ParaPep: a web resource for experimentally validated antiparasitic peptide sequences and their structures

Antimicrobial Peptide Databases as the Guiding Resource in New Antimicrobial Agent Identification via Computational Methods

In light of the growing interest in antimicrobial peptides (AMPs) as potential alternatives to traditional antibiotics, proteomic research has increasingly focused on this area. Addressing this significant scientific need, we undertook an initiative to review and analyze the available databases containing information on AMPs. These databases play a pivotal role as a foundation for most AMP-related studies, enabling not only the identification of new compounds, but also a deeper understanding of their properties and therapeutic potential. As part of this study, we evaluated the quality of information within selected AMP databases, considering their accessibility, content, and research potential. The initial step of the analysis involved a comparison of the per-database and cross-database peptide sequences. A diamond, high-throughput protein alignment program was used to compare the degree of sequence similarity among peptides across the individual databases. The redundancy of the data was also evaluated. Collected information was used for an in silico evaluation of the selected species' venom proteomes in order to identify putative antimicrobial peptide candidates. An example candidate was further evaluated via a combination of structural analysis based on the computed homology based structural model, the in silico digestion of the source protein, and the antimicrobial potential.

Deciphering optimal molecular determinants of non-hemolytic, cell-penetrating antimicrobial peptides through bioinformatics and Random Forest

Antimicrobial peptides (AMPs) are promising molecules for combating resistant pathogens, offering several advantages like broad-spectrum effectiveness and multi-targeted action. While most AMPs exhibit membranolytic activity similar to hemolytic peptides (HPs), some act by entering cells like cell-penetrating peptides (CPPs). The toxicity of AMPs towards the host is the major hurdle in their development and application. Given the peptides' function and toxicity largely depend on their molecular properties, identifying and fine-tuning these factors is imperative for developing effective and safe AMPs. To address these knowledge gaps, we present a study that employs a holistic strategy by investigating the molecular descriptors of AMPs, CPPs, HPs, and non-functional equivalents. The prediction of functional properties categorized datasets of 3697 experimentally validated peptides into six groups and three clusters. Predictive and statistical analyses of physicochemical and structural parameters revealed that AMPs have a mean hydrophobic moment of 1.2, a net charge of 4.5, and a lower isoelectric point of 10.9, with balanced hydrophobicity. For cluster AC-nHPs containing peptides with antimicrobial, cell-penetrating, and non-hemolytic properties, disordered conformation and aggregation propensities, followed by amphiphilicity index, isoelectric point, and net charge were identified as the most critical properties. In addition, this work also explains why most AMPs and HPs are membrane-disruptive, while CPPs are non-membranolytic. In conclusion, the study identifies optimal molecular descriptors and offers valuable insights for designing effective, non-toxic AMPs for therapeutic use.

AbAMPdb: a database of Acinetobacter baumannii specific antimicrobial peptides

Acinetobacter baumannii has emerged as a prominent nosocomial pathogen, exhibiting a progressive rise in resistance to therapeutic interventions. This rise in resistance calls for alternative strategies. Here, we propose an alternative yet specialized resource on antimicrobial peptides (AMPs) against A. baumannii. Database 'AbAMPdb' is the manually curated collection of 300 entries containing the 250 experimental AMP sequences and 50 corresponding synthetic or mutated AMP sequences. The mutated sequences were modified with reported amino acid substitutions intended for decreasing the toxicity and increasing the antimicrobial potency. AbAMPdb also provides 3D models of all 300 AMPs, comprising 250 natural and 50 synthetic or mutated AMPs. Moreover, the database offers docked complexes comprising 5000 AMPs and their corresponding A. baumannii target proteins. These complexes, accessible in Protein Data Bank format, enable the 2D visualization of the interacting amino acid residues. We are confident that this comprehensive resource furnishes vital information concerning AMPs, encompassing their docking interactions with virulence factors and antibiotic resistance proteins of A. baumannii. To enhance clinical relevance, the characterized AMPs could undergo further investigation both in vitro and in vivo. Database URL: https://abampdb.mgbio.tech/.

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.

Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Background

Protozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens.

Objective

The primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets.

Methods

A comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models.

Results

XGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset.

Conclusion

The models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.

A Novel Beta-Defensin Isoform from Malabar Trevally, Carangoides malabaricus (Bloch & Schneider, 1801), an Arsenal Against Fish Bacterial Pathogens: Molecular Characterization, Recombinant Production, and Mechanism of Action

Antimicrobial peptides (AMPs), including beta-defensin from fish, are a crucial class of peptide medicines. The focus of the current study is the molecular and functional attributes of CmDef, a 63-amino acid beta-defensin AMP from Malabar trevally, Carangoides malabaricus. This peptide demonstrated typical characteristics of AMPs, including hydrophobicity, amphipathic nature, and +2.8 net charge. The CmDef was recombinantly expressed and the recombinant peptide, rCmDef displayed a strong antimicrobial activity against bacterial fish pathogens with an MIC of 8 µM for V. proteolyticus and 32 µM for A. hydrophila. The E. tarda and V. harveyi showed an inhibition of 94% and 54%, respectively, at 32 µM concentration. No activity was observed against V. fluvialis and V. alginolyticus. The rCmDef has a multimode of action that exerts an antibacterial effect by membrane depolarization followed by membrane permeabilization and ROS production. rCmDef also exhibited anti-cancer activities in silico without causing hemolysis. The peptide demonstrated stability under various conditions, including different pH levels, temperatures, salts, and metal ions (KCl and CaCl2), and remained stable in the presence of proteases such as trypsin and proteinase K at concentrations up to 0.2 µg/100 µl. The strong antibacterial efficacy and non-cytotoxic nature suggest that rCmDef is a single-edged sword that can contribute significantly to aquaculture disease management.

Targeting peptide based therapeutics: Integrated computational and experimental studies of autophagic regulation in host-parasite interaction

Cutaneous leishmaniasis caused by the intracellular parasite Leishmania major, exhibits significant public health challenge worldwide. With limited treatment options available, the identification of novel therapeutic targets is of paramount importance. Present study manifested the crucial role of ATG8 protein as a potential target in combating L. major infection. Using machine learning algorithms, we identified non-conserved motifs within the ATG8 in L. major. Subsequently, a peptide library was generated based on these motifs, and three peptides were selected for further investigation through molecular docking and molecular dynamics simulations. Surface Plasmon Resonance (SPR) experiments confirmed the direct interaction between ATG8 and the identified peptides. Remarkably, these peptides demonstrated the ability to cross the parasite membrane and exert profound effects on L. major. Peptide treatment significantly impacted parasite survival, inducing alterations in the cell cycle and morphology. Furthermore, the peptides were found to modulate autophagosome formation, particularly under starved conditions, indicating their involvement in autophagy regulation within L. major. In vitro studies revealed that the selected peptides effectively decreased the parasite load within the infected host cells. Encouragingly, in vivo experiments corroborated these findings, demonstrating a reduction in parasite burden upon peptide administration. Additionally, the peptides were observed to affect the levels of LC3II, a known autophagy marker within the host cells. Collectively, our findings highlight the efficacy of these novel peptides in targeting L. major ATG8 and disrupting parasite survival, wherein P2 is showing prominent effect on L. major as compared to P1. These results provide valuable insights into the development of innovative therapeutic strategies against leishmaniasis.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Perspectives in Searching Antimicrobial Peptides (AMPs) Produced by the Microbiota

Changes in the structure and function of the microbiota are associated with various human diseases. These microbial changes can be mediated by antimicrobial peptides (AMPs), small peptides produced by the host and their microbiota, which play a crucial role in host-bacteria co-evolution. Thus, by studying AMPs produced by the microbiota (microbial AMPs), we can better understand the interactions between host and bacteria in microbiome homeostasis. Additionally, microbial AMPs are a new source of compounds against pathogenic and multi-resistant bacteria. Further, the growing accessibility to metagenomic and metatranscriptomic datasets presents an opportunity to discover new microbial AMPs. This review examines the structural properties of microbiota-derived AMPs, their molecular action mechanisms, genomic organization, and strategies for their identification in any microbiome data as well as experimental testing. Overall, we provided a comprehensive overview of this important topic from the microbial perspective.

