Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields

AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

Peptidome Profiling of Bubalus bubalis Urine and Assessment of Its Antimicrobial Activity against Mastitis-Causing Pathogens

Urinary proteins have been studied quite exhaustively in the past, however, the small sized peptides have remained neglected for a long time in dairy cattle. These peptides are the products of systemic protein turnover, which are excreted out of the body and hence can serve as an important biomarker for various pathophysiologies. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the urinary peptides in buffalo and simultaneously their bioactivities, we generated a peptidome profile from the urine of Murrah Buffaloes (n = 10). Urine samples were processed using <10 kDa MWCO filter and filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). The nLC-MS/MS of the aqueous phase from ten animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate the antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains: S. aureus, E. coli, and S. agalactiae. The urinary peptides observed in the study are the result of the activity of possibly 76 proteases. The GO of these proteases showed the significant enrichment of the antibacterial peptide production. The total urinary peptide showed antimicrobial activity against the aforementioned pathogenic bacterial strains with no significant inhibitory effects against a buffalo mammary epithelial cell line. Just like our previous study in cows, the present study suggests the prime role of the antimicrobial peptides in the maintenance of the sterility of the urinary tract in buffalo by virtue of their amino acid composition.

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Identification and Functional Characterization of Peptides With Antimicrobial Activity From the Syphilis Spirochete, Treponema pallidum

The etiological agent of syphilis, Treponema pallidum ssp. pallidum, is a highly invasive “stealth” pathogen that can evade the host immune response and persist within the host for decades. This obligate human pathogen is adept at establishing infection and surviving at sites within the host that have a multitude of competing microbes, sometimes including pathogens. One survival strategy employed by bacteria found at polymicrobial sites is elimination of competing microorganisms by production of antimicrobial peptides (AMPs). Antimicrobial peptides are low molecular weight proteins (miniproteins) that function directly via inhibition and killing of microbes and/or indirectly via modulation of the host immune response, which can facilitate immune evasion. In the current study, we used bioinformatics to show that approximately 7% of the T. pallidum proteome is comprised of miniproteins of 150 amino acids or less with unknown functions. To investigate the possibility that AMP production is an unrecognized defense strategy used by T. pallidum during infection, we developed a bioinformatics pipeline to analyze the complement of T. pallidum miniproteins of unknown function for the identification of potential AMPs. This analysis identified 45 T. pallidum AMP candidates; of these, Tp0451a and Tp0749 were subjected to further bioinformatic analyses to identify AMP critical core regions (AMPCCRs). Four potential AMPCCRs from the two predicted AMPs were identified and peptides corresponding to these AMPCCRs were experimentally confirmed to exhibit bacteriostatic and bactericidal activity against a panel of biologically relevant Gram-positive and Gram-negative bacteria. Immunomodulation assays performed under inflammatory conditions demonstrated that one of the AMPCCRs was also capable of differentially regulating expression of two pro-inflammatory chemokines [monocyte chemoattractant protein-1 (MCP-1) and interleukin-8 (IL-8)]. These findings demonstrate proof-of-concept for our developed AMP identification pipeline and are consistent with the novel concept that T. pallidum expresses AMPs to defend against competing microbes and modulate the host immune response.

Antimicrobial Peptides: An Update on Classifications and Databases

Antimicrobial peptides (AMPs) are distributed across all kingdoms of life and are an indispensable component of host defenses. They consist of predominantly short cationic peptides with a wide variety of structures and targets. Given the ever-emerging resistance of various pathogens to existing antimicrobial therapies, AMPs have recently attracted extensive interest as potential therapeutic agents. As the discovery of new AMPs has increased, many databases specializing in AMPs have been developed to collect both fundamental and pharmacological information. In this review, we summarize the sources, structures, modes of action, and classifications of AMPs. Additionally, we examine current AMP databases, compare valuable computational tools used to predict antimicrobial activity and mechanisms of action, and highlight new machine learning approaches that can be employed to improve AMP activity to combat global antimicrobial resistance.

AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches

Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.

Peptide profiling in cow urine reveals molecular signature of physiology-driven pathways and in-silico predicted bioactive properties

