Peptide Database Research Articles

Potential chimeric peptides to block the SARS-CoV-2 spike receptor-binding domain

Background: There are no known medicines or vaccines to control the COVID-19 pandemic caused by SARS-CoV-2 (nCoV). Antiviral peptides are superior to conventional drugs and may also be effective against COVID-19. Hence, we investigated the SARS-CoV-2 Spike receptor-binding domain (nCoV-RBD) that interacts with hACE2 for viral attachment and entry. Methods: Three strategies and bioinformatics approaches were employed to design potential nCoV-RBD - hACE2 interaction-blocking peptides that may restrict viral attachment and entry. Firstly, the key residues interacting with nCoV-RBD - hACE2 are identified and hACE2 sequence-based peptides are designed. Second, peptides from five antibacterial peptide databases that block nCoV-RBD are identified; finally, a chimeric peptide design approach is used to design peptides that can bind to key nCoV-RBD residues. The final peptides are selected based on their physiochemical properties, numbers and positions of key residues binding, binding energy, and antiviral properties. Results: We found that: (i) three amino acid stretches in hACE2 interact with nCoV-RBD; (ii) effective peptides must bind to three key positions of nCoV-RBD (Gly485/Phe486/Asn487, Gln493, and Gln498/Thr500/Asn501); (iii) Phe486, Gln493, and Asn501 are critical residues; (iv) AC20 and AC23 derived from hACE2 may block two key critical positions; (iv) DBP6 identified from databases can block the three sites of the nCoV-RBD and interacts with one critical position, Gln498; (v) seven chimeric peptides were considered promising, among which cnCoVP-3, cnCoVP-4, and cnCoVP-7 are the top three; and (vi) cnCoVP-4 meets all the criteria and is the best peptide. Conclusions: To conclude, using three different bioinformatics approaches, we identified 17 peptides that can potentially bind to the nCoV-RBD that interacts with hACE2. Binding these peptides to nCoV-RBD may potentially inhibit the virus to access hACE2 and thereby may prevent the infection. Out of 17, 10 peptides have promising potential and need further experimental validation.

ANTIHYPERTENSIVE PEPTIDES PRODUCED BY INDIGENOUS LACTIC ACID BACTERIA FROM DADIH ORIGIN

In this study the capability of Lactobacillus plantarum ssp. plantarum DG17 and Lactococcus lactis ssp. lactis DK12 isolated from dadih origin was considered to generate antihypertensive peptides in reconstituted skim milk (RSM) medium followed bymeasuringangiotensin converting enzyme (ACE) inhibitor activity, protein profile and protein sequence. Theisolates showed high ACE inhibitor abilities i.e. 60.79±6.2% and 61.98±7.8%, with IC50 values of 439.9 μg/mL and 442.2 μg/mL respectively. Two smaller molecular weight bands ranging in size from 12 to 14 kDa were recorded in peptides profile of these isolates and assumed as antihypertensive peptides. The peptides sequence with molecule weight less than 3.0kDa were identified by LC Nano/MS and then compared withthe peptide database (Milk Bioactive Peptide Databaseand BIOPEP-UWM).One of the peptide sequence (VVVPPF) generated from Lactobacillus plantarum ssp. plantarumDG17had high ACE inhibitor activity due to the presence of F residue at the C terminal and V residue at the N terminal. The experimental result indicated that Lactococcus lactis ssp. Lactis DK12 and Lactobacillus plantarum ssp. plantarum DG17 isolates were potential developed into a starter culture in functional fermented milk.

