with PL (17.3 ± 0.6%) and the residual proportion of gastric intact caseins decreased (p < 0.01) in DL+Lf (5.4 ± 2.5%) compared with PL and DL (10.6 ± 3.1% and 21.8 ± 6.8%, respectively). Peptide diversity in ileum and colon digesta was lower in PL compared to DL and DL+Lf. DL and DL+Lf displayed an increased (p < 0.01) proportion of diacylglycerol/cholesterol in jejunum and ileum digesta compared to PL and tended (p = 0.07) to have lower triglyceride/total lipid ratio in ileum DL+Lf (0.019 ± 0.003) as compared to PL (0.045 ± 0.011). The percentage of endocrine tissue and the number of islets in the pancreas were decreased (p < 0.05) in DL+Lf compared with DL. DL+Lf displayed a beneficial effect on host defenses [increased goblet cell density in jejunum (p < 0.05)] and a trophic effect [increased duodenal (p = 0.09) and jejunal (p < 0.05) weights]. Altogether, our results demonstrate that the addition of dairy lipids and probiotic Lf in infant formula modulated protein and lipid digestion, with consequences on lipid profile and with beneficial, although moderate, physiological effects.