In silico approach for predicting toxicity of peptides and proteins

AntAngioCOOL: computational detection of anti-angiogenic peptides

Background

Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment.

Methods

A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build an efficient predictor. Also, more than 17,000 features were extracted to encode the peptides.

Results

Finally, more than 2000 informative features were selected to train the classifiers for detecting anti-angiogenic peptides. AntAngioCOOL includes three different models that can be selected by the user for different purposes; it is the most sensitive, most specific and most accurate. According to the obtained results AntAngioCOOL can effectively suggest anti-angiogenic peptides; this tool achieved sensitivity of 88%, specificity of 77% and accuracy of 75% on the independent test set. AntAngioCOOL can be accessed at https://cran.r-project.org/.

Conclusions

Only 2% of the extracted descriptors were used to build the predictor models. The results revealed that physico-chemical profile is the most important feature type in predicting anti-angiogenic peptides. Also, atomic profile and PseAAC are the other important features.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1813-7) contains supplementary material, which is available to authorized users.

Peptide from thaumatin plant protein exhibits selective anticandidal activity by inducing apoptosis via membrane receptor

Osmotin- and thaumatin-like proteins (OLPs and TLPs) have been associated with plant defense responses to different biotic stresses. In the present work, several in silico sequences from OLPs and TLPs were investigated by means of bioinformatics tools aiming to prospect for antimicrobial peptides. The peptide sequences chosen were further synthesized and characterized, and their activities and action mechanisms were assayed against some phytopathogenic fungi, bacteria and yeasts of clinical importance. From this survey approach, four peptide sequences (GDCKATSC, CPRALKVPGGCN, IVGQCPAKLKA, and CAADIVGQCPAKLK) were selected considering some chemical parameters commonly attributed to antimicrobial peptides. Antimicrobial assays showed that these peptides were unable to inhibit mycelial growth of phytopathogenic fungi and they did not affect bacterial cell growth. Nevertheless, significant inhibitory activity was found for CPRALKVPGGCN and CAADIVGQCPAKLK against Candida albicans and Saccharomyces cerevisiae. Fluorescence and scanning electron microscopy assays suggested that CAADIVGQCPAKLK did not damage the overall cell structure, or its activity was negligible on yeast membrane and cell wall integrity. However, it induced the production of reactive oxygen species (ROS) and apoptosis. Molecular docking analysis showed that CAADIVGQCPAKLK had strong affinity to interact with specific plasma membrane receptors of C. albicans and S. cerevisiae, which have been described as promoting the induction of apoptosis. The results indicate that CAADIVGQCPAKLK can be a valuable target for the development of a desired antimicrobial agent against the pathogen C. albicans.

Identification of relevant regions on structural and nonstructural proteins of Zika virus for vaccine and diagnostic test development: an in silico approach

Zika virus (ZIKV) is an arbovirus belonging to the Flaviviridae family and the genus Flavivirus. Infection with ZIKV causes a mild, self-limiting febrile illness called Zika fever. However, ZIKV infection has been recently associated with microcephaly and Guillain-Barré syndrome. Vaccines for the disease are a high priority of World Health Organization. Several studies are currently being conducted to develop a vaccine against ZIKV, but until now there is no licensed ZIKV vaccine. This study used a novel immunoinformatics approach to identify potential T-cell immunogenic epitopes present in the structural and nonstructural proteins of ZIKV. Fourteen T-cell candidate epitopes were identified on ZIKV structural and nonstructural proteins: pr^36-50; C^61-75; C^103-117; E^374-382; E^477-491; NS2a^90-104; NS2a^174-188; NS2a^179-193; NS2a^190-204; NS2a^195-209; NS2a^200-214; NS3^175-189; and NS4a^82-96; NS4a^99-113. Among these epitopes, only E^374-382 is a human leukocyte antigen (HLA) type I restricted epitope. All identified epitopes showed a low similarity with other important flaviviruses but had a high conservation rate among the ZIKV strains and a high population coverage rate. Therefore, these predicted T-cell epitopes are potential candidates targets for development of vaccines to prevent ZIKV infection.

Design of novel multi-epitope vaccines against severe acute respiratory syndrome validated through multistage molecular interaction and dynamics

Severe acute respiratory syndrome (SARS) is endemic in South China and is continuing to spread worldwide since the 2003 outbreak, affecting human population of 37 countries till present. SARS is caused by the severe acute respiratory syndrome Coronavirus (SARS-CoV). In the present study, we have designed two multi-epitope vaccines (MEVs) composed of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B cell epitopes overlap, bearing the potential to elicit cellular as well as humoral immune response. We have used truncated (residues 10-153) Onchocerca volvulus activation-associated secreted protein-1 as molecular adjuvants at N-terminal of both the MEVs. Selected overlapping epitopes of both the MEVs were further validated for stable molecular interactions with their respective human leukocyte antigen class I and II allele binders. Moreover, CTL epitopes were further studied for their molecular interaction with transporter associated with antigen processing. Furthermore, after tertiary structure modelling, both the MEVs were validated for their stable molecular interaction with Toll-like receptors 2 and 4. Codon-optimized cDNA of both the MEVs was analysed for their potential high level of expression in the mammalian cell line (Human) needed for their further in vivo testing. Overall, the present study proposes in silico validated design of two MEVs against SARS composed of specific epitopes with the potential to cause a high level of SARS-CoV specific cellular as well as humoral immune response. Communicated by Ramaswamy H. Sarma.