A new opening for the tricky untargeted investigation of natural and modified short peptides

Short peptides are of extreme interest in clinical and food research fields, nevertheless they still represent a crucial analytical issue. The main aim of this paper was the development of an analytical platform for a considerable advancement in short peptides identification. For the first time, short sequences presenting both natural and post-translationally modified amino acids were comprehensively studied thanks to the generation of specific databases. Short peptide databases had a dual purpose. First, they were employed as inclusion lists for a suspect screening mass-spectrometric analysis, overcoming the limits of data dependent acquisition mode and allowing the fragmentation of such low-abundance substances. Moreover, the databases were implemented in Compound Discoverer 3.0, a software dedicated to the analysis of short molecules, for the creation of a data processing workflow specifically dedicated to short peptide tentative identification. For this purpose, a detailed study of short peptide fragmentation pathways was carried out for the first time. The proposed method was applied to the study of short peptide sequences in enriched urine samples and led to the tentative identification more than 200 short natural and modified short peptides, the highest number ever reported.

Potential chimeric peptides to block the SARS-CoV-2 spike receptor-binding domain.

Background: There are no known medicines or vaccines to control the COVID-19 pandemic caused by SARS-CoV-2 (nCoV). Antiviral peptides are superior to conventional drugs and may also be effective against COVID-19. Hence, we investigated the SARS-CoV-2 Spike receptor-binding domain (nCoV-RBD) that interacts with hACE2 for viral attachment and entry. Methods: Three strategies and bioinformatics approaches were employed to design potential nCoV-RBD - hACE2 interaction-blocking peptides that may restrict viral attachment and entry. Firstly, the key residues interacting with nCoV-RBD - hACE2 are identified and hACE2 sequence-based peptides are designed. Second, peptides from five antibacterial peptide databases that block nCoV-RBD are identified; finally, a chimeric peptide design approach is used to design peptides that can bind to key nCoV-RBD residues. The final peptides are selected based on their physiochemical properties, numbers and positions of key residues binding, binding energy, and antiviral properties. Results: We found that: (i) three amino acid stretches in hACE2 interact with nCoV-RBD; (ii) effective peptides must bind to three key positions of nCoV-RBD (Gly485/Phe486/Asn487, Gln493, and Gln498/Thr500/Asn501); (iii) Phe486, Gln493, and Asn501 are critical residues; (iv) AC20 and AC23 derived from hACE2 may block two key critical positions; (iv) DBP6 identified from databases can block the three sites of the nCoV-RBD and interacts with one critical position, Gln498; (v) seven chimeric peptides were considered promising, among which cnCoVP-3, cnCoVP-4, and cnCoVP-7 are the top three; and (vi) cnCoVP-4 meets all the criteria and is the best peptide. Conclusions: To conclude, using three different bioinformatics approaches, we identified 17 peptides that can potentially bind to the nCoV-RBD that interacts with hACE2. Binding these peptides to nCoV-RBD may potentially inhibit the virus to access hACE2 and thereby may prevent the infection. Out of 17, 10 peptides have promising potential and need further experimental validation.

2108-P: Complex Posttranslational Processing of Glucagon Prohormone in Human Pancreatic Islets

An array of signaling peptides and peptide hormones in pancreatic islets results from posttranslational processing of precursor proteins by well-characterized endopeptidases. While the encoded peptide complement can be predicted in silico from the prohormone sequence, actual peptide products often are more diverse due to additional chemical or enzymatic modifications of mature predicted peptides. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) for comprehensive molecular characterization, we compiled an experimentally deduced database of endogenous peptides in human pancreatic islets and revealed an unexpected complex processing of several prohormones including proglucagon. Live pancreatic islets from several donors were provided by the Human Pancreas Analysis Program, University of Pennsylvania. Total of 2034 chemically unique peptide sequences were identified and matched to 477 proteins in the human protein database. Among interesting observations, we find differential cleavage of the signal peptide on the glucagon precursor protein, NP_002045.1, and unusual further processing of its encoded mature peptides. Alternate signal peptide cleavage sites are confirmed by detection of both classical GRPP, R[21]-D[50], and its novel variant S[18]-D[50]. An amidated IP-2 D[131]-L[142] and an isoform of MPGF D[131]-D[178], missing the GLP-1 sequence were detected. Previously unreported N-terminus acetylation of glucagon, GLP-1, GLP-1 (7-36), and oxyntomodulin was detected. Novel peptides potentially resulted from variable processing of mature glucagon, GRPP, and oxyntomodulin at internal convention cleavage sites were characterized. New chemically unique peptides originating from proglucagon and documented in this study indicate a more complex posttranslational processing of proglucagon in human islets than previously thought. The finding expands opportunity for mechanistic studies on pathology, progression, and treatments of diabetes. Disclosure E.V. Romanova: None. S. Rubakhin: None. J.V. Sweedler: None. Funding American Diabetes Association/Pathway to Stop Diabetes (1-18-VSN-19 to J.V.S.)