Peptidomics allows the identification of peptides that are derived from proteins. Urinary peptidomics has revolutionized the field of diagnostics as the samples represent complete systemic changes happening in the body. Moreover, it can be collected in a non-invasive manner. We profiled the peptides in urine collected from different physiological states (heifer, pregnancy, and lactation) of Sahiwal cows. Endogenous peptides were extracted from 30 individual cows belonging to three groups, each group comprising of ten animals (biological replicates n = 10). Nano Liquid chromatography Mass spectrometry (nLC-MS/MS) experiments revealed 5239, 4774, and 5466 peptides in the heifer, pregnant and lactating animals respectively. Urinary peptides of <10 kDa size were considered for the study. Peptides were extracted by 10 kDa MWCO filter. Sequences were identified by scanning the MS spectra ranging from 200 to 2200 m/z. The peptides exhibited diversity in sequences across different physiological states and in-silico experiments were conducted to classify the bioactive peptides into anti-microbial, anti-inflammatory, anti-hypertensive, and anti-cancerous groups. We have validated the antimicrobial effect of urinary peptides on Staphylococcus aureus and Escherichia coli under an in-vitro experimental set up. The origin of these peptides was traced back to certain proteases viz. MMPs, KLKs, CASPs, ADAMs etc. which were found responsible for the physiology-specific peptide signature of urine. Proteins involved in extracellular matrix structural constituent (GO:0005201) were found significant during pregnancy and lactation in which tissue remodeling is extensive. Collagen trimers were prominent molecules under cellular component category during lactation. Homophilic cell adhesion was found to be an important biological process involved in embryo attachment during pregnancy. The in-silico study also highlighted the enrichment of progenitor proteins on specific chromosomes and their relative expression in context to specific physiology. The urinary peptides, precursor proteins, and proteases identified in the study offers a base line information in healthy cows which can be utilized in biomarker discovery research for several pathophysiological studies.

In Silico, Molecular Docking and In Vitro Antimicrobial Activity of the Major Rapeseed Seed Storage Proteins

BACKGROUND

In addition to their use as an edible oil and condiment crop, mustard and rapeseed (Brassica napus L., B. juncea (L.) Czern., B. nigra (L.) W.D.J.Koch, B. rapa L. and Sinapis alba L.) have been commonly used in traditional medicine for relieving pain, coughs and treating infections. The seeds contain high amounts of oil, while the remaining by-product meal after oil extraction, about 40% of seed dry weight, has a low value despite its high protein-content (~85%). The seed storage proteins (SSP) 2S albumin-type napin and 12S globulin-type cruciferin are the two predominant proteins in the seeds and show potential for value adding to the waste stream; however, information on their biological activities is scarce. In this study, purified napin and cruciferin were tested using in silico, molecular docking, and in vitro approaches for their bioactivity as antimicrobial peptides.

MATERIALS AND METHODS

The 3D-structure of 2S albumin and 12S globulin storage proteins from B. napus were investigated to predict antimicrobial activity employing an antimicrobial peptide database survey. To gain deeper insights into the potential antimicrobial activity of these SSP, in silico molecular docking was performed. The purified B. napus cruciferin and napin were then tested against both Gram-positive and Gram-negative bacteria for in vitro antimicrobial activity by disc diffusion and microdilution antimicrobial susceptibility testing.

RESULTS

In silico analysis demonstrated both SSP share similar 3D-structure with other well studied antimicrobial proteins. Molecular docking revealed that the proteins exhibited high binding energy to bacterial enzymes. Cruciferin and napin proteins appeared as a double triplet and a single doublet, respectively, following SDS-PAGE. SDS-PAGE and Western blotting also confirmed the purity of the protein samples used for assessment of antimicrobial activity. Antimicrobial susceptibility testing provided strong evidence for antimicrobial activity for the purified napin protein; however, cruciferin showed no antimicrobial activity, even at the highest dose applied.

DISCUSSION

In silico and molecular docking results presented evidence for the potential antimicrobial activity of rapeseed cruciferin and napin SSP. However, only the in vitro antimicrobial activity of napin was confirmed. These findings warrant further investigation of this SSP protein as a potential new agent against infectious disease.

Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks

Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each peptide in a given dataset although the peptides themselves may differ in number of amino acids. Features are often chosen which represent certain periodic patterns in the peptide sequence without any initial guidance as to whether such patterns are relevant for the classification task at hand. This can result in the construction of a large number of irrelevant features in addition to relevant features. To help alleviate these issues, we choose to extract a feature vector from individual amino acid feature representations through the application of bidirectional Long Short-Term Memory recurrent neural networks. The Long Short-Term Memory network recursively iterates along both directions of the given amino acid sequence and ultimately extracts a finite length feature vector that is then used to classify the peptide. This work demonstrates the application of Long Short-Term Memory recurrent neural networks to classification of antibacterial peptides and compares it to a Random Forest classifier and a k-nearest neighbor classifier.

dbAMP: an integrated resource for exploring antimicrobial peptides with functional activities and physicochemical properties on transcriptome and proteome data