Dual-action potential of cationic cryptides against infections and cancers

Cryptides are a subfamily of bioactive peptides embedded latently in their parent proteins and have multiple biological functions. Cationic cryptides could be used as modern drugs in both infectious diseases and cancers because their mechanism of action is less likely to be affected by genetic mutations in the treated cells, therefore addressing a current unmet need in these two areas of medicine. In this review, we present the current understanding of cryptides, methods to mine them sustainably using available online databases and prediction tools, with a particular focus on their antimicrobial and anticancer potential, and their potential applicability in a clinical setting.

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.

Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics

In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications.

Graphical Abstract

Advances in Antimicrobial Peptide Discovery via Machine Learning and Delivery via Nanotechnology

Antimicrobial peptides (AMPs) have been investigated for their potential use as an alternative to antibiotics due to the increased demand for new antimicrobial agents. AMPs, widely found in nature and obtained from microorganisms, have a broad range of antimicrobial protection, allowing them to be applied in the treatment of infections caused by various pathogenic microorganisms. Since these peptides are primarily cationic, they prefer anionic bacterial membranes due to electrostatic interactions. However, the applications of AMPs are currently limited owing to their hemolytic activity, poor bioavailability, degradation from proteolytic enzymes, and high-cost production. To overcome these limitations, nanotechnology has been used to improve AMP bioavailability, permeation across barriers, and/or protection against degradation. In addition, machine learning has been investigated due to its time-saving and cost-effective algorithms to predict AMPs. There are numerous databases available to train machine learning models. In this review, we focus on nanotechnology approaches for AMP delivery and advances in AMP design via machine learning. The AMP sources, classification, structures, antimicrobial mechanisms, their role in diseases, peptide engineering technologies, currently available databases, and machine learning techniques used to predict AMPs with minimal toxicity are discussed in detail.

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew's coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical applications.

tREPs-A New Class of Functional tRNA-Encoded Peptides

We asked if transfer RNA (tRNA) ever got an opportunity of translating its own sequence during evolution, what would have been the function of such tRNA-encoded peptides (tREPs)? If not, could one artificially synthesize tREPs to study the corresponding functional outcomes? Here, we report a novel, first-in-the-class, chemically synthesized tREP-18 molecule originating from the Escherichia coli tRNA sequence showing potent antileishmanial property. As a first step, E. coli tRNAs were computationally translated into peptide sequence equivalents and a database of full-length hypothetical tREPs was created. The tREP sequences were sent into sequence, structure, and energy filters to narrow down potential peptides for experimental validation. Based on the functional predictions, tREPs were screened against antiparasitic targets, leading to the identification of tREP-18 as a potential antiparasitic peptide. The in vitro assay of chemically synthesized tREP-18 on the Ag83 strain of Leishmania donovani showed its potent antileishmanial property (IC50 value of 22.13 nM). The atomic force microscopy and scanning electron microscopy images indicated significant alteration in the cytoskeletal architecture of tREP-18-treated parasites. Also, tREP-18 seems to destabilize the mitochondrial membrane potential of parasites, disrupting their cellular integrity and leading to parasitic death. The cellular assays of the tREP-18 peptide on the BS12 strain, a clinical isolate of post-kala azar dermal leishmaniasis, demonstrated its significant efficacy at an IC50 value of 15 nM. The tREP-18 peptide showed a toxic effect on the amastigote stage of the parasite, showing macrophage pathogen clearance at a concentration of 22.5 nM. This study provides the proof of the concept of making a new class of functional peptides from tRNA sequences. It also opens a huge untapped tRNA-peptide space toward novel discoveries and applications. In the future, it would be interesting to perform tREP edits and redesign tREPs toward specific applications.

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides

Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.

i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7-35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew's correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

Machine Learning Prediction of Antimicrobial Peptides

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.