<i>In silico</i> Approach Toward the Identification of Unique Peptides from Viral Protein Infection: Application to COVID-19

We describe a method for rapid in silico selection of diagnostic peptides from newly described viral pathogens and applied this approach to SARS-CoV-2/COVID-19. This approach is multi-tiered, beginning with compiling the theoretical protein sequences from genomic derived data. In the case of SARS-CoV-2 we begin with 496 peptides that would be produced by proteolytic digestion of the viral proteins. To eliminate peptides that would cause cross-reactivity and false positives we remove peptides from consideration that have sequence homology or similar chemical characteristics using a progressively larger database of background peptides. Using this pipeline, we can remove 47 peptides from consideration as diagnostic due to the presence of peptides derived from the human proteome. To address the complexity of the human microbiome, we describe a method to create a database of all proteins of relevant abundance in the saliva microbiome. By utilizing a protein-based approach to the microbiome we can more accurately identify peptides that will be problematic in COVID-19 studies which removes 12 peptides from consideration. To identify diagnostic peptides, another 7 peptides are flagged for removal following comparison to the proteome backgrounds of viral and bacterial pathogens of similar clinical presentation. By aligning the protein sequences of SARS-CoV-2 field isolates deposited to date we can identify peptides for removal due to their presence in highly variable regions that may lead to false negatives as the pathogen evolves. We provide maps of these regions and highlight 3 peptides that should be avoided as potential diagnostic or vaccine targets. Finally, we leverage publicly deposited proteomics data from human cells infected with SARS-CoV-2, as well as a second study with the closely related MERS-CoV to identify the two proteins of highest abundance in human infections. The resulting final list contains the 24 peptides most unique and diagnostic of SARS-CoV-2 infections. These peptides represent the best targets for the development of antibodies are clinical diagnostics. To demonstrate one application of this we model peptide fragmentation using a deep learning tool to rapidly generate targeted LCMS assays and data processing method for detecting CoVID-19 infected patient samples. Funding: No external funding was utilized in or toward this study. Conflict of Interest: The authors declare no competing interests.

PepFunk: a tool for peptide-centric functional analysis of metaproteomic human gut microbiome studies.

Enzymatic digestion of proteins before mass spectrometry analysis is a key process in metaproteomic workflows. Canonical metaproteomic data processing pipelines typically involve matching spectra produced by the mass spectrometer to a theoretical spectra database, followed by matching the identified peptides back to parent-proteins. However, the nature of enzymatic digestion produces peptides that can be found in multiple proteins due to conservation or chance, presenting difficulties with protein and functional assignment. To combat this challenge, we developed pepFunk, a peptide-centric metaproteomic workflow focused on the analysis of human gut microbiome samples. Our workflow includes a curated peptide database annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG) terms and a gene set variation analysis-inspired pathway enrichment adapted for peptide-level data. Analysis using our peptide-centric workflow is fast and highly correlated to a protein-centric analysis, and can identify more enriched KEGG pathways than analysis using protein-level data. Our workflow is open source and available as a web application or source code to be run locally. pepFunk is available online as a web application at https://shiny.imetalab.ca/pepFunk/ with open-source code available from https://github.com/northomics/pepFunk. dfigeys@uottawa.ca. Supplementary data are available at Bioinformatics online.

In silico identification of vaccine targets for 2019-nCoV.