Antimicrobial peptides (AMPs), naturally encoded from genes and generally contained 10–100 amino acids, are crucial components of the innate immune system and can protect the host from various pathogenic bacteria, as well as viruses. In recent years, the widespread use of antibiotics has inspired the rapid growth of antibiotic-resistant microorganisms that usually induce critical infection and pathogenesis. An increasing interest therefore was motivated to explore natural AMPs that enable the development of new antibiotics. With the potential of AMPs being as new drugs for multidrug-resistant pathogens, we were thus motivated to develop a database (dbAMP, http://csb.cse.yzu.edu.tw/dbAMP/) by accumulating comprehensive AMPs from public domain and manually curating literature. Currently in dbAMP there are 12 389 unique entries, including 4271 experimentally verified AMPs and 8118 putative AMPs along with their functional activities, supported by 1924 research articles. The advent of high-throughput biotechnologies, such as mass spectrometry and next-generation sequencing, has led us to further expand dbAMP as a database-assisted platform for providing comprehensively functional and physicochemical analyses for AMPs based on the large-scale transcriptome and proteome data. Significant improvements available in dbAMP include the information of AMP–protein interactions, antimicrobial potency analysis for ‘cryptic’ region detection, annotations of AMP target species, as well as AMP detection on transcriptome and proteome datasets. Additionally, a Docker container has been developed as a downloadable package for discovering known and novel AMPs on high-throughput omics data. The user-friendly visualization interfaces have been created to facilitate peptide searching, browsing, and sequence alignment against dbAMP entries. All the facilities integrated into dbAMP can promote the functional analyses of AMPs and the discovery of new antimicrobial drugs.

Design of bioactive peptides derived from CART sequence isolated from the toadfish Thalassophryne nattereri

The emergence of bacterial resistance due to the indiscriminate use of antibiotics warrants the need for developing new bioactive agents. In this context, antimicrobial peptides are highly useful for managing resistant microbial strains. In this study, we report the isolation and characterization of peptides obtained from the venom of the toadfish Thalassophryne nattereri. These peptides were active against Gram-positive and Gram-negative bacteria and fungi. The primary amino acid sequences showed similarity to Cocaine and Amphetamine Regulated Transcript peptides, and two peptide analogs-Tn CRT2 and Tn CRT3-were designed using the AMPA algorithm based on these sequences. The analogs were subjected to physicochemical analysis and antimicrobial screening and were biologically active at concentrations ranging from 2.1 to 13 µM. Zeta potential analysis showed that the peptide analogs increased the positive charge on the cell surface of Gram-positive and Gram-negative bacteria. The toxicity of Tn CRT2 and Tn CRT3 were analyzed in vitro using a hemolytic assay and tetrazolium salt reduction in fibroblasts and was found to be significant only at high concentrations (up to 40 µM). These results suggest that this methodological approach is appropriate to design novel antimicrobial peptides to fight bacterial infections and represents a new and promising discovery in fish venom.

Characterization and Identification of Natural Antimicrobial Peptides on Different Organisms

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms.

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.

Large-Scale Analysis of Antimicrobial Activities in Relation to Amphipathicity and Charge Reveals Novel Characterization of Antimicrobial Peptides

It has been unclear to which antimicrobial activities (e.g., anti-gram-positive bacterial, anti-gram-negative bacterial, antifungal, antiparasitic, and antiviral activities) of antimicrobial peptides (AMPs) a given physiochemical property matters most. This is the first computational study using large-scale AMPs to examine the relationships between antimicrobial activities and two major physiochemical properties of AMPs—amphipathicity and net charge. The results showed that among all kinds of antimicrobial activities, amphipathicity and net charge best differentiated between AMPs with and without anti-gram-negative bacterial activities. In terms of amphipathicity and charge, all the AMPs whose activities were significantly associated with amphipathicity and net charge were alike except those with anti-gram-positive bacterial activities. Furthermore, the higher the amphipathic value, the greater the proportion of AMPs possessing both antibacterial and antifungal activities. This dose–response-like pattern suggests a possible causal relationship—dual antibacterial and antifungal activities of AMPs may be attributable to amphipathicity. These novel findings could be useful for identifying potent AMPs computationally.

Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms

When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods.

Antimicrobial Protein Candidates from the Thermophilic Geobacillus sp. Strain ZGt-1: Production, Proteomics, and Bioinformatics Analysis

A thermophilic bacterial strain, Geobacillus sp. ZGt-1, isolated from Zara hot spring in Jordan, was capable of inhibiting the growth of the thermophilic G. stearothermophilus and the mesophilic Bacillus subtilis and Salmonella typhimurium on a solid cultivation medium. Antibacterial activity was not observed when ZGt-1 was cultivated in a liquid medium; however, immobilization of the cells in agar beads that were subjected to sequential batch cultivation in the liquid medium at 60 °C showed increasing antibacterial activity up to 14 cycles. The antibacterial activity was lost on protease treatment of the culture supernatant. Concentration of the protein fraction by ammonium sulphate precipitation followed by denaturing polyacrylamide gel electrophoresis separation and analysis of the gel for antibacterial activity against G. stearothermophilus showed a distinct inhibition zone in 15–20 kDa range, suggesting that the active molecule(s) are resistant to denaturation by SDS. Mass spectrometric analysis of the protein bands around the active region resulted in identification of 22 proteins with molecular weight in the range of interest, three of which were new and are here proposed as potential antimicrobial protein candidates by in silico analysis of their amino acid sequences. Mass spectrometric analysis also indicated the presence of partial sequences of antimicrobial enzymes, amidase and dd-carboxypeptidase.

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.