What are the limitations of variable epitope libraries?
Best insight from top research papers
Variable epitope libraries (VELs) have limitations in the context of vaccine development. The antigenic variability of tumor cells and pathogens, along with immune tolerance, pose challenges for the design of effective vaccines . The success rate of vaccine development against major diseases has not been satisfactory, highlighting the need for extraordinary measures and alternative approaches . The burden of infectious diseases is mainly caused by antigenically variable pathogens (AVPs) that escape immune responses . The dynamic nature of the tumor epitope landscape and extensive genetic and antigenic variability are major obstacles for the development of vaccines targeting difficult targets . Therefore, the limitations of VELs include the need for continuous updates due to antigenic evolution and the challenge of targeting complex and permanently changing epitope landscapes . Alternative approaches for the rational design of new types of immunogens are being explored to overcome these limitations .
Answers from top 4 papers
More filters
| Papers (4) | Insight |
|---|---|
| The limitations of variable epitope libraries are not mentioned in the provided paper. | |
Patent 14 Jul 2016 1 Citations | The limitations of variable epitope libraries are not mentioned in the provided paper. The paper discusses the construction and use of variable epitope libraries for targeting antigenically variable pathogens and diseases. |
07 Apr 2020 | The limitations of variable epitope libraries are not mentioned in the provided paper. The paper discusses the need for a COVID-19 vaccine and the importance of generating prophylactic/therapeutic vaccines. |
20 Citations | The limitations of variable epitope libraries are not mentioned in the provided paper. |
Related Questions
How to predict epitopes-b using rnn abcpred?3 answersEpitope prediction using RNN ABCpred involves the following steps. First, the physicochemical properties of epitopes are converted into digital vectors, resulting in high-dimensional features. Then, Principal Component Analysis (PCA) is applied to reduce the dimensionality of these features, while retaining the main components. The dimensionality reduction features are then used as input for Recurrent Neural Network (RNN) to predict epitopes, resulting in good prediction results. Another method, GraphBepi, combines sequence representations generated by a pretrained language model with protein structures predicted by AlphaFold2. The spatial information from protein structures is captured using an edge-enhanced deep graph neural network (EGNN), while long-range dependencies from sequences are captured using bidirectional long short-term memory neural networks (BiLSTM). The low-dimensional representation learned by EGNN and BiLSTM is combined to predict B-cell epitopes with improved performance compared to existing methods. Additionally, BCEPS is a web server that predicts linear B cell epitopes using machine learning models trained on a dataset of linearized conformational B cell epitopes. The predicted epitopes can be ranked based on various properties and their immunogenicity, and the server also detects if predicted epitopes are located in ectodomains of membrane proteins or possess N-glycosylation sites. Finally, epitope1D is an explainable machine learning method that accurately identifies linear B-cell epitopes by leveraging a graph-based signature representation of protein sequences and Organism Ontology information. It outperforms state-of-the-art tools and has been made available as a user-friendly web server interface and API.
How to predict epitopes-b using rnn?5 answersEpitope prediction using Recurrent Neural Networks (RNN) can be achieved by following a few steps. First, select the relevant properties of the epitope and convert them into digital vectors. Next, apply Principal Component Analysis (PCA) to reduce the dimensionality of the features, while retaining the main components. Finally, use the reduced dimensionality features as input for the RNN model to predict the B-cell epitopes. Another approach for predicting B-cell epitopes using RNN involves using a python-based web-tool called LBCEPred. This tool utilizes a random forest classifier and statistical moment-based descriptors to predict B-cell epitopes from protein sequences. LBCEPred outperforms other sequence-based models with high accuracy and area under the curve values.
What are the three important limitations of an ELISA? Explain each one.?5 answersELISA has three important limitations. Firstly, the assay is subject to error caused by interfering substances found in the sample matrix, which can affect the accuracy and precision of the test. Secondly, analytical errors are commonly seen in ELISA papers, highlighting the need for strict caution in the pre-analytical, analytical, and post-analytical phases to ensure immunoassay quality. Lastly, the performance of ELISAs often varies due to nonstandard experimental procedures and inadequate data analysis, emphasizing the importance of standardized procedures and statistical analysis to increase sensitivity, reproducibility, and precision.
Why Stability can be the general criteria for epitope selection?3 answersStability can be a general criteria for epitope selection because it quantifies the reproducibility of the feature selection method, which is crucial for identifying relevant and reproducible features. Most feature selection methods evaluate performance based on model predictions, but the ability to identify meaningful subsets of features cannot be solely evaluated based on prediction accuracy. If tiny changes to the data lead to large changes in the chosen feature subset, then many selected features are likely to be data artifacts rather than real biological signals. Stability addresses this need by quantifying how robust a method is to perturbations in the data, providing a measure of the method's ability to identify relevant and reproducible features. Therefore, stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.
Why Efficacy can be the general criteria for epitope selection?2 answersEfficacy can be the general criteria for epitope selection because it is important to select epitopes that elicit a strong immune response. By selecting epitopes with great immunogenic potential, the resulting vaccines can have a higher chance of recovery and effective immunogenicity. This is crucial for vaccine design as it ensures that the immune system is directed towards generating antibodies against specific regions of a protein that are known to be productive or neutralizing in the case of an infection. Additionally, selecting epitopes that can skew the balance towards inflammatory Type I T cells can invigorate cancer immunotherapeutic approaches. Therefore, considering efficacy as a criteria for epitope selection allows for the development of vaccines that can effectively stimulate the immune system and provide meaningful information about treatment efficacy and safety.
Why Conservativity can be the general criteria for epitope selection?5 answersConservativity can be a general criteria for epitope selection because highly conserved epitopes have the potential to induce a broader immune response and provide protection against a wide range of pathogen variants. Several studies have utilized computational approaches to identify and analyze highly conserved amino acid sequences in viral proteins, such as the envelope (E) protein of Dengue virus (DENV). These conserved regions have been found to possess special attributes that make them suitable candidates for vaccine development. Additionally, the selection of highly conserved epitopes has been shown to achieve a high population coverage, making them effective in targeting diverse populations. Furthermore, the use of conservativity as a criterion for epitope selection allows for the identification of promiscuous, strong binding T-cell epitopes that can elicit an immune response across different human leukocyte antigen (HLA) types. Overall, conservativity is a valuable criterion for epitope selection as it enhances the potential efficacy and coverage of epitope-based vaccines.