In-Silico Tool for Predicting, Scanning, and Designing Defensins

Defensins are host defense peptides present in nearly all living species, which play a crucial role in innate immunity. These peptides provide protection to the host, either by killing microbes directly or indirectly by activating the immune system. In the era of antibiotic resistance, there is a need to develop a fast and accurate method for predicting defensins. In this study, a systematic attempt has been made to develop models for predicting defensins from available information on defensins. We created a dataset of defensins and non-defensins called the main dataset that contains 1,036 defensins and 1,035 AMPs (antimicrobial peptides, or non-defensins) to understand the difference between defensins and AMPs. Our analysis indicates that certain residues like Cys, Arg, and Tyr are more abundant in defensins in comparison to AMPs. We developed machine learning technique-based models on the main dataset using a wide range of peptide features. Our SVM (support vector machine)-based model discriminates defensins and AMPs with MCC of 0.88 and AUC of 0.98 on the validation set of the main dataset. In addition, we created an alternate dataset that consists of 1,036 defensins and 1,054 non-defensins obtained from Swiss-Prot. Models were also developed on the alternate dataset to predict defensins. Our SVM-based model achieved maximum MCC of 0.96 with AUC of 0.99 on the validation set of the alternate dataset. All models were trained, tested, and validated using standard protocols. Finally, we developed a web-based service "DefPred" to predict defensins, scan defensins in proteins, and design the best defensins from their analogs. The stand-alone software and web server of DefPred are available at https://webs.iiitd.edu.in/raghava/defpred.

PredAPP: Predicting Anti-Parasitic Peptides with Undersampling and Ensemble Approaches

Anti-parasitic peptides (APPs) have been regarded as promising therapeutic candidate drugs against parasitic diseases. Due to the fact that the experimental techniques for identifying APPs are expensive and time-consuming, there is an urgent need to develop a computational approach to predict APPs on a large scale. In this study, we provided a computational method, termed PredAPP (Prediction of Anti-Parasitic Peptides) that could effectively identify APPs using an ensemble of well-performed machine learning (ML) classifiers. Firstly, to solve the class imbalance problem, a balanced training dataset was generated by the undersampling method. We found that the balanced dataset based on cluster centroid achieved the best performance. Then, nine groups of features and six ML algorithms were combined to generate 54 classifiers and the output of these classifiers formed 54 feature representations, and in each feature group, we selected the feature representation with best performance for classification. Finally, the selected feature representations were integrated using logistic regression algorithm to construct the prediction model PredAPP. On the independent dataset, PredAPP achieved accuracy and AUC of 0.880 and 0.922, respectively, compared to 0.739 and 0.873 of AMPfun, a state-of-the-art method to predict APPs. The web server of PredAPP is freely accessible at http://predapp.xialab.info and https://github.com/xialab-ahu/PredAPP .

LAMP2: a major update of the database linking antimicrobial peptides

Antimicrobial peptides (AMPs) have been regarded as a potential weapon to fight against drug-resistant bacteria, which is threating the globe. Thus, more and more AMPs had been designed or identified. There is a need to integrate them into a platform for researchers to facilitate investigation and analyze existing AMPs. The AMP database has become an important tool for the discovery and transformation of AMPs as agents. A database linking antimicrobial peptides (LAMPs), launched in 2013, serves as a comprehensive tool to supply exhaustive information of AMP on a single platform. LAMP2, an updated version of LAMP, holds 23 253 unique AMP sequences and expands to link 16 public AMP databases. In the current version, there are more than 50% (12 236) sequences only linking a single database and more than 45% of AMPs linking two or more database links. Additionally, updated categories based on primary structure, collection, composition, source and function have been integrated into LAMP2. Peptides in LAMP2 have been integrated in 8 major functional classes and 38 functional activities. More than 89% (20 909) of the peptides are experimentally validated peptides. A total of 1924 references were extracted and regarded as the evidence for supporting AMP activity and cytotoxicity. The updated version will be helpful to the scientific community.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Peptides to Tackle Leishmaniasis: Current Status and Future Directions

