MimoDB 2.0: a mimotope database and beyond

Peptide-Based Recognition Agents of Histamine: A Biopanning Approach with Enhanced Specificity

Histamine is a biogenic amine that poses a potential threat to public health due to its toxicological effects. In this study, we identified histamine-binding peptides by screening a random 12-mer peptide library, employing a novel biopanning approach that excluded histidine-binding sequences in the final round. This additional step enhanced the selectivity of the peptides and prevented interference from histidine during detection. The binding affinities of synthesized peptides to histamine were assessed using isothermal titration calorimetry (ITC). Among the identified peptides, HBF10 (SGFRDGIEDFLW) and HBF26 (IPLENQHKIYST) showed significant affinity to histamine, with Ka values of 2.56×104 (M-1) and 8.94×104 (M-1), respectively. Notably, the identified peptides did not demonstrate binding affinity towards histidine, despite its structural similarity to histamine. Subsequently, the surface plasmon resonance (SPR) sensor surface was prepared by immobilizing the peptide HBF26 to investigate the potential of the peptide as a recognition agent for histamine detection. The findings suggest that the identified peptides have an affinity to histamine specifically, showcasing their potential applications as diagnostic agents with specific targeting capabilities.

Screening Peptide Drug Candidates To Neutralize Whole Viral Agents: A Case Study with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

The COVID-19 pandemic revealed the need for therapeutic and pharmaceutical molecule development in a short time with different approaches. Although boosting immunological memory by vaccination was the quickest and robust strategy, still medication is required for the immediate treatment of a patient. A popular approach is the mining of new therapeutic molecules. Peptide-based drug candidates are also becoming a popular avenue. To target whole pathogenic viral agents, peptide libraries can be employed. With this motivation, we have used the 12mer M13 phage display library for selecting SARS-CoV-2 targeting peptides as potential neutralizing molecules to prevent viral infections. Panning was applied with four iterative cycles to select SARS-CoV-2 targeting phage particles displaying 12-amino acid-long peptides. Randomly selected peptide sequences were synthesized by a solid-state peptide synthesis method. Later, selected peptides were analyzed by the quartz crystal microbalance method to characterize their molecular interaction with SARS-CoV-2's S protein. Finally, the neutralization activity of the selected peptides was probed with an in-house enzyme-linked immunosorbent assay. The results showed that scpep3, scpep8, and scpep10 peptides have both binding and neutralizing capacity for S1 protein as a candidate for therapeutic molecule. The results of this study have a translational potential with future in vivo and human studies.

Incorporating the Molecular Mimicry of Environmental Antigens into the Causality of Autoimmune Hepatitis

Molecular mimicry between foreign and self-antigens has been implicated as a cause of autoimmune hepatitis in experimental models and cross-reacting antibodies in patients. This review describes the experimental and clinical evidence for molecular mimicry as a cause of autoimmune hepatitis, indicates the limitations and uncertainties of this premise, and encourages investigations that assess diverse environmental antigens as sources of disease-relevant molecular mimics. Pertinent articles were identified in PubMed using multiple search phrases. Several pathogens have linear or conformational epitopes that mimic the self-antigens of autoimmune hepatitis. The occurrence of an acute immune-mediated hepatitis after vaccination for severe acute respiratory syndrome (SARS)-associated coronavirus 2 (SARS-CoV-2) has suggested that vaccine-induced peptides may mimic disease-relevant tissue antigens. The intestinal microbiome is an under-evaluated source of gut-derived antigens that could also engage in molecular mimicry. Chaperone molecules may enhance the pathogenicity of molecular mimics, and they warrant investigation. Molecular mimics of immune dominant epitopes within cytochrome P450 IID6, the autoantigen most closely associated with autoimmune hepatitis, should be sought in diverse environmental antigens and assessed for pathogenicity. Avoidance strategies, dietary adjustments, vaccine improvement, and targeted manipulation of the intestinal microbiota may emerge as therapeutic possibilities. In conclusion, molecular mimicry may be a missing causality of autoimmune hepatitis. Molecular mimics of key immune dominant epitopes of disease-specific antigens must be sought in diverse environmental antigens. The ubiquity of molecular mimicry compels rigorous assessments of peptide mimics for immunogenicity and pathogenicity in experimental models. Molecular mimicry may complement epigenetic modifications as causative mechanisms of autoimmune hepatitis.

Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes

Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na⁺ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.

Engineering of Cry3Bb1 provides mechanistic insights toward countering western corn rootworm resistance

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Cry3Bb1 engineering for receptor retargeting to counter WCR resistance described

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Phage display screens against BBMV or recombinant insecticidal-protein receptors

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77 gut binding peptides selected and engineered into 284 new variants of Cry3Bb1

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112 variants were active against susceptible but not resistant WCR

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Engineering of Cry3Bb1 Domain II loops 1 and 2 disrupted insecticidal activity

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Sites for Cry3Bb1 engineering and implications for Cry3Bb1 resistance discussed

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is an economically important pest of corn (maize) in North America and Europe. Current management practices for WCR involve transgenic expression of insecticidal proteins to minimize larval feeding damage to corn roots. The evolution of resistant WCR populations to transgenic corn expressing insecticidal proteins (e.g. Cry3Bb1, Gpp34Ab1/Tpp35Ab1) necessitates efforts to discover and deploy new modes of action for WCR control. Here, we tested the hypothesis that the addition of short peptides selected for binding to the WCR gut would restore insecticidal activity of Cry3Bb1 to resistant insects. Phage display technology coupled with deep sequencing was used to identify peptides selected for binding to WCR brush border membrane vesicles and to recombinant putative receptors aminopeptidase and cadherin. The binding and specificity of selected peptides was confirmed by ELISA and pull-down assays, and candidate gut surface binding partners were identified. Although production of 284 novel Cry3Bb1 variants with these peptides did not restore activity against resistant WCR in artificial diet bioassays, 112 variants were active against susceptible insects. These results provided insights for the mechanism of Cry3Bb1 activity and toward engineering a new mode-of-action via receptor re-targeting in the context of protein structure and function.