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

In silico identification of vaccine targets for 2019-nCoV

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

An experimentally generated peptide database increases the sensitivity of XL-MS with complex samples

Cross-linking mass spectrometry (XL-MS) is steadily expanding its range of applications from purified protein complexes to more complex samples like organelles and even entire cells. One main challenge using non-cleavable cross-linkers is the so-called n2 problem: With linearly increasing database size, the search space for the identification of two covalently linked peptides per spectrum increases quadratically. Here, we report an alternative search strategy that focuses on only those peptides, which were demonstrated to cross-link under the applied experimental conditions. The performance of a parallel XL-MS experiment using a thiol-cleavable cross-linker enabled the identification of peptides that carried a cleaved cross-link moiety after reduction and hence were involved in cross-linking reactions. Based on these identifications, a peptide database was generated and used for the database search of the actual cross-linking experiment with a non-cleavable cross-linker. This peptide-focused approach was tested on protein complexes with a reported structural model and obtained results corresponded well to a conventional database search. An application of the strategy on in vivo cross-linked Bacillus subtilis and Bacillus cereus cells revealed a five- to tenfold reduction in search time and led to significantly more identifications with the latter species than a search against the entire proteome. SignificanceInstead of considering all theoretically cross-linkable peptides in a proteome, identification and pre-filtering for a subset of cross-link peptide candidates allows for a dramatically decreased search space. Hence, there is less potential for the random accumulation of false positives ultimately leading to a higher sensitivity in the XL-MS experiment. Using the peptide-focused approach, a cross-linking database search can be conducted in a fraction of time while yielding a similar or higher number of identifications, thereby enabling the cross-linking analysis of samples of mammalian proteome complexity.

Metastable decomposition at the peptide C-terminus: Possible use in protein identification.

The b n-1 ion of a peptide, as well as a [b n-1 + 18] ion, can be observed not only as normal product ions, but also as prominent metastable ions in a reflectron-embedded matrix-assisted laser desorption ionization time-of-flight spectrometer. The m/z values for the peaks are slightly shifted compared with the ordinary product ions and appear as relatively broad peaks, which permits them to be discriminated from other ions. A standard protein mixture and gel-derived proteins digested with LysN protease, which cleaves peptide linkages in proteins at the N-terminal side of Lys residues, were examined. The collected data were used for protein identification using in-house software, iD-plus (http://coco.protein.osaka-u.ac.jp/id-plus/), which was developed for searching for proteins in the peptide database, based on enzyme specificity (N-terminal Lys in this study), peptide masses and C-terminal amino acids. The b n-1 as well as [b n-1 + 18] ions were observed as broad ion peaks for all of the peptides (86 peptides) examined in this study. In silico calculations using the database of LysN digested peptides (11 969 470), created from 553 941 protein sequences (SwissProt: 2017_03), indicate that the use of no less than four peptides permits a protein to be identified without the need of any probability-based scoring. The preference for b n-1 ion formation is probably due to the higher propensity of the C-terminal peptide bond to be cleaved than other internal bonds. The fact that such C-terminal fragmentation takes place for most of the peptides examined suggests that the use of an N-terminal specific enzyme would allow the C-terminal amino acids to be more reliably read out than other internal sequences, information that could be efficiently used for protein identification.