Peptide-based drugs are an attractive class of therapeutic agents, recently recognized by the pharmaceutical industry. These molecules are currently being used in the development of innovative therapies for diverse health conditions, including tropical diseases such as leishmaniasis. Despite its socioeconomic influence on public health, leishmaniasis remains long-neglected and categorized as a poverty-related disease, with limited treatment options. Peptides with antileishmanial effects encountered to date are a structurally heterogeneous group, which can be found in different natural sources—amphibians, reptiles, insects, bacteria, marine organisms, mammals, plants, and others—or inspired by natural toxins or proteins. This review details the biochemical and structural characteristics of over one hundred peptides and their potential use as molecular frameworks for the design of antileishmanial drug leads. Additionally, we detail the main chemical modifications or substitutions of amino acid residues carried out in the peptide sequence, and their implications in the development of antileishmanial candidates for clinical trials. Our bibliographic research highlights that the action of leishmanicidal peptides has been evaluated mainly using in vitro assays, with a special emphasis on the promastigote stage. In light of these findings, and considering the advances in the successful application of peptides in leishmaniasis chemotherapy, possible approaches and future directions are discussed here.

Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.

Recent Advances in Small Peptides of Marine Origin in Cancer Therapy

Cancer is one of the leading causes of death in the world, and antineoplastic drug research continues to be a major field in medicine development. The marine milieu has thousands of biological species that are a valuable source of novel functional proteins and peptides, which have been used in the treatment of many diseases, including cancer. In contrast with proteins and polypeptides, small peptides (with a molecular weight of less than 1000 Da) have overwhelming advantages, such as preferential and fast absorption, which can decrease the burden on human gastrointestinal function. Besides, these peptides are only connected by a few peptide bonds, and their small molecular weight makes it easy to modify and synthesize them. Specifically, small peptides can deliver nutrients and drugs to cells and tissues in the body. These characteristics make them stand out in relation to targeted drug therapy. Nowadays, the anticancer mechanisms of the small marine peptides are still largely not well understood; however, several marine peptides have been applied in preclinical treatment. This paper highlights the anticancer linear and cyclic small peptides in marine resources and presents a review of peptides and the derivatives and their mechanisms.

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

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

Antimicrobial Peptides as Potential Antiviral Factors in Insect Antiviral Immune Response

Antimicrobial peptides (AMPs) with antiviral activity (antiviral peptides: AVPs) have become a research hotspot and already show immense potential to become pharmaceutically available antiviral drugs. AVPs have exhibited huge potential in inhibiting viruses by targeting various stages of their life cycle. Insects are the most speciose group of animals that inhabit almost all ecosystems and habitats on the land and are a rich source of natural AMPs. However, insect AVP mining, functional research, and drug development are still in their infancy. This review aims to summarize the currently validated insect AVPs, explore potential new insect AVPs and to discuss their possible mechanism of synthesis and action, with a view to providing clues to unravel the mechanisms of insect antiviral immunity and to develop insect AVP-derived antiviral drugs.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis

MOTIVATION

Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites.

RESULTS

After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options.

AVAILABILITY AND IMPLEMENTATION

Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antimicrobial Peptides as Anticancer Agents: Functional Properties and Biological Activities

Antimicrobial peptides (AMPs), or host defense peptides, are small cationic or amphipathic molecules produced by prokaryotic and eukaryotic organisms that play a key role in the innate immune defense against viruses, bacteria and fungi. AMPs have either antimicrobial or anticancer activities. Indeed, cationic AMPs are able to disrupt microbial cell membranes by interacting with negatively charged phospholipids. Moreover, several peptides are capable to trigger cytotoxicity of human cancer cells by binding to negatively charged phosphatidylserine moieties which are selectively exposed on the outer surface of cancer cell plasma membranes. In addition, some AMPs, such as LTX-315, have shown to induce release of tumor antigens and potent damage associated molecular patterns by causing alterations in the intracellular organelles of cancer cells. Given the recognized medical need of novel anticancer drugs, AMPs could represent a potential source of effective therapeutic agents, either alone or in combination with other small molecules, in oncology. In this review we summarize and describe the properties and the mode of action of AMPs as well as the strategies to increase their selectivity toward specific cancer cells.