Peptide-Enabled Targeted Delivery Systems for Therapeutic Applications

Receptor-targeting peptides have been extensively pursued for improving binding specificity and effective accumulation of drugs at the site of interest, and have remained challenging for extensive research efforts relating to chemotherapy in cancer treatments. By chemically linking a ligand of interest to drug-loaded nanocarriers, active targeting systems could be constructed. Peptide-functionalized nanostructures have been extensively pursued for biomedical applications, including drug delivery, biological imaging, liquid biopsy, and targeted therapies, and widely recognized as candidates of novel therapeutics due to their high specificity, well biocompatibility, and easy availability. We will endeavor to review a variety of strategies that have been demonstrated for improving receptor-specificity of the drug-loaded nanoscale structures using peptide ligands targeting tumor-related receptors. The effort could illustrate that the synergism of nano-sized structures with receptor-targeting peptides could lead to enrichment of biofunctions of nanostructures.

TUPDB: Target-Unrelated Peptide Data Bank

The isolation of target-unrelated peptides (TUPs) through biopanning remains as a major problem of phage display selection experiments. These TUPs do not have any actual affinity toward targets of interest, which tend to be mistakenly identified as target-binding peptides. Therefore, an information portal for storing TUP data is urgently needed. Here, we present a TUP data bank (TUPDB), which is a comprehensive, manually curated database of approximately 73 experimentally verified TUPs and 1963 potential TUPs collected from TUPScan, the BDB database, and public research articles. The TUPScan tool has been integrated in TUPDB to facilitate TUP analysis. We believe that TUPDB can help identify and remove TUPs in future reports in the biopanning community. The database is of great importance to improving the quality of phage display-based epitope mapping and promoting the development of vaccines, diagnostics, and therapeutics. The TUPDB database is available at http://i.uestc.edu.cn/tupdb .

Peptide-Based Strategies for Targeted Tumor Treatment and Imaging

Cancer is one of the leading causes of death worldwide. The development of cancer-specific diagnostic agents and anticancer toxins would improve patient survival. The current and standard types of medical care for cancer patients, including surgery, radiotherapy, and chemotherapy, are not able to treat all cancers. A new treatment strategy utilizing tumor targeting peptides to selectively deliver drugs or applicable active agents to solid tumors is becoming a promising approach. In this review, we discuss the different tumor-homing peptides discovered through combinatorial library screening, as well as native active peptides. The different structure–function relationship data that have been used to improve the peptide’s activity and conjugation strategies are highlighted.

Streamlined phage display library protocols for identification of insect gut binding peptides highlight peptide specificity

Phage display libraries have been used to isolate insect gut binding peptides for use as pathogen transmission blocking agents, and to provide artificial anchors for increased toxicity of bacteria-derived pesticidal proteins. Previously, phage clones displaying enriched peptides were sequenced by Sanger sequencing. Here we present a streamlined protocol for identification of insect gut binding peptides, using insect-appropriate feeding strategies, with next generation sequencing and tailored bioinformatics analyses. The bioinformatics pipeline is designed to eliminate poorly enriched and false positive peptides, and to identify peptides predicted to be stable and hydrophilic. In addition to developing streamlined protocols, we also sought to address whether candidate gut binding peptides can bind to insects from more than one order, which is an important consideration for safe, practical use of peptide-modified pesticidal proteins. To this end, we screened phage display libraries for peptides that bind to the gut epithelia of two pest insects, the Asian citrus psyllid, Diaphorina citri (Hemiptera) and beet armyworm, Spodoptera exigua (Lepidoptera), and one beneficial insect, the western honey bee, Apis mellifera (Hymenoptera). While unique peptide sequences totaling 13,427 for D. citri, 89,561 for S. exigua and 69,053 for A. mellifera were identified from phage eluted from the surface of the insect guts, final candidate pools were comprised of 53, 107 and 1423 peptides respectively. The benefits of multiple rounds of biopanning, along with peptide binding properties in relation to practical use of peptide-modified pesticidal proteins for insect pest control are discussed.

Novel anti-cancer candidates from a combinatorial peptide library

STAT3 is attractive target for development of anti-cancer therapeutics as it is implicated in nearly all forms of human tumors. To identify novel leads, we screened a combinatorial peptide library displayed on the surface of M13 bacteriophage. After three rounds of biopanning, a dodecapeptide with the YYVSWPPDMMHY sequence was found to be enriched by 36% while another with a short consensus motif was displayed in 20% of the phages. Binding analysis by isothermal titration calorimetry shows the most displayed peptide interacted with a K_d of 1.79 μM, which on modification of its structure to mimic the natural binding partners of STAT3 brought the affinity to high nanomolar range (K_d  = 500 nM). Using a panel of tumor cell lines, we show that the peptides prevented the proliferation of triple-negative breast cancer cells with a moderate activity (GI₅₀  = 50 μM). Furthermore, gene expression analysis shows the peptide reduced the expression of oncoproteins critical for tumor cell proliferation, angiogenesis, and metastasis. To find novel STAT3-interacting proteins, we searched the non-redundant sequences of the National Center for Biotechnology Information database which allowed us to identify potential binding partners of the protein. In sum, our data show the identified agents could serve as useful therapeutics candidates for further development.

Recent Advances in Computational Methods for Identifying Anticancer Peptides

Anticancer peptide (ACP) is a kind of small peptides that can kill cancer cells without damaging normal cells. In recent years, ACP has been pre-clinically used for cancer treatment. Therefore, accurate identification of ACPs will promote their clinical applications. In contrast to labor-intensive experimental techniques, a series of computational methods have been proposed for identifying ACPs. In this review, we briefly summarized the current progress in computational identification of ACPs. The challenges and future perspectives in developing reliable methods for identification of ACPs were also discussed. We anticipate that this review could provide novel insights into future researches on anticancer peptides.