Identification and availability of peptides from lactoferrin in the gastrointestinal tract of mice

The metabolic fate of lactoferrin in vivo.

Identification of angiotensin converting enzyme and dipeptidyl peptidase-IV inhibitory peptides derived from oilseed proteins using two integrated bioinformatic approaches

Angiotensin-converting enzyme (ACE) and dipeptidyl peptidase-IV (DPP-IV) play critical roles in the development of hypertension and type 2 diabetes, respectively. Inhibiting ACE and DPP-IV activity using peptides has become part of new therapeutic strategies for supporting medicinal treatment of both diseases. In this study, oilseed proteins, including soybean, flaxseed, rapeseed, sunflower and sesame are evaluated for the possibility of generating ACE and DPP-IV inhibitory peptides using different integrated bioinformatic approaches (UniProt knowledgebase, ProtParam, BLAST, BIOPEP, PeptideRanker, Pepsite2 and ToxinPred), and three bovine proteins (β-lactoglobulin, β-casein and κ-casein) as comparisons. Compared with bovine proteins, the potency indices of ACE and DPP-IV inhibitory peptides, calculated using the BIOPEP database, suggest that oilseed proteins may be considered as good precursors of ACE inhibitory peptides but generate a relative lower yield of DPP-IV inhibitory peptides following subtilisin, pepsin (pH = 1.3) or pepsin (pH > 2) hydrolysis. Average scores aligned using PeptideRanker confirmed oilseed proteins as significant potential sources of bioactive peptides: over 105 peptides scored over 0.8. Pepsite2 predicted that these peptides would largely bind via Gln281, His353, Lys511, His513, Tyr520 and Tyr523 of ACE to inhibit the enzyme, while Trp629 would be the predominant binding site of peptides in reducing DPP-IV activity. All peptides were capable of inhibiting ACE and DPP-IV whilst 65 of these 105 peptides are not currently recorded in BIOPEP database. In conclusion, our in silico study demonstrates that oilseed proteins could be considered as good precursors of ACE and DPP-IV inhibitory peptides as well as so far unexplored peptides that potentially have roles in ACE and DPP-IV inhibition and beyond.

AMPs as Anti-biofilm Agents for Human Therapy and Prophylaxis

Microbial cells show a strong natural tendency to adhere to surfaces and to colonize them by forming complex communities called biofilms. In this growth mode, biofilm-forming cells encase themselves inside a dense matrix which efficiently protects them against antimicrobial agents and effectors of the immune system. Moreover, at the physiological level, biofilms contain a very heterogeneous cell population including metabolically inactive organisms and persisters, which are highly tolerant to antibiotics. The majority of human infectious diseases are caused by biofilm-forming microorganisms which are responsible for pathologies such as cystic fibrosis, infective endocarditis, pneumonia, wound infections, dental caries, infections of indwelling devices, etc. AMPs are well suited to combat biofilms because of their potent bactericidal activity of broad spectrum (including resting cells and persisters) and their ability to first penetrate and then to disorganize these structures. In addition, AMPs frequently synergize with antimicrobial compounds and were recently reported to repress the molecular pathways leading to biofilm formation. Finally, there is a very active research to develop AMP-containing coatings that can prevent biofilm formation by killing microbial cells on contact or by locally releasing their active principle. In this chapter we will describe these strategies and discuss the perspectives of the use of AMPs as anti-biofilm agents for human therapy and prophylaxis.

Identification of protegrin-1 as a stable and nontoxic scaffold among protegrin family - a computational approach

Achieving both, nontoxicity and stability in antimicrobial peptides (AMP) is a challenge. This study predicts a structurally stable, nontoxic scaffold among the protegrin family, for future therapeutic peptide analogs. Protegrins (PG) are a class of pharmaceutically approved, in demand AMPs, which require further improvement in terms of nontoxicity and stability. Out of five protegrins viz., PG1, PG2, PG3, PG4 and PG5, PG1 has been predicted as best scaffold. Prediction was based upon sequential elimination of other protegrins, using computational methods to assess the extracellular bacterial membrane penetrability, nontoxicity and structural stability by geometric observables. Initially, PG2 and PG4 showing the lowest membrane penetrability and highest toxicity respectively, were screened out. Among the remaining three protegrins, PG1 displayed both lowest root mean square deviation and radius of gyration, with a considerable occupancy of seven H-bonds and established uniform secondary structure profile throughout its ensembles. Therefore, the authors claim the superiority of PG1 as a nontoxic stable scaffold among its family. Communicated by Ramaswamy H. Sarma.

