Prediction of antimicrobial activity of synthetic peptides by a decision tree model

Roles of Different β-Defensins in the Human Reproductive System: A Review Study

Human β-defensins (hBDs) are cationic peptides with an amphipathic spatial shape and a high cysteine content. The members of this peptide family have been found in the human body with various functions, including the human reproductive system. Of among β-defensins in the human body, β-defensin 1, β-defensin 2, and β-defensin 126 are known in the human reproductive system. Human β-defensin 1 interacts with chemokine receptor 6 (CCR6) in the male reproductive system to prevent bacterial infections. This peptide has a positive function in antitumor immunity by recruiting dendritic cells and memory T cells in prostate cancer. It is necessary for fertilization via facilitating capacitation and acrosome reaction in the female reproductive system. Human β-defensin 2 is another peptide with antibacterial action which can minimize infection in different parts of the female reproductive system such as the vagina by interacting with CCR6. Human β-defensin 2 could play a role in preventing cervical cancer via interactions with dendritic cells. Human β-defensin 126 is required for sperm motility and protecting the sperm against immune system factors. This study attempted to review the updated knowledge about the roles of β-defensin 1, β-defensin 2, and β-defensin 126 in both the male and female reproductive systems.

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.

Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides

The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700), we have explored a variety of machine learning classifiers to build in silico models for AMP prediction, including Random Forest (RF), k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), and ensemble learning. Among the various models generated, the RF classifier-based model top-performed in both the internal [Accuracy: 91.40%, Precision: 89.37%, Sensitivity: 90.05%, and Specificity: 92.36%] and external validations [Accuracy: 89.43%, Precision: 88.92%, Sensitivity: 85.21%, and Specificity: 92.43%]. In addition, the RF classifier-based model correctly predicted the known AMPs and non-AMPs; those kept aside as an additional external validation set. The performance assessment revealed three features viz. ChargeD2001, PAAC12 (pseudo amino acid composition), and polarity T13 that are likely to play vital roles in the antimicrobial activity of AMPs. The developed RF-based classification model may further be useful in the design and prediction of the novel potential AMPs.

The anti-ageing effects of a natural peptide discovered by artificial intelligence

OBJECTIVE

As skin ages, impaired extracellular matrix (ECM) protein synthesis and increased action of degradative enzymes manifest as atrophy, wrinkling and laxity. There is mounting evidence for the functional role of exogenous peptides across many areas, including in offsetting the effects of cutaneous ageing. Here, using an artificial intelligence (AI) approach, we identified peptide RTE62G (pep_RTE62G), a naturally occurring, unmodified peptide with ECM stimulatory properties. The AI-predicted anti-ageing properties of pep_RTE62G were then validated through in vitro, ex vivo and proof of concept clinical testing.

METHODS

A deep learning approach was applied to unlock pep_RTE62G from a plant source, Pisum sativum (pea). Cell culture assays of human dermal fibroblasts (HDFs) and keratinocytes (HaCaTs) were subsequently used to evaluate the in vitro effect of pep_RTE62G. Distinct activities such as cell proliferation and ECM protein production properties were determined by ELISA assays. Cell migration was assessed using a wound healing assay, while ECM protein synthesis and gene expression were analysed, respectively, by immunofluorescence microscopy and PCR. Immunohistochemistry of human skin explants was employed to further investigate the induction of ECM proteins by pep_RTE62G ex vivo. Finally, the clinical effect of pep_RTE626 was evaluated in a proof of concept 28-day pilot study.

RESULTS

In vitro testing confirmed that pep_RTE62G is an effective multi-functional anti-ageing ingredient. In HaCaTs, pep_RTE62G treatment significantly increases both cellular proliferation and migration. Similarly, in HDFs, pep_RTE62G consistently induced the neosynthesis of ECM protein elastin and collagen, effects that are upheld in human skin explants. Lastly, in our proof of concept clinical study, application of pep_RTE626 over 28 days demonstrated anti-wrinkle and collagen stimulatory potential.

CONCLUSION

pep_RTE62G represents a natural, unmodified peptide with AI-predicted and experimentally validated anti-ageing properties. Our results affirm the utility of AI in the discovery of novel, functional topical ingredients.