SSH: A Tool for Predicting Hydrophobic Interaction of Monoclonal Antibodies Using Sequences

Therapeutic antibodies are one of the most important parts of the pharmaceutical industry. They are widely used in treating various diseases such as autoimmune diseases, cancer, inflammation, and infectious diseases. Their development process however is often brought to a standstill or takes a longer time and is then more expensive due to their hydrophobicity problems. Hydrophobic interactions can cause problems on half-life, drug administration, and immunogenicity at all stages of antibody drug development. Some of the most widely accepted and used technologies for determining the hydrophobic interactions of antibodies include standup monolayer adsorption chromatography (SMAC), salt-gradient affinity-capture self-interaction nanoparticle spectroscopy (SGAC-SINS), and hydrophobic interaction chromatography (HIC). However, to measure SMAC, SGAC-SINS, and HIC for hundreds of antibody drug candidates is time-consuming and costly. To save time and money, a predictor called SSH is developed. Based on the antibody's sequence only, it can predict the hydrophobic interactions of monoclonal antibodies (mAbs). Using the leave-one-out crossvalidation, SSH achieved 91.226% accuracy, 96.396% sensitivity or recall, 84.196% specificity, 87.754% precision, 0.828 Mathew correlation coefficient (MCC), 0.919 f-score, and 0.961 area under the receiver operating characteristic (ROC) curve (AUC).

Antibody characterization using immunosignatures

Therapeutic monoclonal antibodies have the potential to work as biological therapeutics. OKT3, Herceptin, Keytruda and others have positively impacted healthcare. Antibodies evolved naturally to provide high specificity and high affinity once mature. These characteristics can make them useful as therapeutics. However, we may be missing characteristics that are not obvious. We present a means of measuring antibodies in an unbiased manner that may highlight therapeutic activity. We propose using a microarray of random peptides to assess antibody properties. We tested twenty-four different commercial antibodies to gain some perspective about how much information can be derived from binding antibodies to random peptide libraries. Some monoclonals preferred to bind shorter peptides, some longer, some preferred motifs closer to the C-term, some nearer the N-term. We tested some antibodies with clinical activity but whose function was blinded to us at the time. We were provided with twenty-one different monoclonal antibodies, thirteen mouse and eight human IgM. These antibodies produced a variety of binding patterns on the random peptide arrays. When unblinded, the antibodies with polyspecific binding were the ones with the greatest therapeutic activity. The protein target to these therapeutic monoclonals is still unknown but using common sequence motifs from the peptides we predicted several human and mouse proteins. The same five highest proteins appeared in both mouse and human lists.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

AGONOTES: A Robot Annotator for Argonaute Proteins

The argonaute protein (Ago) exists in almost all organisms. In eukaryotes, it functions as a regulatory system for gene expression. In prokaryotes, it is a type of defense system against foreign invasive genomes. The Ago system has been engineered for gene silencing and genome editing and plays an important role in biological studies. With an increasing number of genomes and proteomes of various microbes becoming available, computational tools for identifying and annotating argonaute proteins are urgently needed. We introduce AGONOTES (Argonaute Notes). It is a web service especially designed for identifying and annotating Ago. AGONOTES uses the BLASTP similarity search algorithm to categorize all submitted proteins into three groups: prokaryotic argonaute protein (pAgo), eukaryotic argonaute protein (eAgo), and non-argonaute protein (non-Ago). Argonaute proteins can then be aligned to the corresponding standard set of Ago sequences using the multiple sequence alignment program MUSCLE. All functional domains of Ago can further be curated from the alignment results and visualized easily through Bio::Graphic modules in the BioPerl bundle. Compared with existing tools such as CD-Search and available databases such as UniProt and AGONOTES showed a much better performance on domain annotations, which is fundamental in studying the new Ago. AGONOTES can be freely accessed at http://i.uestc.edu.cn/agonotes/. AGONOTES is a friendly tool for annotating Ago domains from a proteome or a series of protein sequences.

Antimicrobial Peptides and Vaccine Development to Control Multi-drug Resistant Bacteria

Antimicrobial resistance (AMR) reported to increase globally at alarming levels in the recent past. A number of potential alternative solutions discussed and implemented to control AMR in bacterial pathogens. Stringent control over the clinical application of antibiotics for a reduction in uses is a special consideration along with alternative solutions to fight against AMR. Although alternatives to conventional antibiotics like antimicrobial peptides (AMP) might warrant serious consideration to fight against AMR, there is a thriving recognition for vaccines in encountering the problem of AMR. Vaccines can reduce the prevalence of AMR by reducing the number of specific pathogens, which result in cutting down the antimicrobial need and uses. However, conventional vaccines produced using live or attenuated microorganisms while the presence of immunologically redundant biological components or impurities might cause major side effects and health related problems. Here we discussed AMPs based vaccination strategies as an emerging concept to overcome the disadvantages of traditional vaccines while boosting the AMPs to control multidrug resistant bacteria or AMR. Nevertheless, the poor immune response is a major challenge in the case of peptide vaccines as minimal antigenic epitopes used for immunization in peptide vaccines.

HMMCAS: A Web Tool for the Identification and Domain Annotations of CAS Proteins

The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) adaptive immune systems are discovered in many bacteria and most archaea. These systems are encoded by cas (CRISPR-associated) operons that have an extremely diverse architecture. The most crucial step in the depiction of cas operons composition is the identification of cas genes or Cas proteins. With the continuous increase of the newly sequenced archaeal and bacterial genomes, the recognition of new Cas proteins is becoming possible, which not only provides candidates for novel genome editing tools but also helps to understand the prokaryotic immune system better. Here, we describe HMMCAS, a web service for the detection of CRISPR-associated structural and functional domains in protein sequences. HMMCAS uses hmmscan similarity search algorithm in HMMER3.1 to provide a fast, interactive service based on a comprehensive collection of hidden Markov models of Cas protein family. It can accurately identify the Cas proteins including those fusion proteins, for example the Cas1-Cas4 fusion protein in Candidatus Chloracidobacterium thermophilum B (Cab. thermophilum B). HMMCAS can also find putative cas operon and determine which type it belongs to. HMMCAS is freely available at http://i.uestc.edu.cn/hmmcas.