ADAPTABLE: a comprehensive web platform of antimicrobial peptides tailored to the user's research

Antimicrobial peptides (AMPs) are part of the innate immune response to pathogens in all of the kingdoms of life. They have received significant attention because of their extraordinary variety of activities, in particular, as candidate drugs against the threat of super-bacteria. A systematic study of the relation between the sequence and the mechanism of action is urgently needed, given the thousands of sequences already in multiple web resources. ADAPTABLE web platform (http://gec.u-picardie.fr/adaptable) introduces the concept of "property alignment" to create families of property and sequence-related peptides (SR families). This feature provides the researcher with a tool to select those AMPs meaningful to their research from among more than 40,000 nonredundant sequences. Selectable properties include the target organism and experimental activity concentration, allowing selection of peptides with multiple simultaneous actions. This is made possible by ADAPTABLE because it not only merges sequences of AMP databases but also merges their data, thereby standardizing values and handling non-proteinogenic amino acids. In this unified platform, SR families allow the creation of peptide scaffolds based on common traits in peptides with similar activity, independently of their source.

DRAMP 2.0, an updated data repository of antimicrobial peptides

Data Repository of Antimicrobial Peptides (DRAMP, http://dramp.cpu-bioinfor.org/ ) is an open-access comprehensive database containing general, patent and clinical antimicrobial peptides (AMPs). Currently DRAMP has been updated to version 2.0, it contains a total of 19,899 entries (newly added 2,550 entries), including 5,084 general entries, 14,739 patent entries, and 76 clinical entries. The update covers new entries, structures, annotations, classifications and downloads. Compared with APD and CAMP, DRAMP contains 14,040 (70.56% in DRAMP) non-overlapping sequences. In order to facilitate users to trace original references, PubMed_ID of references have been contained in activity information. The data of DRAMP can be downloaded by dataset and activity, and the website source code is also available on dedicatedly designed download webpage. Although thousands of AMPs have been reported, only a few parts have entered clinical stage. In the paper, we described several AMPs in clinical trials, including their properties, indications and clinicaltrials.gov identifiers. Finally, we provide the applications of DRAMP in the development of AMPs.

Natural ACE inhibitory peptides discovery from Spirulina (Arthrospira platensis) strain C1

Bioactive peptides from natural sources are utilized as food supplements for disease prevention and are increasingly becoming targets for drug discovery due to their specificity, efficacy and the absence of undesirable side effects, among others. Hence, the 'SpirPep' platform was developed to facilitate the in silico-based bioactive peptide discovery of these highly sought-after biomolecules from Spirulina(Arthrospira platensis) and to select the protease (thermolysin) used for in vitro digestion. Analysis of the predicted and experimentally-derived peptides suggested that they were mainly involved in ACE inhibition; thus, an ACEi assay was used to study the ACE inhibitory activity of five candidate peptides (SpirPep1-5), chosen from common peptides with multifunctional bioactivity and 100% bioactive peptide coverage, originating from phycobiliproteins. Results showed that SpirPep1 inhibited the activity of ACE with IC₅₀ of 1.748 mM and was non-toxic to fibroblasts of African green monkey kidney and human dermal skin. The molecular docking and MD simulation analysis revealed SpirPep1 had significantly lower binding scores than others and showed greater specificity to ACE. The non-bonded interaction energy of SpirPep1 and ACE was -883 kJ/mol. The SpirPep1 indirectly bound to ACE via the ACE substrate binding sites residues (D121, E123, S516, and S517) found in natural ACE inhibitory peptides (angiotensin II and bradykinin potentiating peptides). In addition, two unreported substrate binding sites including R124 and S219 were found. These results indicate that 'SpirPep' platform could increase the success rate for natural bioactive peptide discovery.

Characterization and identification of antimicrobial peptides with different functional activities

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

Molecular Design of Peptide-Fc Fusion Drugs

Background:

Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.

Methods:

We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.

Results:

The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.

Conclusion:

Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.