Structural basis of development of multi-epitope vaccine against Middle East respiratory syndrome using in silico approach

Background

Middle East respiratory syndrome (MERS) is caused by MERS coronavirus (MERS-CoV). Thus far, MERS outbreaks have been reported from Saudi Arabia (2013 and 2014) and South Korea (2015). No specific vaccine has yet been reported against MERS.

Purpose

To address the urgent need for an MERS vaccine, in the present study, we have designed two multi-epitope vaccines (MEVs) against MERS utilizing several in silico methods and tools.

Methods

The design of both the multi-epitope vaccines (MEVs) are composed of cytotoxic T lymphocyte (CTL) and helper T lymphocyte (HTL) epitopes, screened form thirteen different proteins of MERS-CoV. Both the MEVs also carry potential B-cell linear epitope regions, B-cell discontinuous epitopes as well as interferon-γ-inducing epitopes. Human β-defensin-2 and β-defensin-3 were used as adjuvants to enhance the immune response of MEVs. To design the MEVs, short peptide molecular linkers were utilized to link screened most potential CTL epitopes, HTL epitopes and the adjuvants. Tertiary models for both the MEVs were generated, refined, and further studied for their molecular interaction with toll-like receptor 3. The cDNAs of both MEVs were generated and analyzed in silico for their expression in a mammalian host cell line (human).

Results

Screened CTL and HTL epitopes were found to have high propensity for stable molecular interaction with HLA alleles molecules. CTL epitopes were also found to have favorable molecular interaction within the cavity of transporter associated with antigen processing. The selected CTL and HTL epitopes jointly cover upto 94.0% of worldwide human population. Both the CTL and HTL MEVs molecular models have shown to have stable binding and complex formation propensity with toll-like receptor 3. The cDNA analysis of both the MEVs have shown high expression tendency in mammalian host cell line (human).

Conclusion

After multistage in silico analysis, both the MEVs are predicted to elicit humoral as well as cell mediated immune response. Epitopes of the designed MEVs are predicted to cover large human population worldwide. Hence both the designed MEVs could be tried in vivo as potential vaccine candidates against MERS.

Biotherapeutics: Challenges and Opportunities for Predictive Toxicology of Monoclonal Antibodies

Biotherapeutics are a rapidly growing portion of the total pharmaceutical market accounting for almost one-half of recent new drug approvals. A major portion of these approvals each year are monoclonal antibodies (mAbs). During development, non-clinical pharmacology and toxicology testing of mAbs differs from that done with chemical entities since these biotherapeutics are derived from a biological source and therefore the animal models must share the same epitopes (targets) as humans to elicit a pharmacological response. Mechanisms of toxicity of mAbs are both pharmacological and non-pharmacological in nature; however, standard in silico predictive toxicological methods used in research and development of chemical entities currently do not apply to these biotherapeutics. Challenges and potential opportunities exist for new methodologies to provide a more predictive program to assess and monitor potential adverse drug reactions of mAbs for specific patients before and during clinical trials and after market approval.

Exploring dengue proteome to design an effective epitope-based vaccine against dengue virus

Dengue, a mosquito-borne disease, is caused by four known dengue serotypes. This infection causes a range of symptoms from a mild fever to a sever homorganic fever and death. It is a serious public health problem in subtropical and tropical countries. There is no specific vaccine currently available for clinical use and study on this issue is ongoing. In this study, bioinformatics approaches were used to predict antigenic, immunogenic, non-allergenic, and conserved B and T-cell epitopes as promising targets to design an effective peptide-based vaccine against dengue virus. Molecular docking analysis indicated the deep binding of the identified epitopes in the binding groove of the most popular human MHC I allele (human leukocyte antigens [HLA] A*0201). The final vaccine construct was created by conjugating the B and T-cell identified epitopes using proper linkers and adding an appropriate adjuvant at the N-terminal. The characteristics of the new subunit vaccine demonstrated that the epitope-based vaccine was antigenic, non-toxic, stable, and soluble. Other physicochemical properties of the new designed construct including isoelectric point value, aliphatic index, and grand average of hydropathicity were biologically considerable. Molecular docking of the engineered vaccine with Toll-like receptor 2 (TLR2) model revealed the hydrophobic interaction between the adjuvant and the ligand binding regions in the hydrophobic channel of TLR2. The study results indicated the high potential capability of the new multi-epitope vaccine to induce cellular and humoral immune responses against the dengue virus. Further experimental tests are required to investigate the immune protection capacity of the new vaccine construct in animal models. Communicated by Ramaswamy H. Sarma.

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/).

Production of Bioactive Peptides by Lactobacillus Species: From Gene to Application

To compensate for their amino acid auxotrophy, lactobacilli have developed the ability to hydrolyze proteins present in their environment. This proteolytic activity not only generates the free amino acids needed by the bacteria, but also a large variety of peptides, some of which are endowed with biological activities. These so-called “bioactive peptides” (BAPs) are interesting from a nutrition and healthcare perspective. The use of lactic acid bacteria (LAB) such as lactobacilli is an effective strategy for production and valorization of new BAPs. The proteolytic activity of lactobacilli is exerted in a strain- and species-dependent manner: each species exhibits different proteinase content, leading to a large variety of proteolytic activities. This underlines the high potential of Lactobacillus strains to produce novel hydrolysates and BAPs of major interest. This review aims at discussing the potential of different Lactobacillus species to release BAPs from fermentation media and processes. Strategies used for peptide production are presented. Additionally, we propose a methodology to select the most promising Lactobacillus strains as sources of BAPs. This methodology combines conventional approaches and in silico analyses.

Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus

Oropouche virus (OROV) is an emerging pathogen which causes Oropouche fever and meningitis in humans. Several outbreaks of OROV in South America, especially in Brazil, have changed its status as an emerging disease, but no vaccine or specific drug target is available yet. Our approach was to identify the epitope-based vaccine candidates as well as the ligand-binding pockets through the use of immunoinformatics. In this report, we identified both T-cell and B-cell epitopes of the most antigenic OROV polyprotein with the potential to induce both humoral and cell-mediated immunity. Eighteen highly antigenic and immunogenic CD8⁺ T-cell epitopes were identified, including three 100% conserved epitopes (TSSWGCEEY, CSMCGLIHY, and LAIDTGCLY) as the potential vaccine candidates. The selected epitopes showed 95.77% coverage for the mixed Brazilian population. The docking simulation ensured the binding interaction with high affinity. A total of five highly conserved and nontoxic linear B-cell epitopes "NQKIDLSQL," "HPLSTSQIGDRC," "SHCNLEFTAITADKIMSL," "PEKIPAKEGWLTFSKEHTSSW," and "HHYKPTKNLPHVVPRYH" were selected as potential vaccine candidates. The predicted eight conformational B-cell epitopes represent the accessibility for the entered virus. In the posttherapeutic strategy, ten ligand-binding pockets were identified for effective inhibitor design against emerging OROV infection. Collectively, this research provides novel candidates for epitope-based peptide vaccine design against OROV.

Conserved epitopes in variants of amastin protein of Trypanosoma cruzi for vaccine design: A bioinformatics approach

Chagas disease caused by protozoan parasite Trypanosoma cruzi is endemic disease in South and Central American countries but due to migrating human populations it has shown emergence in Europe, North America and Australia. With only two drugs, benznidazole and nifurtimox for its treatment there is need for newer therapies. In the current study, we have tried to analyse the potential of amastin, a major surface protein as a vaccine target using bioinformatics tools. Using 282 variants of this protein available in NCBI protein database we have found out five conserved potential T_c cell and two T_H cell epitopes. These epitopes are conserved in more than 90% of the cohort of sequences used in the study. The epitopes showed binding to the peptide binding cleft of HLA-A02 and HLA-DR molecules. With coverage of pan world populations and being non-toxic and non-allergic these epitopes could be used for future vaccine applications.

The potential HLA Class I-restricted epitopes derived from LeIF and TSA of Leishmania donovani evoke anti-leishmania CD8+ T lymphocyte response

To explore new protective measure against visceral leishmaniasis, reverse vaccinology approach was employed to identify key immunogenic regions which can mediate long-term immunity. In-depth computational analysis revealed nine promiscuous epitopes which can possibly be presented by 46 human leukocyte antigen, thereby broadening the worldwide population up to 94.16%. This is of reasonable significance that most of the epitopes shared 100% sequence homology with other Leishmania species and could evoke a common pattern of protective immune response. Transporter associated with antigen processing binding affinity, molecular docking approach followed by dynamics simulation and human leukocyte antigen stabilization assay suggested that the best five optimal set of epitopes bind in between α1 and α2 binding groove with sufficient affinity and stability which allows the translocation of intact epitope to the cell surface. Fascinatingly, the human leukocyte antigen stabilization assay exhibited a modest correlation with the positive immunogenicity score predicted by class I pMHC immunogenicity predictor. A support for this notion came from ELISA and FACS analysis where the epitopes as a cocktail induced CD8+ IFN-γ and Granzyme B levels significantly in treated visceral leishmaniasis subject which suggests the immunogenic ability of the selected epitopes.

In silico identification of immunodominant B-cell and T-cell epitopes of non-structural proteins of Usutu Virus

Usutu Virus (USUV; flavivirus) is a re-emerging pathogen invading the territories of European countries, Asia, and Africa. It is a mosquito-borne zoonotic virus with a bi-directional transmission route from animal to human and vice versa, and causes neurological disorders such as meningoencephalitis in bats, Homo sapiens, birds and horses. Due to limited availability of information about USUV and its deleterious effects on neural cells causing neurologic impairments, it becomes imperative to study this virus in detail to equip ourselves with a solution beforehand. The current study aims to identify immunodominant peptides that could be exploited in future for designing global peptide vaccine for combating the infections caused by USUV. In this study, an immunoinformatics approach was applied to evaluate the immunogenicity of 7 non-structural proteins and determined 64 continuous B-cell epitopes, numerous probable discontinuous B-cell epitopes, 64 MHC Class-I binders, 126 MHC class-II binders and 52 promiscuous binders with a maximum population coverage of 98.55%(MHC Class-I binder ofYP_164815.1 NS4a) and 81.81% (MHC Class-II binders of YP_164812.1 NS2a, YP_164813.1 NS2b, YP_164814.1 NS3, YP_164817.1 NS4b, YP_164818.1 NS5). Further, studies involving experimental validation of these predicted epitopes is warranted to ensure the potential of B-cells and T-cells stimulation for their effective use as vaccine candidates, and as diagnostic agents against USUV.

Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features

Tuberculosis is one of the leading cause of death worldwide, particularly due to evolution of drug resistant strains. Antitubercular peptides may provide an alternate approach to combat antibiotic tolerance. Sequence analysis reveals that certain residues (e.g., Lysine, Arginine, Leucine, Tryptophan) are more prevalent in antitubercular peptides. This study describes the models developed for predicting antitubercular peptides by using sequence features of the peptides. We have developed support vector machine based models using different sequence features like amino acid composition, binary profile of terminus residues, dipeptide composition. Our ensemble classifiers that combines models based on amino acid composition and N5C5 binary pattern, achieves highest Acc of 73.20% with 0.80 AUROC on our main dataset. Similarly, the ensemble classifier achieved maximum Acc 75.62% with 0.83 AUROC on secondary dataset. Beside this, hybrid model achieves Acc of 75.87 and 78.54% with 0.83 and 0.86 AUROC on main and secondary dataset, respectively. In order to facilitate scientific community in designing of antitubercular peptides, we implement above models in a user friendly webserver (http://webs.iiitd.edu.in/raghava/antitbpred/).

Identification and molecular docking study of novel angiotensin-converting enzyme inhibitory peptides from Salmo salar using in silico methods

BACKGROUND

In order to circumvent some challenges of the classical approach, the in silico method has been applied to the discovery of angiotensin-converting enzyme (ACE) inhibitory peptides from food proteins. In this study, some convenient and efficient in silico tools were utilized to identify novel ACE inhibitory peptides from Salmo salar.

RESULTS

Collagen from Salmo salar was digested in silico into hundreds of peptides. Results revealed that tetrapeptides PGAR and IGPR showed potent ACE inhibitory activity, with IC₅₀ values of 0.598 ± 0.12 and 0.43 ± 0.09 mmol L^-1 , respectively. The molecular docking result showed that PGAR and IGPR interact with ACE mostly via hydrogen bonds and attractive charge. Peptide IGPR interacts with Zn⁺ at the ACE active site, showing high inhibitory activity.

CONCLUSION

Interaction with Zn⁺ in ACE may lead to higher inhibitory activity of peptides, and Pi interactions may promote the effect of peptides on ACE. The in silico method can be an effective method to predict potent ACE inhibitory peptides from food proteins. © 2018 Society of Chemical Industry.

Venomix: a simple bioinformatic pipeline for identifying and characterizing toxin gene candidates from transcriptomic data

The advent of next-generation sequencing has resulted in transcriptome-based approaches to investigate functionally significant biological components in a variety of non-model organism. This has resulted in the area of “venomics”: a rapidly growing field using combined transcriptomic and proteomic datasets to characterize toxin diversity in a variety of venomous taxa. Ultimately, the transcriptomic portion of these analyses follows very similar pathways after transcriptome assembly often including candidate toxin identification using BLAST, expression level screening, protein sequence alignment, gene tree reconstruction, and characterization of potential toxin function. Here we describe the Python package Venomix, which streamlines these processes using common bioinformatic tools along with ToxProt, a publicly available annotated database comprised of characterized venom proteins. In this study, we use the Venomix pipeline to characterize candidate venom diversity in four phylogenetically distinct organisms, a cone snail (Conidae; Conus sponsalis), a snake (Viperidae; Echis coloratus), an ant (Formicidae; Tetramorium bicarinatum), and a scorpion (Scorpionidae; Urodacus yaschenkoi). Data on these organisms were sampled from public databases, with each original analysis using different approaches for transcriptome assembly, toxin identification, or gene expression quantification. Venomix recovered numerically more candidate toxin transcripts for three of the four transcriptomes than the original analyses and identified new toxin candidates. In summary, we show that the Venomix package is a useful tool to identify and characterize the diversity of toxin-like transcripts derived from transcriptomic datasets. Venomix is available at: https://bitbucket.org/JasonMacrander/Venomix/.

Short Oligopeptides for Biocompatible and Biodegradable Supramolecular Hydrogels

Short Phe-rich oligopeptides, consisting of only four and five amino acids, worked as effective supramolecular hydrogelators for buffer solutions at low gelator concentrations (0.5-1.5 wt %). Among 10 different oligopeptides synthesized, peptide P1 (Ac-Phe-Phe-Phe-Gly-Lys) showed high gelation ability. Transmission electron microscopy observations suggested that the peptide molecules self-assembled into nanofibrous networks, which turned into gels. The hydrogel of peptide P1 showed reversible thermal gel-sol transition and viscoelastic properties typical of a gel. Circular dichroism spectra revealed that peptide P1 formed a β-sheetlike structure, which decreased with increasing temperature. The self-assembly of peptide P1 occurred even in the presence of nutrients in culture media and common surfactants. Escherichia coli and yeast successfully grew on the hydrogel. The hydrogel exhibited low cytotoxicity to animal cells. Finally, we demonstrated that functional compounds can be released from the hydrogel in different manners based on the interaction between the compounds and the hydrogel.