PhD7Faster 2.0: predicting clones propagating faster from the Ph.D.-7 phage display library by coupling PseAAC and tripeptide composition

Selection from phage display libraries empowers isolation of high-affinity ligands for various targets. However, this method also identifies propagation-related target-unrelated peptides (PrTUPs). These false positive hits appear because of their amplification advantages. In this report, we present PhD7Faster 2.0 for predicting fast-propagating clones from the Ph.D.-7 phage display library, which was developed based on the support vector machine. Feature selection was performed against PseAAC and tripeptide composition using the incremental feature selection method. Ten-fold cross-validation results show that PhD7Faster 2.0 succeeds a decent performance with the accuracy of 81.84%, the Matthews correlation coefficient of 0.64 and the area under the ROC curve of 0.90. The permutation test with 1,000 shuffles resulted in p < 0.001. We implemented PhD7Faster 2.0 into a publicly accessible web tool (http://i.uestc.edu.cn/sarotup3/cgi-bin/PhD7Faster.pl) and constructed standalone graphical user interface and command-line versions for different systems. The standalone PhD7Faster 2.0 is able to detect PrTUPs within small datasets as well as large-scale datasets. This makes PhD7Faster 2.0 an enhanced and powerful tool for scanning and reporting faster-growing clones from the Ph.D.-7 phage display library.

Evaluation of different computational methods on 5-methylcytosine sites identification

5-Methylcytosine (m5C) plays an extremely important role in the basic biochemical process. With the great increase of identified m5C sites in a wide variety of organisms, their epigenetic roles become largely unknown. Hence, accurate identification of m5C site is a key step in understanding its biological functions. Over the past several years, more attentions have been paid on the identification of m5C sites in multiple species. In this work, we firstly summarized the current progresses in computational prediction of m5C sites and then constructed a more powerful and reliable model for identifying m5C sites. To train the model, we collected experimentally confirmed m5C data from Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Arabidopsis thaliana, and compared the performances of different feature extraction methods and classification algorithms for optimizing prediction model. Based on the optimal model, a novel predictor called iRNA-m5C was developed for the recognition of m5C sites. Finally, we critically evaluated the performance of iRNA-m5C and compared it with existing methods. The result showed that iRNA-m5C could produce the best prediction performance. We hope that this paper could provide a guide on the computational identification of m5C site and also anticipate that the proposed iRNA-m5C will become a powerful tool for large scale identification of m5C sites.

SAROTUP: a suite of tools for finding potential target-unrelated peptides from phage display data

SAROTUP (Scanner And Reporter Of Target-Unrelated Peptides) 3.1 is a significant upgrade to the widely used SAROTUP web server for the rapid identification of target-unrelated peptides (TUPs) in phage display data. At present, SAROTUP has gathered a suite of tools for finding potential TUPs and other purposes. Besides the TUPScan, the motif-based tool, and three tools based on the BDB database, i.e., MimoScan, MimoSearch, and MimoBlast, three predictors based on support vector machine, i.e., PhD7Faster, SABinder and PSBinder, are integrated into SAROTUP. The current version of SAROTUP contains 27 TUP motifs and 823 TUP sequences. We also developed the standalone SAROTUP application with graphical user interface (GUI) and command line versions for processing deep sequencing phage display data and distributed it as an open source package, which can perform perfectly locally on almost all systems that support C++ with little or no modification. The web interfaces of SAROTUP have also been redesigned to be more self-evident and user-friendly. The latest version of SAROTUP is freely available at http://i.uestc.edu.cn/sarotup3.

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.

The effect of silencing immunity related genes on longevity in a naturally occurring Anopheles arabiensis mosquito population from southwest Ethiopia

BACKGROUND

Vector control remains the most important tool to prevent malaria transmission. However, it is now severely constrained by the appearance of physiological and behavioral insecticide resistance. Therefore, the development of new vector control tools is warranted. Such tools could include immunization of blood hosts of vector mosquitoes with mosquito proteins involved in midgut homeostasis (anti-mosquito vaccines) or genetic engineering of mosquitoes that can drive population-wide knockout of genes producing such proteins to reduce mosquito lifespan and malaria transmission probability.

METHODS

To achieve this, candidate genes related to midgut homeostasis regulation need to be assessed for their effect on mosquito survival. Here, different such candidate genes were silenced through dsRNA injection in the naturally occurring Anopheles arabiensis mosquitoes and the effect on mosquito survival was evaluated.

RESULTS

Significantly higher mortality rates were observed in the mosquitoes silenced for FN3D1 (AARA003032), FN3D3 (AARA007751) and GPRGr9 (AARA003963) genes as compared to the control group injected with dsRNA against a non-related bacterial gene (LacZ). This observed difference in mortality rate between the candidate genes and the control disappeared when gene-silenced mosquitoes were treated with antibiotic mixtures, suggesting that gut microbiota play a key role in the observed reduction of mosquito survival.

CONCLUSIONS

We demonstrated that interference with the expression of the FN3D1, FN3D3 or GPRGr9 genes causes a significant reduction of the longevity of An. arabiensis mosquito in the wild.

Screening and Identification of PLK1-Polo Box Binding Peptides by High-Throughput Sequencing of Phage-Selected Libraries

BACKGROUND

Human proteome contains a plethora of short linear peptide motifs that is crucial for signaling and other cellular processes. These motifs are difficult to identify due to lack of systematic approach for their detection.