Curved or linear? Predicting the 3-dimensional structure of α-helical antimicrobial peptides in an amphipathic environment

α-Helical membrane-active antimicrobial peptides (AMPs) are known to act via a range of mechanisms, including the formation of barrel-stave and toroidal pores and the micellisation of the membrane (carpet mechanism). Different mechanisms imply that the peptides adopt different 3D structures when bound at the water-membrane interface, a highly amphipathic environment. Here, an evolutionary algorithm is used to predict the 3D structure of a range of α-helical membrane-active AMPs at the water-membrane interface by optimising amphipathicity. This amphipathic structure prediction (ASP) is capable of distinguishing between curved and linear peptides solved experimentally, potentially allowing the activity and mechanism of action of different membrane-active AMPs to be predicted. The ASP algorithm is accessible via a web interface at http://atb.uq.edu.au/asp/.

Fermented mulberry leaf meal as fishmeal replacer in the formulation of feed for carp Labeo rohita and catfish Heteropneustes fossilis-optimization by mathematical programming

Search for cost-effective, eco-friendly and sustainable plant resources as potential feedstuff to replace fishmeal in the formulation of feed for fish has been in the forefront of aquaculture researches since the last few years. In this study, experiments were conducted to evaluate if replacement of fishmeal by the fermented leaf meal of mulberry (Morus indica) was viable in the formulation of feed for carp fish Labeo rohita and catfish Heteropneustes fossilis. Four iso-proteinous, iso-lipidic and iso-energetic experimental feed were formulated by replacing 0, 25, 50 and 75% of fishmeal by the fermented mulberry leaf meal (FMLM), and both species were grown on these feeds for 8 weeks. Since the results revealed differences in response to fishmeal replacement level between parameters, we determined optimum fishmeal replacement level (OFRL) for each parameter from the polynomial curve equation. While maximum weight gain and specific growth rate and minimum feed conversion ratio was found at 30-32% OFRL for L. rohita and at 52-53% OFRL for H. fossilis, other parameters responded differently in both fish. Therefore, we applied a two-phase fuzzy goal programming technique using all parameters, which showed overall OFRL for L. rohita and H. fossilis as 30.95% and 52%, respectively. We also applied the concept of 'decision tree' to identify the key factor behind utilization of FMLM. It was concluded that activity of amylase and subsequent utilization of carbohydrate was the key factor in utilizing FMLM. Interestingly, H. fossilis was found more efficient in utilizing carbohydrate of FMLM than L. rohita.

An advanced approach to identify antimicrobial peptides and their function types for penaeus through machine learning strategies

BACKGROUND

Antimicrobial peptides (AMPs) are essential components of the innate immune system and can protect the host from various pathogenic bacteria. The marine environment is known to be one of the richest sources for AMPs. Effective usage of AMPs and their derivatives can greatly improve the immunity and breeding survival rate of aquatic products. It is highly desirable to develop computational tools for rapidly and accurately identifying AMPs and their functional types, for the purpose of helping design new and more effective antimicrobial agents.

RESULTS

In this study, we made an attempt to develop an advanced machine learning based computational approach, MAMPs-Pred, for identification of AMPs and its function types. Initially, SVM-prot 188-D features were extracted that were subsequently used as input to a two-layer multi-label classifier. In specific, the first layer is to identify whether it is an AMP by applying RF classifier, and the second layer addresses the multi-type problem by identifying the activites or function types of AMPs by applying PS-RF and LC-RF classifiers. To benchmark the methods,the MAMPs-Pred method is also compared with existing best-performing methods in literature and has shown an improved identification accuracy.

CONCLUSIONS

The results reported in this study indicate that the MAMP-Pred method achieves high performance for identifying AMPs and its functional types.The proposed approach is believed to supplement the tools and techniques that have been developed in the past for predicting AMPs and their function types.

De Novo Design and In Vitro Testing of Antimicrobial Peptides against Gram-Negative Bacteria

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

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.

An Optimized Framework for Cancer Prediction Using Immunosignature

Background:

Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma.

Methods:

In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers.

Results:

The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm.

Conclusion:

The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.

Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.

Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types

MOTIVATION

With the rapid increase of infection resistance to antibiotics, it is urgent to find novel infection therapeutics. In recent years, antimicrobial peptides (AMPs) have been utilized as potential alternatives for infection therapeutics. AMPs are key components of the innate immune system and can protect the host from various pathogenic bacteria. Identifying AMPs and their functional types has led to many studies, and various predictors using machine learning have been developed. However, there is room for improvement; in particular, no predictor takes into account the lack of balance among different functional AMPs.