Bioactive hydrolysates from casein: generation, identification, and in silico toxicity and allergenicity prediction of peptides

BACKGROUND

Bioactive casein peptides have attracted considerable attention for their applications in industry. However, there is little clarity regarding mass spectrometric profiles for peptides in enzymatic hydrolysates of casein produced under varying conditions. In this study, the compositions of the peptides from casein hydrolysates were compared for different enzyme/substrate ratio (E/S) and hydrolysis times. The toxicity, allergenicity and bioactivity of the identified peptides were assessed in silico.

RESULTS

A total of 70 unique peptides were identified, and there were 28, 21, 13 and 8 peptides from α_s1 -casein, α_s2 -casein, β-casein and κ-casein respectively. The peptide number decreased with the increase in E/S and hydrolysis time. Moreover, peptides with relative molecular mass M_r ranging from 1000 to 1500 Da occupied the highest proportion of 31.43%, and almost all of the peptides showed M_r less than 5000 Da. In silico analysis showed that all of the peptides were non-toxic and non-allergenic, and several of them were assessed by PeptideRanker as having a relatively high likelihood of being bioactive peptides.

CONCLUSIONS

Composition of the peptides in the casein hydrolysates varied with the enzymolysis conditions. This study's results may facilitate the production of target bioactive peptides by controlling E/S and hydrolysis time, which is beneficial for the application of casein peptides in the functional food industry. © 2017 Society of Chemical Industry.

Subtractive proteomics to identify novel drug targets and reverse vaccinology for the development of chimeric vaccine against Acinetobacter baumannii

The emergence of drug-resistant Acinetobacter baumannii is the global health problem associated with high mortality and morbidity. Therefore it is high time to find a suitable therapeutics for this pathogen. In the present study, subtractive proteomics along with reverse vaccinology approaches were used to predict suitable therapeutics against A. baumannii. Using subtractive proteomics, we have identified promiscuous antigenic membrane proteins that contain the virulence factors, resistance factors and essentiality factor for this pathogenic bacteria. Selected promiscuous targeted membrane proteins were used for the design of chimeric-subunit vaccine with the help of reverse vaccinology. Available best tools and servers were used for the identification of MHC class I, II and B cell epitopes. All selected epitopes were further shortlisted computationally to know their immunogenicity, antigenicity, allergenicity, conservancy and toxicity potentials. Immunogenic predicted promiscuous peptides used for the development of chimeric subunit vaccine with immune-modulating adjuvants, linkers, and PADRE (Pan HLA-DR epitopes) amino acid sequence. Designed vaccine construct V4 also interact with the MHC, and TLR4/MD2 complex as confirm by docking and molecular dynamics simulation studies. Therefore designed vaccine construct V4 can be developed to control the host-pathogen interaction or infection caused by A. baumannii.

Proteomics assisted profiling of antimicrobial peptide signatures from black pepper (Piper nigrum L.)

Plant antimicrobial peptides are the interesting source of studies in defense response as they are essential components of innate immunity which exert rapid defense response. In spite of abundant reports on the isolation of antimicrobial peptides (AMPs) from many sources, the profile of AMPs expressed/identified from single crop species under certain stress/physiological condition is still unknown. This work describes the AMP signature profile of black pepper and their expression upon Phytophthora infection using label-free quantitative proteomics strategy. The differential expression of 24 AMPs suggests that a combinatorial strategy is working in the defense network. The 24 AMP signatures belonged to the cationic, anionic, cysteine-rich and cysteine-free group. As the first report on the possible involvement of AMP signature in Phytophthora infection, our results offer a platform for further study on regulation, evolutionary importance and exploitation of theses AMPs as next generation molecules against pathogens.

Analysis of epitope-based vaccine candidates against the E antigen of the hepatitis B virus based on the B genotype sequence: An in silico and in vitro approach

Chronic hepatitis B virus infection is a worldwide health problem with no current effective strategy to achieve a cure. The Hepatitis B virus (HBV) E antigen (HBeAg) has a negative effect on the immune system and a therapeutic vaccine is a promising strategy in order to treat chronic virus infection. In this study, we analyzed and identified the MHC-I, MHC-II and B cell epitopes of the HBeAg based on a B genotype sequence of HBV using a bioinformatic approach and in vitro experiments. The computational approach provided us with four epitopes (LLWFHISCL, YLVSFGVWI, MQLFHLCLI, TVLEYLVSF) of the specific MHC-I allele HLA-A0201 that conformed to all criteria. Molecular docking and a peptide binding assay showed that epitope TVLEYLVSF had the lowest binding energy and epitope LLWFHISCL had the highest binding affinity to the HLA-A0201 molecule. An interferonγenzyme-linked immunospot assay and cytotoxicity assay revealed that epitope LLWFHISCL had the highest ability to induce and stimulate T cells. Furthermore, we determined four core peptides of MHC-II epitopes and a region of the B cell epitope. The epitopes and region identified in this research may be helpful in designing epitope-based vaccines and boosting the mechanism research of HBeAg and its effect on the immune system.