OBJECTIVES

Here we demonstrate the use of peptide phage display in combination with high throughput next generation sequencing to identify enriched peptide sequences through biopanning process against polo box domain (PBD) of mitotic polo like kinase 1 (Plk1).

METHODS

Purified recombinant Plk1 and two unrelated controls namely B-lymphocyte antigen (CD20) and fluorescent protein (mCherry) were subjected to peptide phage display analysis. Bacterially-propagated phage DNA was amplified by PCR using triplet bar coded primers to tag the pool from each amplicon.

RESULTS

Proteomic peptide phage display along with next generation sequencing and Bioinformatics analysis demonstrated several known and putative novel interactions which were potentially related to Plk1-PBD. With our strategy, we were able to identify and characterize several Plk1-PBD binding peptides, as well as define more precisely, consensus sequences.

CONCLUSION

We believe that this information could provide valuable tools for exploring novel interaction involved in Plk1 signaling as well as to choose peptides for Plk1 specific drug development.

ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank

ccPDB 2.0 (http://webs.iiitd.edu.in/raghava/ccpdb) is an updated version of the manually curated database ccPDB that maintains datasets required for developing methods to predict the structure and function of proteins. The number of datasets compiled from literature increased from 45 to 141 in ccPDB 2.0. Similarly, the number of protein structures used for creating datasets also increased from ~74 000 to ~137 000 (PDB March 2018 release). ccPDB 2.0 provides the same web services and flexible tools which were present in the previous version of the database. In the updated version, links of the number of methods developed in the past few years have also been incorporated. This updated resource is built on responsive templates which is compatible with smartphones (mobile, iPhone, iPad, tablets etc.) and large screen gadgets. In summary, ccPDB 2.0 is a user-friendly web-based platform that provides comprehensive as well as updated information about datasets.

Recent Advances on the Machine Learning Methods in Identifying DNA Replication Origins in Eukaryotic Genomics

The initiate site of DNA replication is called origins of replication (ORI) which is regulated by a set of regulatory proteins and plays important roles in the basic biochemical process during cell growth and division in all living organisms. Therefore, the study of ORIs is essential for understanding the cell-division cycle and gene expression regulation so that scholars can develop a new strategy against genetic diseases by using the knowledge of DNA replication. Thus, the accurate identification of ORIs will provide key clues for DNA replication research and clinical medicine. Although, the conventional experiments could provide accurate results, they are time-consuming and cost ineffective. On the contrary, bioinformatics-based methods can overcome these shortcomings. Especially, with the emergence of DNA sequences in the post-genomic era, it is highly expected to develop high throughput tools to identify ORIs based on sequence information. In this review, we will summarize the current progress in computational prediction of eukaryotic ORIs including the collection of benchmark dataset, the application of machine learning-based techniques, the results obtained by these methods, and the construction of web servers. Finally, we gave the future perspectives on ORIs prediction. The review provided readers with a whole background of ORIs prediction based on machine learning methods, which will be helpful for researchers to study DNA replication in-depth and drug therapy of genetic defect.

Biopanning data bank 2018: hugging next generation phage display

The 2018 update of the biopanning data bank (BDB) stores phage display data sequenced by Sanger sequencing and next generation sequencing technologies. In this work, we upgraded the database with more biopanning data sets and several new features, including (i) incorporation of next generation biopanning data and the unselected population where the target is not determined and the round of screening is zero; (ii) addition of sequencing information; (iii) improvement of browsing and searching systems and 3 D chemical structure viewer; (iv) integration of standalone tools for target-unrelated peptides analysis within conventional phage display and next generation phage display (NGPD) data. In the current version of BDB (released on 19 January 2018), the database houses 3291 sets of biopanning data collected from 1540 published articles, including 95 NGPD data sets and 3196 traditional biopanning data sets. The BDB database serves as an important and comprehensive resource for developing peptide ligands.

Database URL: The BDB database is available at http://immunet.cn/bdb

Mucus-penetrating phage-displayed peptides for improved transport across a mucus-like model

The objective of this work is to use phage display libraries as a screening tool to identify peptides that facilitate transport across the mucus barrier. Mucus is a complex selective barrier to particles and molecules, limiting penetration to the epithelial surface of mucosal tissues. In mucus-associated diseases such as cystic fibrosis (CF), mucus has increased viscoelasticity and a higher concentration of covalent and non-covalent physical entanglements compared to healthy tissues, which greatly hinders permeability and transport of drugs and particles across the mucosae for therapeutic delivery. Treatment of CF lung diseases and associated infections must overcome this abnormal mucosal barrier. Critical bottlenecks hindering effective drug penetration remain and while recent studies have shown hydrophilic, net-neutral charge polymers can improve the transport of nanoparticles and minimize interactions with mucus, there is a dearth of alternative carriers available. We hypothesized that the screening of a phage peptide library against a CF mucus model would lead to the identification of phage-displayed peptide sequences able to improve transport in mucus. These combinatorial libraries possess a large diversity of peptide-based formulations (10⁸-10⁹) to achieve unprecedented screening for potential mucus-penetrating peptides. Here, phage clones displaying discovered peptides were shown to have up to 2.6-fold enhanced diffusivity in the CF mucus model. In addition, we demonstrate reduced binding affinities to mucin compared to wild-type control. These findings suggest that phage display libraries can be used as a strategy to improve transmucosal delivery.