Comparing sequence and structure of falcipains and human homologs at prodomain and catalytic active site for malarial peptide based inhibitor design

BACKGROUND

Falcipains are major cysteine proteases of Plasmodium falciparum involved in haemoglobin degradation and remain attractive anti-malarial drug targets. Several inhibitors against these proteases have been identified, yet none of them has been approved for malaria treatment. Other Plasmodium species also possess highly homologous proteins to falcipains. For selective therapeutic targeting, identification of sequence and structure differences with homologous human cathepsins is necessary. The substrate processing activity of these proteins is tightly controlled via a prodomain segment occluding the active site which is chopped under low pH conditions exposing the catalytic site. Current work characterizes these proteases to identify residues mediating the prodomain regulatory function for the design of peptide based anti-malarial inhibitors.

METHODS

Sequence and structure variations between prodomain regions of plasmodial proteins and human cathepsins were determined using in silico approaches. Additionally, evolutionary clustering of these proteins was evaluated using phylogenetic analysis. High quality partial zymogen protein structures were modelled using homology modelling and residue interaction analysis performed between the prodomain segment and mature domain to identify key interacting residues between these two domains. The resulting information was used to determine short peptide sequences which could mimic the inherent regulatory function of the prodomain regions. Through flexible docking, the binding affinity of proposed peptides on the proteins studied was evaluated.

RESULTS

Sequence, evolutionary and motif analyses showed important differences between plasmodial and human proteins. Residue interaction analysis identified important residues crucial for maintaining prodomain integrity across the different proteins as well as the pro-segment responsible for inhibitory mechanism. Binding affinity of suggested peptides was highly dependent on their residue composition and length.

CONCLUSIONS

Despite the conserved structural and catalytic mechanism between human cathepsins and plasmodial proteases, current work revealed significant differences between the two protein groups which may provide valuable information for selective anti-malarial inhibitor development. Part of this study aimed to design peptide inhibitors based on endogenous inhibitory portions of protease prodomains as a novel aspect. Even though peptide inhibitors may not be practical solutions to malaria at this stage, the approach followed and results offer a promising means to find new malarial inhibitors.

ImmunoSPdb: an archive of immunosuppressive peptides

Immunosuppression proved as a captivating therapy in several autoimmune disorders, asthma as well as in organ transplantation. Immunosuppressive peptides are specific for reducing efficacy of immune system with wide range of therapeutic implementations. `ImmunoSPdb’ is a comprehensive, manually curated database of around 500 experimentally verified immunosuppressive peptides compiled from 79 research article and 32 patents. The current version comprises of 553 entries providing extensive information including peptide name, sequence, chirality, chemical modification, origin, nature of peptide, its target as well as mechanism of action, amino acid frequency and composition, etc. Data analysis revealed that most of the immunosuppressive peptides are linear (91%), are shorter in length i.e. up to 20 amino acids (62%) and have L form of amino acids (81%). About 30% peptide are either chemically modified or have end terminal modification. Most of the peptides either are derived from proteins (41%) or naturally (27%) exist. Blockage of potassium ion channel (24%) is one a major target for immunosuppressive peptides. In addition, we have annotated tertiary structure by using PEPstrMOD and I-TASSER. Many user-friendly, web-based tools have been integrated to facilitate searching, browsing and analyzing the data. We have developed a user-friendly responsive website to assist a wide range of users.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

Antimicrobial peptides: Promising alternatives in the post feeding antibiotic era

Antimicrobial peptides (AMPs), critical components of the innate immune system, are widely distributed throughout the animal and plant kingdoms. They can protect against a broad array of infection-causing agents, such as bacteria, fungi, parasites, viruses, and tumor cells, and also exhibit immunomodulatory activity. AMPs exert antimicrobial activities primarily through mechanisms involving membrane disruption, so they have a lower likelihood of inducing drug resistance. Extensive studies on the structure-activity relationship have revealed that net charge, hydrophobicity, and amphipathicity are the most important physicochemical and structural determinants endowing AMPs with antimicrobial potency and cell selectivity. This review summarizes the recent advances in AMPs development with respect to characteristics, structure-activity relationships, functions, antimicrobial mechanisms, expression regulation, and applications in food, medicine, and animals.