In silico identification of vaccine targets for 2019-nCoV

Background: The newly identified coronavirus known as 2019-nCoV has posed a serious global health threat. According to the latest report (18-February-2020), it has infected more than 72,000 people globally and led to deaths of more than 1,016 people in China. Methods: The 2019 novel coronavirus proteome was aligned to a curated database of viral immunogenic peptides. The immunogenicity of detected peptides and their binding potential to HLA alleles was predicted by immunogenicity predictive models and NetMHCpan 4.0. Results: We report in silico identification of a comprehensive list of immunogenic peptides that can be used as potential targets for 2019 novel coronavirus (2019-nCoV) vaccine development. First, we found 28 nCoV peptides identical to Severe acute respiratory syndrome-related coronavirus (SARS CoV) that have previously been characterized immunogenic by T cell assays. Second, we identified 48 nCoV peptides having a high degree of similarity with immunogenic peptides deposited in The Immune Epitope Database (IEDB). Lastly, we conducted a de novo search of 2019-nCoV 9-mer peptides that i) bind to common HLA alleles in Chinese and European population and ii) have T Cell Receptor (TCR) recognition potential by positional weight matrices and a recently developed immunogenicity algorithm, iPred, and identified in total 63 peptides with a high immunogenicity potential. Conclusions: Given the limited time and resources to develop vaccine and treatments for 2019-nCoV, our work provides a shortlist of candidates for experimental validation and thus can accelerate development pipeline.

MtSSPdb: The Medicago truncatula Small Secreted Peptide Database.

A growing number of small secreted peptides (SSPs) in plants are recognized as important regulatory molecules with roles in processes such as growth, development, reproduction, stress tolerance, and pathogen defense. Recent discoveries further implicate SSPs in regulating root nodule development, which is of particular significance for legumes. SSP-coding genes are frequently overlooked, because genome annotation pipelines generally ignore small open reading frames, which are those most likely to encode SSPs. Also, SSP-coding small open reading frames are often expressed at low levels or only under specific conditions, and thus are underrepresented in non-tissue-targeted or non-condition-optimized RNA-sequencing projects. We previously identified 4,439 SSP-encoding genes in the model legume Medicago truncatula To support systematic characterization and annotation of these putative SSP-encoding genes, we developed the M. truncatula Small Secreted Peptide Database (MtSSPdb; https://mtsspdb.noble.org/). MtSSPdb currently hosts (1) a compendium of M. truncatula SSP candidates with putative function and family annotations; (2) a large-scale M. truncatula RNA-sequencing-based gene expression atlas integrated with various analytical tools, including differential expression, coexpression, and pathway enrichment analyses; (3) an online plant SSP prediction tool capable of analyzing protein sequences at the genome scale using the same protocol as for the identification of SSP genes; and (4) information about a library of synthetic peptides and root and nodule phenotyping data from synthetic peptide screens in planta. These datasets and analytical tools make MtSSPdb a unique and valuable resource for the plant research community. MtSSPdb also has the potential to become the most complete database of SSPs in plants.

Identification of bacterial pathogens in cultured fish with a custom peptide database constructed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)

BackgroundThe majority of infectious diseases of cultured fish is caused by bacteria. Rapid identification of bacterial pathogens is necessary for immediate management. The present study developed a custom Main Spectra Profile (MSP) database and validate the method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for rapid identification of fish bacterial pathogens. Streptococcus agalactiae, Streptococcus iniae, Aeromonas hydrophila, Aeromonas veronii, and Edwardsiella tarda obtained from diseased fish were used as representative bacterial pathogens in this study. Bacterial peptides were extracted to create a Main Spectra Profile (MSP), and the MSPs of each bacterial species was added into the MALDI Biotyper database. Fifteen additional isolates of each bacterial species were tested to validate the utilized technique.ResultsThe MSPs of all field isolates were clearly distinguishable, and the MSPs of the same species were clustered together. The identification methodology was validated with 75 bacterial isolates. The reliability and specificity of the method were determined with MALDI Biotyper log score values and matching results with 16 s rDNA sequencing. The species identification using the public MALDI Biotyper library (Bruker MALDI Biotyper) showed unreliable results (log score < 2.000) with 42.67% matching result with the reference method. In contrast, accurate identification was obtained when using the custom-made database, giving log score > 2.115, and a 100% matching result.ConclusionThis study demonstrates an effective identification of fish bacterial pathogens when a complete custom-made MSP database is applied. Further applications require a broad, well-established database to accommodate prudent identification of many fish bacterial pathogens by MALDI-TOF MS.