RESULTS

In this paper, a new synthetic minority over-sampling technique on imbalanced and multi-label datasets, referred to as ML-SMOTE, was designed for processing and identifying AMPs' functional families. A novel multi-label classifier, MLAMP, was also developed using ML-SMOTE and grey pseudo amino acid composition. The classifier obtained 0.4846 subset accuracy and 0.16 hamming loss.

AVAILABILITY AND IMPLEMENTATION

A user-friendly web-server for MLAMP was established at http://www.jci-bioinfo.cn/MLAMP CONTACTS: linweizhong@jci.edu.cn or xudong@missouri.edu.

Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity

Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs.

First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens

Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.

High Emergence of ESBL-Producing E. coli Cystitis: Time to Get Smarter in Cyprus

Background: Widespread prevalence of extended-spectrum βeta-lactamase producing Escherichia coli (ESBL-producing E. coli) limits the infection therapeutic options and is a growing global health problem. In this study our aim was to investigate the antimicrobial resistance profile of the E. coli in hospitalized and out-patients in Cyprus.

Results: During the period 2010–2014, 389 strains of E. coli were isolated from urine samples of hospitalized and out-patients in Cyprus. ESBL-producing E. coli, was observed in 53% of hospitalized and 44% in out-patients, latest one being in 2014. All ESBL-producing E. coli remained susceptible to amikacin, carbapenems except ertapenem (in-patients = 6%, out-patients = 11%).

Conclusion: High emerging ESBL-producing E. coli from urine samples in hospitalized and out-patients is an extremely worrisome sign of development of untreatable infections in the near future on the island. We therefore emphasize the immediate need for establishment of optimal therapy guidelines based on the country specific surveillance programs. The need for new treatment strategies, urgent prescription habit changes and ban of over-the-counter sale of antimicrobials at each segment of healthcare services is also discussed in this research.

Machine-learning techniques applied to antibacterial drug discovery

The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.

Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput 'omics' technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

Escherichia coli in Europe: an overview

Escherichia coli remains one of the most frequent causes of several common bacterial infections in humans and animals. E. coli is the prominent cause of enteritis, urinary tract infection, septicaemia and other clinical infections, such as neonatal meningitis. E. coli is also prominently associated with diarrhoea in pet and farm animals. The therapeutic treatment of E. coli infections is threatened by the emergence of antimicrobial resistance. The prevalence of multidrug-resistant E. coli strains is increasing worldwide principally due to the spread of mobile genetic elements, such as plasmids. The rise of multidrug-resistant strains of E. coli also occurs in Europe. Therefore, the spread of resistance in E. coli is an increasing public health concern in European countries. This paper summarizes the current status of E. coli strains clinically relevant in European countries. Furthermore, therapeutic interventions and strategies to prevent and control infections are presented and discussed. The article also provides an overview of the current knowledge concerning promising alternative therapies against E. coli diseases.

Database-Guided Discovery of Potent Peptides to Combat HIV-1 or Superbugs

Antimicrobial peptides (AMPs), small host defense proteins, are indispensable for the protection of multicellular organisms such as plants and animals from infection. The number of AMPs discovered per year increased steadily since the 1980s. Over 2,000 natural AMPs from bacteria, protozoa, fungi, plants, and animals have been registered into the antimicrobial peptide database (APD). The majority of these AMPs (>86%) possess 11–50 amino acids with a net charge from 0 to +7 and hydrophobic percentages between 31–70%. This article summarizes peptide discovery on the basis of the APD. The major methods are the linguistic model, database screening, de novo design, and template-based design. Using these methods, we identified various potent peptides against human immunodeficiency virus type 1 (HIV-1) or methicillin-resistant Staphylococcus aureus (MRSA). While the stepwise designed anti-HIV peptide is disulfide-linked and rich in arginines, the ab initio designed anti-MRSA peptide is linear and rich in leucines. Thus, there are different requirements for antiviral and antibacterial peptides, which could kill pathogens via different molecular targets. The biased amino acid composition in the database-designed peptides, or natural peptides such as θ-defensins, requires the use of the improved two-dimensional NMR method for structural determination to avoid the publication of misleading structure and dynamics. In the case of human cathelicidin LL-37, structural determination requires 3D NMR techniques. The high-quality structure of LL-37 provides a solid basis for understanding its interactions with membranes of bacteria and other pathogens. In conclusion, the APD database is a comprehensive platform for storing, classifying, searching, predicting, and designing potent peptides against pathogenic bacteria, viruses, fungi, parasites, and cancer cells.