In silico approaches for predicting the half-life of natural and modified peptides in blood

This paper describes a web server developed for designing therapeutic peptides with desired half-life in blood. In this study, we used 163 natural and 98 modified peptides whose half-life has been determined experimentally in mammalian blood, for developing in silico models. Firstly, models have been developed on 261 peptides containing natural and modified residues, using different chemical descriptors. The best model using 43 PaDEL descriptors got a maximum correlation of 0.692 between the predicted and the actual half-life peptides. Secondly, models were developed on 163 natural peptides using amino acid composition feature of peptides and achieved a maximum correlation of 0.643. Thirdly, models were developed on 163 natural peptides using chemical descriptors and attained a maximum correlation of 0.743 using 45 selected PaDEL descriptors. In order to assist researchers in the prediction and designing of half-life of peptides, the models developed have been integrated into PlifePred web server (http://webs.iiitd.edu.in//raghava/plifepred/).

BioPepDB: an integrated data platform for food-derived bioactive peptides

Food-derived bioactive peptides play critical roles in regulating most biological processes and have considerable biological, medical and industrial importance. However, a large number of active peptides data, including sequence, function, source, commercial product information, references and other information are poorly integrated. BioPepDB is a searchable database of food-derived bioactive peptides and their related articles, including more than four thousand bioactive peptide entries. Moreover, BioPepDB provides modules of prediction and hydrolysis-simulation for discovering novel peptides. It can serve as a reference database to investigate the function of different bioactive peptides. BioPepDB is available at http://bis.zju.edu.cn/biopepdbr/ . The web page utilises Apache, PHP5 and MySQL to provide the user interface for accessing the database and predict novel peptides. The database itself is operated on a specialised server.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

Annotation of Peptide Structures Using SMILES and Other Chemical Codes-Practical Solutions

Contemporary peptide science exploits methods and tools of bioinformatics, and cheminformatics. These approaches use different languages to describe peptide structures—amino acid sequences and chemical codes (especially SMILES), respectively. The latter may be applied, e.g., in comparative studies involving structures and properties of peptides and peptidomimetics. Progress in peptide science “in silico” may be achieved via better communication between biologists and chemists, involving the translation of peptide representation from amino acid sequence into SMILES code. Recent recommendations concerning good practice in chemical information include careful verification of data and their annotation. This publication discusses the generation of SMILES representations of peptides using existing software. Construction of peptide structures containing unnatural and modified amino acids (with special attention paid on glycosylated peptides) is also included. Special attention is paid to the detection and correction of typical errors occurring in SMILES representations of peptides and their correction using molecular editors. Brief recommendations for training of staff working on peptide annotations, are discussed as well.

PEPlife: A Repository of the Half-life of Peptides

Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

Topical Delivery of Protein and Peptide Using Novel Cell Penetrating Peptide IMT-P8

Skin, being the largest organ of the body, is an important site for drug administration. However, most of the drugs have poor permeability and thus drug delivery through the skin is very challenging. In this study, we examined the transdermal delivery capability of IMT-P8, a novel cell-penetrating peptide. We generated IMT-P8-GFP and IMT-P8-KLA fusion constructs and evaluated their internalization into mouse skin after topical application. Our results demonstrate that IMT-P8 is capable of transporting green fluorescent protein (GFP) and proapoptotic peptide, KLA into the skin and also in different cell lines. Interestingly, uptake of IMT-P8-GFP was considerably higher than TAT-GFP in HeLa cells. After internalization, IMT-P8-KLA got localized to the mitochondria and caused significant cell death in HeLa cells signifying an intact biological activity. Further in vivo skin penetration experiments revealed that after topical application, IMT-P8 penetrated the stratum corneum, entered into the viable epidermis and accumulated inside the hair follicles. In addition, both IMT-P8-KLA and IMT-P8-GFP internalized into the hair follicles and dermal tissue of the skin following topical application. These results suggested that IMT-P8 could be a potential candidate to be used as a topical delivery vehicle for various cosmetic and skin disease applications.