Analysis and Evaluation of the Inhibitory Mechanism of a Novel Angiotensin-I-Converting Enzyme Inhibitory Peptide Derived from Casein Hydrolysate

Casein hydrolysates exert various biological activities, and the responsible functional peptides are being identified from them continuously. In this study, the tryptic casein hydrolysate was fractionated by an ultrafiltration membrane (3 kDa), and the peptides were identified by capillary electrophoresis-quadrupole-time-of-flight-tandem mass spectrometry. Meanwhile, in silico methods were used to analyze the toxicity, solubility, stability, and affinity between the peptides and angiotensin-I-converting enzyme (ACE). Finally, a new angiotensin-I-converting enzyme inhibitory (ACEI) peptide, EKVNELSK, derived from α_s1-casein (fragment 35-42) was screened. The half maximal inhibitory concentration value of the peptide is 5.998 mM, which was determined by a high-performance liquid chromatography method. The Lineweaver-Burk plot indicated that this peptide is a mixed-type inhibitor against ACE. Moreover, Discovery Studio 2017 R2 software was adopted to perform molecular docking to propose the potential mechanisms underlying the ACEI activity of the peptide. These results indicated that EKVNELSK is a new ACEI peptide identified from casein hydrolysate.

Peptide based therapeutics and their use for the treatment of neurodegenerative and other diseases

Bioactive peptides are actively involved in different biological functions and importantly contribute to human health, and the use of peptides as therapeutics has a long successful history in disease management. A number of peptides have wide-ranging therapeutic effects, such as antioxidant, antimicrobial, and antithrombotic effects. Neurodegenerative diseases are typically caused by abnormal aggregations of proteins or peptides, and the depositions of these aggregates in or on neurons, disrupt signaling and eventually kill neurons. During recent years, research on short peptides has advanced tremendously. This review offers a brief introduction to peptide based therapeutics and their application in disease management and provides an overview of peptide vaccines, and toxicity related issues. In addition, the importance of peptides in the management of different neurodegenerative diseases and their therapeutic applications is discussed. The present review provides an understanding of peptides and their applications for the management of different diseases, but with focus on neurodegenerative diseases. The role of peptides as anti-cancer, antimicrobial and antidiabetic agents has also been discussed.

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.

In Silico Approach for Prediction of Antifungal Peptides

This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server ‘Antifp’ (http://webs.iiitd.edu.in/raghava/antifp).

Identification and Antithrombotic Activity of Peptides from Blue Mussel (Mytilus edulis) Protein

The blue mussel (Mytilus edulis) reportedly contains many bioactive components of nutritional value. Water-, salt- and acid-soluble M. edulis protein fractions were obtained and the proteins were trypsinized. The resultant peptides were analyzed by ultra-performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS). 387 unique peptides were identified that matched 81 precursor proteins. Molecular mass distributions of the proteins and peptides were analyzed by sodium dodecyl sulfate-polyacryl amide gel electrophoresis (SDS-PAGE). The differences between the three protein samples were studied by Venn diagram of peptide and protein compositions. Toxicity, allergic and antithrombotic activity of peptides was predicted using database website and molecular docking respectively. The antithrombotic activity of enzymatic hydrolysate from water-, salt- and acid-soluble M. edulis protein were 40.17%, 85.74%, 82.00% at 5 mg/mL, respectively. Active mechanism of antithrombotic peptide (ELEDSLDSER) was also research about amino acid binding sites and interaction, simultaneously.

Identification and the molecular mechanism of a novel myosin-derived ACE inhibitory peptide

The objective of this work was to identify a novel ACE inhibitory peptide from myosin using a number of in silico methods.

Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation

Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.

Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

Immune regulation effect of lienal polypeptides extract in Lewis lung carcinoma-bearing mice treated with cyclophosphamide