Peptide ligand-based ELISA reagents for antibody detection

Detection of specific antibodies has numerous research, therapeutic and diagnostic applications. Short peptide ligands that bind specifically to antibodies with continuous epitopes can be derived from epitope mapping experiments. Short peptide ligands (mimotopes) specific to antibodies with discontinuous epitopes can be identified by screening complex peptide libraries. In an effort to enhance practical utility of such peptide ligands, we describe here a simple approach to turn such target antibody-specific peptide ligands into specific ELISA detection reagents. We show that a simple addition of biotinylated peptide ligands to commonly available horseradish peroxidase (HRP)-labeled streptavidin (or HRP-anti-biotin antibody), or digoxigenin-labeled peptides to HRP-anti-digoxigenin antibody detection reagents transformed these generic detection reagents into sensitive target antibody-specific reagents. ELISA assays performed using these reagents exhibited excellent analytical properties indicating their practical utility for antibody detection. One generic detection reagent can be readily transformed into many different specific ELISA reagents by a simple mix and match design using an appropriate target-specific peptide ligand. Simplicity of preparation of these ELISA reagents for detecting antibodies should facilitate their practical applications.

Peptide Vaccines: New Trends for Avoiding the Autoimmune Response

Background:

Several marketed antiviral vaccines (such as that against hepatitis virus A and/or B, influenza virus, human papillomavirus, yellow fever virus, measles, rubella and mumps viruses) may elicit various autoimmune reactions.

Results:

The cause of autoimmune response due to vaccination may be: 1. the adjuvant which is regularly added to the vaccine (especially in the case of various oil substrates), 2. the specific viral component itself (a protein or glycoprotein potentially possessing cross-reactive epitopes) and/or 3. contamination of the vaccine with traces of non-viral proteins mostly cellular in origin. Believing that peptide vaccines might represent an optimal solution for avoiding the above-mentioned problems, we discuss the principles of rational design of a typical peptide vaccine which should contain oligopeptides coming either from the selected structural virion components (i.e.capsid proteins and/or envelop glycoproteins or both) or from the virus-coded non-structural polypeptides. The latter should be equally immunogenic as the structural virus proteins. Describing the feasibility of identification and design of immunogenic epitopes, our paper also deals with possible problems of peptide vaccine manufacturing. The presented data are in part based on the experience of our own, in part, they are coming from the results published by others.

Conclusion:

Any peptide vaccine should be able to elicit relevant and specific antibody formation, as well as an efficient cell-mediated immune response. Consequently, the properly designed peptide vaccine is expected to consist of carefully selected viral peptides, which should stimulate the receptors of helper T/CD4 cells as well as of cytotoxic (T/CD8) lymphocytes.

Comprehensive in-silico prediction of damage associated SNPs in Human Prolidase gene

Prolidase is cytosolic manganese dependent exopeptidase responsible for the catabolism of imido di and tripeptides. Prolidase levels have been associated with a number of diseases such as bipolar disorder, erectile dysfunction and varied cancers. Single nucleotide polymorphism present in coding region of proteins (nsSNPs) has the potential to alter the primary structure as well as function of the protein. Hence, it becomes necessary to differentiate the potential harmful nsSNPs from the neutral ones. 19 nsSNPs were predicted as damaging by in-silico analysis of 298 nsSNPs retrieved from dbSNP database. Consurf analysis showed 18 out of 19 substitutions were present in the conserved regions. 4 substitutions (D276N, D287N, E412K, and G448R) that observed to have damaging effect are present in catalytic pocket. Four SNPs listed in splice site region were found to affect splicing of mRNA by altering acceptor site. On 3′UTR scan of 77 SNPs listed in SNP database, 9 SNPs were lead to alter miRNA target sites. These results provide a filtered data to explore the effect of uncharacterized nsSNP and SNP related to UTRs and splice site of prolidase to find their association with the disease susceptibility and to design the target dependent drugs for therapeutics.

RicyerDB: A Database For Collecting Rice Yield-related Genes with Biological Analysis

The Rice Yield-related Database (RicyerDB) was created to complement with related research of influence rice (Oryza sativa L.) yield in multiple traits by manually curating the related databases and literature, and genomics and proteomics information that could be useful for comprehensive understanding of the rice biology. RicyerDB provides a more valuable resource in which to efficiently investigate, browse and analyze yield-related genes. The whole data set can be easily queried and downloaded through the webpage. In addition, RicyerDB also constructed a protein-protein interaction network with biological analysis. The combined rice database opens a new path to facilitate researchers achieving information on rice gene in terms of their effects on traits important for rice breeding. The web server is freely available at: http://server.malab.cn/Ricyer/index.html.

HBPred: a tool to identify growth hormone-binding proteins

Hormone-binding protein (HBP) is a kind of soluble carrier protein and can selectively and non-covalently interact with hormone. HBP plays an important role in life growth, but its function is still unclear. Correct recognition of HBPs is the first step to further study their function and understand their biological process. However, it is difficult to correctly recognize HBPs from more and more proteins through traditional biochemical experiments because of high experimental cost and long experimental period. To overcome these disadvantages, we designed a computational method for identifying HBPs accurately in the study. At first, we collected HBP data from UniProt to establish a high-quality benchmark dataset. Based on the dataset, the dipeptide composition was extracted from HBP residue sequences. In order to find out the optimal features to provide key clues for HBP identification, the analysis of various (ANOVA) was performed for feature ranking. The optimal features were selected through the incremental feature selection strategy. Subsequently, the features were inputted into support vector machine (SVM) for prediction model construction. Jackknife cross-validation results showed that 88.6% HBPs and 81.3% non-HBPs were correctly recognized, suggesting that our proposed model was powerful. This study provides a new strategy to identify HBPs. Moreover, based on the proposed model, we established a webserver called HBPred, which could be freely accessed at http://lin-group.cn/server/HBPred.

