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Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library.

ABSTRACT: Dengue being one of the deadliest diseases of tropical regions, enforces to put continuous efforts for the development of vaccine and effective therapeutics. Most of the antibodies generated during dengue infection are non-neutralizing and cause antibody dependent enhancement. Hence, making a potent neutralizing antibody against all four dengue serotypes could be very effective for the treatment. However, designing a single antibody for all serotypes is difficult due to variation in protein sequences. Therefore, the objective is to identify conserved region of dengue envelope protein and then develop an antibody against that conserved region. Before advancing to the development of such an antibody, it is desirable to validate the interactions between antibody and dengue envelope protein. In silico analysis of such interactions provides a good platform to find out a suitable region to design and construct an antibody against it by analyzing antigen-antibody interaction before synthesizing the antibody. In this study, two highly conserved regions of dengue envelope protein were identified and an scFv was constructed against it. Both scFv and FuBc proteins were expressed in bacterial expression system and binding efficiency was analyzed by SPR analysis with KD value 2.3 ?M. In order to improve binding efficiency, an in silico scFv mutant library was created which was virtually screened for higher binding efficiency. Six mutants with high binding efficiency were selected for further analysis. The binding ability of these mutants were predicted using simulation analysis which shows these mutations were stabilizing scFv-FuBc complex.

SUBMITTER: Rathore AS

PROVIDER: S-EPMC6328183 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library.

Rathore Abhishek Singh AS Sarker Animesh A Gupta Rinkoo Devi RD

PloS one 20190110 1

Dengue being one of the deadliest diseases of tropical regions, enforces to put continuous efforts for the development of vaccine and effective therapeutics. Most of the antibodies generated during dengue infection are non-neutralizing and cause antibody dependent enhancement. Hence, making a potent neutralizing antibody against all four dengue serotypes could be very effective for the treatment. However, designing a single antibody for all serotypes is difficult due to variation in protein sequ ...[more]

PMID: 30629625

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Project description:BackgroundCell penetrating peptides have gained much recognition as a versatile transport vehicle for the intracellular delivery of wide range of cargoes (i.e. oligonucelotides, small molecules, proteins, etc.), that otherwise lack bioavailability, thus offering great potential as future therapeutics. Keeping in mind the therapeutic importance of these peptides, we have developed in silico methods for the prediction of cell penetrating peptides, which can be used for rapid screening of such peptides prior to their synthesis.MethodsIn the present study, support vector machine (SVM)-based models have been developed for predicting and designing highly effective cell penetrating peptides. Various features like amino acid composition, dipeptide composition, binary profile of patterns, and physicochemical properties have been used as input features. The main dataset used in this study consists of 708 peptides. In addition, we have identified various motifs in cell penetrating peptides, and used these motifs for developing a hybrid prediction model. Performance of our method was evaluated on an independent dataset and also compared with that of the existing methods.ResultsIn cell penetrating peptides, certain residues (e.g. Arg, Lys, Pro, Trp, Leu, and Ala) are preferred at specific locations. Thus, it was possible to discriminate cell-penetrating peptides from non-cell penetrating peptides based on amino acid composition. All models were evaluated using five-fold cross-validation technique. We have achieved a maximum accuracy of 97.40% using the hybrid model that combines motif information and binary profile of the peptides. On independent dataset, we achieved maximum accuracy of 81.31% with MCC of 0.63.ConclusionThe present study demonstrates that features like amino acid composition, binary profile of patterns and motifs, can be used to train an SVM classifier that can predict cell penetrating peptides with higher accuracy. The hybrid model described in this study achieved more accuracy than the previous methods and thus may complement the existing methods. Based on the above study, a user-friendly web server CellPPD has been developed to help the biologists, where a user can predict and design CPPs with much ease. CellPPD web server is freely accessible at http://crdd.osdd.net/raghava/cellppd/.

Project description:BackgroundThe high variability in envelope regions of some viruses such as HIV allow the virus to establish infection and to escape subsequent immune surveillance. This variability, as well as increasing incorporation of N-linked glycosylation sites, is fundamental to this evasion. It also creates difficulties for multiple sequence alignment methods (MSA) that provide the first step in their analysis. Existing MSA tools often fail to properly align highly variable HIV envelope sequences requiring extensive manual editing that is impractical with even a moderate number of these variable sequences.ResultsWe developed an automated library building tool NGlyAlign, that organizes similar N-linked glycosylation sites as block constraints and statistically conserved global sites as single site constraints to automatically enforce partial columns in consistency-based MSA methods such as Dialign. This combined method accurately aligns variable HIV-1 envelope sequences. We tested the method on two datasets: a set of 156 founder and chronic gp160 HIV-1 subtype B sequences as well as a set of reference sequences of gp120 in the highly variable region 1. On measures such as entropy scores, sum of pair scores, column score, and similarity heat maps, NGlyAlign+Dialign proved superior against methods such as T-Coffee, ClustalOmega, ClustalW, Praline, HIValign and Muscle. The method is scalable to large sequence sets producing accurate alignments without requiring manual editing. As well as this application to HIV, our method can be used for other highly variable glycoproteins such as hepatitis C virus envelope.ConclusionsNGlyAlign is an automated tool for mapping and building glycosylation motif libraries to accurately align highly variable regions in HIV sequences. It can provide the basis for many studies reliant on single robust alignments. NGlyAlign has been developed as an open-source tool and is freely available at https://github.com/UNSW-Mathematical-Biology/NGlyAlign_v1.0 .

Project description:BackgroundThe sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome.MethodsAll reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon's entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database.ResultsA total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity.ConclusionThis work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens.

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Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library.

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Designing antibody against highly conserved region of dengue envelope protein by in silico screening of scFv mutant library.

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