Polypeptides extracted from animal immune organs have been proved to exert immunomodulatory activities in previous reports. However, relative experimental data regarding the influence of a polypeptide mixture extracted from healthy calf spleen (lienal polypeptide [LP]) on the immune function in tumor therapy are limited, and the components in LP remain unclear. In the present study, the immune regulatory effect of LP was investigated in normal mice and Lewis lung carcinoma (LLC)-bearing mice treated with cyclophosphamide (CTX). The components of LP were identified by liquid chromatography-electrospray ionization-coupled with tandem mass spectrometry (LC-MS/MS) analysis and bioinformatic analysis. In LLC-bearing mice, LP showed a synergic antitumor effect with CTX, whereas LP alone did not present direct antitumor activity. Further, LP was found to enhance immune organ indexes, splenocyte number, and T lymphocyte subsets in normal mice and LLC-bearing mice treated with CTX. The decline of white blood cell and platelet counts, splenocyte proliferation activity, and peritoneal macrophage phagocytic function caused by CTX were also significantly suppressed by LP treatment in LLC-bearing mice. Notably, LP treatment significantly decreased the expression of phagocytosis-related proteins including CD47/signal regulatory protein α/Src homology phosphatase-1 in the tumor tissue of LLC-bearing mice treated with CTX. LC-MS/MS-based peptidomics unraveled the main polypeptides in LP with a length from 8 to 25 amino acids. Bioinformatics analysis further confirmed the possibility of LP to regulate immunity, especially in phagocytosis-related pathway. Our above findings indicated that LP can relieve the immunosuppression induced by chemotherapy and is a beneficial supplement in cancer therapy. Impact statement The immunomodulatory activities of polypeptides extracted from animal immune organs have incurred people's interests since a long time ago. In this study, we investigated the immune regulation effects of a polypeptide mixture extracted from health calf spleen (lienal polypeptide [LP]) in Lewis lung carcinoma-bearing mice treated with cyclophosphamide (CTX). Liquid chromatography-electrospray ionization-coupled with tandem mass spectrometry-based peptidomics and bioinformatics analysis unraveled the main polypeptides in LP and further confirmed that LP is mainly associated with immune regulating pathway, especially in tumor cell phagocytosis-related pathway. Our study for the first time revealed that polypeptides from spleen can relieve the immunosuppression induced by CTX and is a beneficial supplement in cancer therapy.

Immune responses to highly conserved influenza A virus matrix 1 peptides

Influenza vaccine development is considered to be complicated and challenging. Constantly evolving influenza viruses require continuous global monitoring and reformulation of the vaccine strains. Peptides that are conserved among different strains and subtypes of influenza A virus are strongly considered to be attractive targets for development of cross protective influenza vaccines that stimulate cellular responses. In this study, three highly conserved (>90%) matrix 1 peptides that contain multiple T cell epitopes, ILGFVFTLTVPSERGLQRRRF (P_M 1), LIRHENRMVLASTTAKA (P_M 2) and LQAYQKRMGVQMQR (P_M 3), were assessed for their immunogenic potential in vitro by subjecting peripheral blood mononuclear cells from healthy volunteers to repetitive stimulation with these chemically synthesised peptides and measuring their IFN-γ concentrations, proliferation by ELISA, and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, respectively. Seven samples were screened for immunogenicity of P_M 1 and P_M 2, and six for that of P_M 3. All six samples had positive responses (IFN-γ secretion) to P_M 3 stimulation, as did five and three for P_M 2 and P_M 1 respectively. In contrast, seven (P_M 1 and P_M 2) and four (P_M 3) samples showed proliferative response as compared with unstimulated cells. The encouraging immunogenic response generated by these highly conserved matrix 1 peptides indicates they are prospective candidates for development of broadly reactive influenza vaccines.

Computer-Aided Design of an Epitope-Based Vaccine against Epstein-Barr Virus

Epstein-Barr virus is a very common human virus that infects 90% of human adults. EBV replicates in epithelial and B cells and causes infectious mononucleosis. EBV infection is also linked to various cancers, including Burkitt's lymphoma and nasopharyngeal carcinomas, and autoimmune diseases such as multiple sclerosis. Currently, there are no effective drugs or vaccines to treat or prevent EBV infection. Herein, we applied a computer-aided strategy to design a prophylactic epitope vaccine ensemble from experimentally defined T and B cell epitopes. Such strategy relies on identifying conserved epitopes in conjunction with predictions of HLA presentation for T cell epitope selection and calculations of accessibility and flexibility for B cell epitope selection. The T cell component includes 14 CD8 T cell epitopes from early antigens and 4 CD4 T cell epitopes, targeted during the course of a natural infection and providing a population protection coverage of over 95% and 81.8%, respectively. The B cell component consists of 3 experimentally defined B cell epitopes from gp350 plus 4 predicted B cell epitopes from other EBV envelope glycoproteins, all mapping in flexible and solvent accessible regions. We discuss the rationale for the formulation and possible deployment of this epitope vaccine ensemble.

In silico methods for design of biological therapeutics

It has been twenty years since the first rationally designed small molecule drug was introduced into the market. Since then, we have progressed from designing small molecules to designing biotherapeutics. This class of therapeutics includes designed proteins, peptides and nucleic acids that could more effectively combat drug resistance and even act in cases where the disease is caused because of a molecular deficiency. Computational methods are crucial in this design exercise and this review discusses the various elements of designing biotherapeutic proteins and peptides. Many of the techniques discussed here, such as the deterministic and stochastic design methods, are generally used in protein design. We have devoted special attention to the design of antibodies and vaccines. In addition to the methods for designing these molecules, we have included a comprehensive list of all biotherapeutics approved for clinical use. Also included is an overview of methods that predict the binding affinity, cell penetration ability, half-life, solubility, immunogenicity and toxicity of the designed therapeutics. Biotherapeutics are only going to grow in clinical importance and are set to herald a new generation of disease management and cure.