Research on folding diversity in statistical learning methods for RNA secondary structure prediction

How to improve the prediction accuracy of RNA secondary structure is currently a hot topic. The existing prediction methods for a single sequence do not fully consider the folding diversity which may occur among RNAs with different functions or sources. This paper explores the relationship between folding diversity and prediction accuracy, and puts forward a new method to improve the prediction accuracy of RNA secondary structure. Our research investigates the following: 1. The folding feature based on stochastic context-free grammar is proposed. By using dimension reduction and clustering techniques, some public data sets are analyzed. The results show that there is significant folding diversity among different RNA families. 2. To assign folding rules to RNAs without structural information, a classification method based on production probability is proposed. The experimental results show that the classification method proposed in this paper can effectively classify the RNAs of unknown structure. 3. Based on the existing prediction methods of statistical learning models, an RNA secondary structure prediction framework is proposed, namely "Cluster - Training - Parameter Selection - Prediction". The results show that, with information on folding diversity, prediction accuracy can be significantly improved.

PSBinder: A Web Service for Predicting Polystyrene Surface-Binding Peptides

Polystyrene surface-binding peptides (PSBPs) are useful as affinity tags to build a highly effective ELISA system. However, they are also a quite common type of target-unrelated peptides (TUPs) in the panning of phage-displayed random peptide library. As TUP, PSBP will mislead the analysis of panning results if not identified. Therefore, it is necessary to find a way to quickly and easily foretell if a peptide is likely to be a PSBP or not. In this paper, we describe PSBinder, a predictor based on SVM. To our knowledge, it is the first web server for predicting PSBP. The SVM model was built with the feature of optimized dipeptide composition and 87.02% (MCC = 0.74; AUC = 0.91) of peptides were correctly classified by fivefold cross-validation. PSBinder can be used to exclude highly possible PSBP from biopanning results or to find novel candidates for polystyrene affinity tags. Either way, it is valuable for biotechnology community.

Entropy is a Simple Measure of the Antibody Profile and is an Indicator of Health Status: A Proof of Concept

We have previously shown that the diversity of antibodies in an individual can be displayed on chips on which 130,000 peptides chosen from random sequence space have been synthesized. This immunosignature technology is unbiased in displaying antibody diversity relative to natural sequence space, and has been shown to have diagnostic and prognostic potential for a wide variety of diseases and vaccines. Here we show that a global measure such as Shannon's entropy can be calculated for each immunosignature. The immune entropy was measured across a diverse set of 800 people and in 5 individuals over 3 months. The immune entropy is affected by some population characteristics and varies widely across individuals. We find that people with infections or breast cancer, generally have higher entropy values than non-diseased individuals. We propose that the immune entropy as measured from immunosignatures may be a simple method to monitor health in individuals and populations.

A baculovirus-conjugated mimotope vaccine targeting Mycobacterium tuberculosis lipoarabinomannan

Mycobacterium tuberculosis, the causative agent of tuberculosis, is a major cause of morbidity and mortality worldwide. However, an effective vaccine for M. tuberculosis is lacking. We panned a phage display library using monoclonal antibodies against M. tuberculosis liporabinomannan (LAM), an important component of the M. tuberculosis cell wall, and identified two peptide sequences, HSFKWLDSPRLR or SGVYKVAYDWQH, with high antibody affinity after multiple rounds of panning. Only the HSFKWLDSPRLR peptide induced an anti-LAM response when conjugated to either keyhole limpet hemocyanin (KLH) or to the baculovirus Autographa californica multicapsid nucleopolyherovirus (AcMNPV) when introduced into mice by injection or via intranasal inoculation, respectively. Vaccination with AcMNPV conjugated HSFKWLDSPRLR peptide delayed mortality in a mouse model of tuberculosis. Thus, we report a proof of principle M. tuberculosis vaccination strategy combining an anti-LAM mimotope with a baculovirus delivery system.

Peptides for targeting βB2-crystallin fibrils

Crystallins are a major family of proteins located within the lens of the eye. Cataracts are thought to be due to the formation of insoluble fibrillar aggregates, which are largely composed of proteins from the crystallin family. Today the only cataract treatment that exists is surgery and this can be difficult to access for individuals in the developing world. Development of novel pharmacotherapeutic approaches for the treatment of cataract rests on the specific targeting of these structures. βB2-crystallin, a member of β-crystallin family, is a large component of the crystallin proteins within the lens, and as such was used to form model fibrils in vitro. Peptides were identified, using phage display techniques, that bound to these fibrils with high affinity. Fibrillation of recombinantly expressed human βB2-crystallin was performed in 10% (v/v) trifluoroethanol (TFE) solution (pH 2.0) at various temperatures, and its amyloid-like structure was confirmed using Thioflavin-T (ThT) assay, transmission electron microscopy (TEM), and X-ray fiber diffraction (XRFD) analysis. Affinity of identified phage-displayed peptides were analyzed using enzyme-linked immunosorbent assay (ELISA). Specific binding of a cyclic peptide (CKQFKDTTC) showed the highest affinity, which was confirmed using a competitive inhibition assay.

Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

A novel peptide specifically binding to VEGF receptor suppresses angiogenesis in vitro and in vivo

Vascular endothelial growth factor (VEGF), one of the most important angiogenic factors, plays an essential role in both physiological and pathological angiogenesis through binding to VEGF receptors (VEGFRs). Here we report a novel peptide designated HRHTKQRHTALH (peptide HRH), which was isolated from the Ph.D. -12 phage display library using VEGFR-Fc fusion protein as the bait. This peptide was found to dose-dependently inhibit the proliferation of human umbilical vein endothelial cells stimulated by VEGF. The anti-angiogenesis effect of the HRH peptide was further confirmed in vivo using the chick chorioallantoic membrane assay, which was also dose-dependent. Besides, peptide HRH was proved to inhibit corneal neovascularization in an alkali-burnt rat corneal model and a suture-induced rat corneal model. Taken together, these findings suggest that the HRH peptide can inhibit angiogenesis both in vitro and in vivo. Consequently, the HRHTKQRHTALH peptide might be a promising lead peptide for the development of potential angiogenic inhibitors.