PlantPepDB: A manually curated plant peptide database

Plants produce an array of peptides as part of their innate defense mechanism against pathogens. The potential use of these peptides for various therapeutic purposes is increasing per diem. In order to excel in this research, the community requires web repositories that provide reliable and accurate information about these phyto-peptides. This work is an attempt to bridge the gaps in plant-based peptide research. PlantPepDB is a manually curated database that consists of 3848 plant-derived peptides among which 2821 are experimentally validated at the protein level, 458 have experimental evidence at the transcript level, 530 are predicted and only 39 peptides are inferred from homology. Incorporation of physicochemical properties and tertiary structure into PlantPepDB will help the users to study the therapeutic potential of a peptide, thus, debuts as a powerful resource for therapeutic research. Different options like Simple, Advanced, PhysicoChem and AA composition search along with browsing utilities are provided in the database for the users to execute dynamic search and retrieve the desired data. Interestingly, many peptides that were considered to possess only a single property were found to exhibit multiple properties after careful curation and merging the duplicate data that was collected from published literature and already available databases. Overall, PlantPepDB is the first database comprising detailed analysis and comprehensive information of phyto-peptides from a broad functional range which will be useful for peptide-based applied research. PlantPepDB is freely available at http://www.nipgr.ac.in/PlantPepDB/.

Comparison of Different Signal Sequences to Use for Periplasmic Over-Expression of Buforin I in Escherichia coli: An In Silico Study

Computational prediction of signal peptides is one of the most important steps in genetic engineering experiments. The periplasmic expression cause the reducing in the inherent destructive behavior of Bofurin I against its host and also reducing its susceptibility to proteolytic degradation. In order to predict the best signal peptides for expression of Buforin I in E. coli, 103 signal sequences were retired from signal peptide databases. Since the purpose of this study was to introduce the optimal signal peptides for periplasmic expression, first, sub-cellular localization site of signal peptides was analyzed. Then, n, h, and c regions of signal peptide, signal peptide probability and physico-chemical features were investigated. Base on the results, MalE, hofQ, papK, ugpB, zraP, and sfmC were introduced as the best signal peptides. For increasing the half-life of mRNA and the increasing the stability of the mRNA against exonuclease activity, secondary structures of mRNA including Shine-Dalgarno, untranslated region of ompA, start codon, signal peptide and sequences of Buforin I were analyzed. Based on the total free energy pilot evaluated and mRNA conformations, papK seemed more appropriate than the rest of the signal peptides. The obtained result of this study can be used for design the periplasmic expression constructs.

Design of Novel Antimicrobial Peptides in a Multi-Stage In Silico Approach

One of the main concerns in world public health is the increase in antimicrobial resistance. An important cause is bacteria developing resistance against naturally occurring antimicrobial peptides (AMPs), which are host defensive peptides with high selectivity of interaction with bacterial cells over mammalian cells. Here, we used an in silico design strategy that can be divided in three main stages. In the first stage we used molecular docking simulations of specifically selected peptides from PepBank peptide database to bacterial and mammalian membrane models (one simple and one complex membrane for each), in order to reduce the large amount of available peptides. Second, we used molecular dynamics simulations to propose a list of potential candidates, whose affinities to (the same) bacterial and mammalian membrane models were calculated in the third stage. The combination of these three methods has proven to be an efficient and relatively fast in silico approach that can be used to design new AMP peptides. A reasonable number of potentially effective novel AMP candidates will then be further tested and validated using experimental techniques. Such an approach has the advantage of using available peptide databases to design novel AMPs with significantly lower cost of research. Acknowledgements: This work was supported by a grant of Ministery of Research and Innovation, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2016-0032 within PNCDI III, contract no. 99/2018. Keywords: antimicrobial peptides, membrane models, molecular docking, molecular dynamics, peptide affinity.

Novel dipeptidyl peptidase-IV and angiotensin-I-converting enzyme inhibitory peptides released from quinoa protein by in silico proteolysis.