Phage-displayed peptides that mimic epitopes of hepatitis E virus capsid

Hepatitis E is an emerging zoonotic infection of increasing public health threat for the UK, especially for immunosuppressed individuals. A human recombinant vaccine has been licensed only in China and is not clear whether it protects against hepatitis E virus (HEV) genotype 3, the most prevalent in Europe. The aim of this study was to use phage display technology as a tool to identify peptides that mimic epitopes of HEV capsid (mimotopes). We identified putative linear and conformational mimotopes using sera from Scottish blood donors that have the immunological imprint of past HEV infection. Four mimotopes did not have homology with the primary sequence of HEV ORF2 capsid but competed effectively with a commercial HEV antigen for binding to anti-HEV reference serum. When the reactivity profile of each mimotope was compared with Wantai HEV-IgG ELISA, the most sensitive HEV immunoassay, mimotopes showed 95.2-100% sensitivity while the specificity ranged from 81.5 to 95.8%. PepSurf algorithm was used to map affinity-selected peptides onto the ORF2 crystal structure of HEV genotype 3, which predicted that these four mimototopes are clustered in the P domain of ORF2 capsid, near conformational epitopes of anti-HEV neutralising monoclonal antibodies. These HEV mimotopes may have potential applications in the design of structural vaccines and the development of new diagnostic tests.

Novel peptides for small-caliber graft functionalization selected by a phage display of endothelial-positive/platelet-negative combined selection

A new protocol to identify peptides with EPCs high affinity and at the same time the ability to suppress the interaction with platelets was presented.

An Overview of Bioinformatics Tools and Resources in Allergy

The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

Solid-Binding Peptides in Biomedicine

Some peptides are able to bind to inorganic materials such as silica and gold. Over the past decade, Solid-binding peptides (SBPs) have been used increasingly as molecular building blocks in nanobiotechnology. These peptides show selectivity and bind with high affinity to a diverse range of inorganic surfaces e.g. metals, metal oxides, metal compounds, magnetic materials, semiconductors, carbon materials, polymers and minerals. They can be used in applications such as protein purification and synthesis, assembly and the functionalization of nanomaterials. They offer simple and versatile bioconjugation methods that can increase biocompatibility and also direct the immobilization and orientation of nanoscale entities onto solid supports without impeding their functionality. SBPs have been employed in numerous nanobiotechnological applications such as the controlled synthesis of nanomaterials and nanostructures, formation of hybrid biomaterials, immobilization of functional proteins and improved nanomaterial biocompatibility. With advances in nanotechnology, a multitude of novel nanomaterials have been designed and synthesized for diagnostic and therapeutic applications. New approaches have been developed recently to exert a greater control over bioconjugation and eventually, over the optimal and functional display of biomolecules on the surfaces of many types of solid materials. In this chapter we describe SBPs and highlight some selected examples of their potential applications in biomedicine.

Phage Display Technology in Biomaterials Engineering: Progress and Opportunities for Applications in Regenerative Medicine

The field of regenerative medicine has been gaining momentum steadily over the past few years. The emphasis in regenerative medicine is to use various in vitro and in vivo approaches that leverage the intrinsic healing mechanisms of the body to treat patients with disabling injuries and chronic diseases such as diabetes, osteoarthritis, and degenerative disorders of the cardiovascular and central nervous system. Phage display has been successfully employed to identify peptide ligands for a wide variety of targets, ranging from relatively small molecules (enzymes, cell receptors) to inorganic, organic, and biological (tissues) materials. Over the past two decades, phage display technology has advanced tremendously and has become a powerful tool in the most varied fields of research, including biotechnology, materials science, cell biology, pharmacology, and diagnostics. The growing interest in and success of phage display libraries is largely due to its incredible versatility and practical use. This review discusses the potential of phage display technology in biomaterials engineering for applications in regenerative medicine.

Blocking peptides and molecular mimicry as treatment for kidney disease

Protein mimotopes, or blocking peptides, are small therapeutic peptides that prevent protein-protein interactions by selectively mimicking a native binding domain. Inexpensive technology facilitates straightforward design and production of blocking peptides in sufficient quantities to allow preventive and therapeutic trials in both in vitro and in vivo experimental disease models. The kidney is an ideal peptide target, since small molecules undergo rapid filtration and efficient bulk absorption by tubular epithelial cells. Because the half-life of peptides is markedly prolonged in the kidneys compared with the bloodstream, blocking peptides are an attractive tool for treating diverse renal diseases, including ischemia, proteinuric states, such as membranous nephropathy and focal and segmental glomerulosclerosis, and renal cell carcinoma. Therapeutic peptides represent one of the fastest-growing reagent classes for novel drug development in human disease, partly because of their ease of administration, high binding affinity, and minimal off-target effects. This review introduces the concepts of blocking peptide design, production, and administration and highlights the potential use of therapeutic peptides to prevent or treat specific renal diseases.

Thyroid malignant neoplasm-associated biomarkers as targets for oncolytic virotherapy

Biomarkers associated with thyroid malignant neoplasm (TMN) have been widely applied in clinical diagnosis and in research oncological programs. The identification of novel TMN biomarkers has greatly improved the efficacy of clinical diagnosis. A more accurate diagnosis may lead to better clinical outcomes and effective treatments. However, the major deficiency of conventional chemotherapy and radiotherapy is lack of specificity. Due to the macrokinetic interactions, adverse side effects will occur, including chemotherapy and radiotherapy resistance. Therefore, a new treatment is urgently needed. As an alternative approach, oncolytic virotherapy may represent an opportunity for treatment strategies that can more specifically target tumor cells. In most cases, viral entry requires the expression of specific receptors on the surface of the host cell. Currently, molecular virologists and gene therapists are working on engineering oncolytic viruses with altered tropism for the specific targeting of malignant cells. This review focuses on the strategy of biomarkers for the production of novel TMN oncolytic therapeutics, which may improve the specificity of targeting of tumor cells and limit adverse effects in patients.