Quinoa protein has been paid more and more attention because of its nutritional properties and beneficial effects. With the development of bioinformatics, bioactive peptide database and computer‐assisted simulation provide an efficient and time‐saving method for the theoretical estimation of potential bioactivities of protein. Therefore, the potential of quinoa protein sequences for releasing bioactive peptides was evaluated using the BIOPEP database, which revealed that quinoa protein, especially globulin, is a potential source of peptides with dipeptidyl peptidase‐IV (DPP‐IV) and angiotensin‐I‐converting enzyme (ACE) inhibitory activities. Three plant proteases, namely papain, ficin, and stem bromelain, were employed for the in silico proteolysis of quinoa protein. Furthermore, four tripeptides (MAF, NMF, HPF, and MCG) were screened as novel promising bioactive peptides by PeptideRanker. The bioactivities of selected peptides were confirmed using chemical synthesis and in vitro assay. The present work suggests that quinoa protein can serve as a good source of bioactive peptides, and in silico approach can provide theoretical assistance for investigation and production of functional peptides.

Targeting the GRP78-Dependant SARS-CoV-2 Cell Entry by Peptides and Small Molecules.

The global burden of infections and the rapid spread of viral diseases show the need for new approaches in the prevention and development of effective therapies. To this end, we aimed to explore novel inhibitor compounds that can stop replication or decrease the viral load of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), for which there is currently no approved treatment. Besides using the angiotensin-converting enzyme (ACE2) receptor as a main gate, the CoV-2 can bind to the glucose-regulating protein 78 (GRP78) receptor to get into the cells to start an infection. Here, we report potential inhibitors comprising small molecules and peptides that could interfere with the interaction of SARS-CoV-2 and its target cells by blocking the recognition of the GRP78 cellular receptor by the viral Spike protein. These inhibitors were discovered through an approach of in silico screening of available databases of bioactive peptides and polyphenolic compounds and the analysis of their docking modes. This process led to the selection of 9 compounds with optimal binding affinities to the target sites. The peptides (satpdb18674, satpdb18446, satpdb12488, satpdb14438, and satpdb28899) act on regions III and IV of the viral Spike protein and on its binding sites in GRP78. However, 4 polyphenols such as epigallocatechin gallate (EGCG), homoeriodictyol, isorhamnetin, and curcumin interact, in addition to the Spike protein and its binding sites in GRP78, with the ATPase domain of GRP78. Our work demonstrates that there are at least 2 approaches to block the spread of SARS-CoV-2 by preventing its fusion with the host cells via GRP78.

In Silico Study of Different Signal Peptides to Express Recombinant Glutamate Decarboxylase in the Outer Membrane of Escherichia coli

Gamma amino butyric acid (GABA) is used as drugs, food ingredients, and dietary supplements. l-glutamate is converted to GABA by the decarboxylation reaction, which is catalyzed by the glutamate decarboxylase (GAD). Escherichia coli is widely being used to express proteins. However, without appropriate signal peptide, it cannot be applied for secretory proteins. Selecting a suitable signal peptide (SP) is a critical step in the secretory production of different proteins. In silico identification of suitable SP is a reliable and cost-effective alternative to experimental approaches. In previous studies, the localization of proteins was not considered and the SPs of periplasmic, membranes and extracellular were compared. Therefore, this study aimed to predict the best SP for the expression of recombinant GAD in the outer membrane of E. coli only. Also, we compared twelve servers to evaluate protein localization, solubility, and secretory pathway. In the present study, 127 SPs were taken from the Signal Peptide database. The localization site, physico-chemical properties, location of cleavage sites, regions and D-score of them were determined by ProtComp, ProtParam, and SignalP 3.0 and 4.1 servers, respectively. To rank SPs based on the secretion properties, PRED-TAT and SignalP 5.0 webservers were used. Based on the results, the localization site of 13 SPs was in the outer membrane of E. coli. Among them, the most suitable candidates seemed to be torT with a reasonably high D-score, aliphatic index, and GRAVY, followed by ccmH and then pspE. TorT accelerates GAD scale-up production and might be useful in future experimental research.