A proof of concept for new methodology to detect and potentially quantify mAb aggregation is presented. Assay development included using an aggregated mAb as bait for screening of a phage display peptide library and identifying those peptides with random sequence which can recognize mAb aggregates. The selected peptides can be used for developing homogeneous quantitative methods to assess mAb aggregation. Results indicate that a peptide-binding method coupled with fluorescence polarization detection can detect mAb aggregation and potentially monitor the propensity of therapeutic protein candidates to aggregate.Peptide-based vaccines are an appealing strategy which involves usage of short synthetic peptides to engineer a highly targeted immune response. These short synthetic peptides contain potential T- and B-cell epitopes. Experimental approaches in identifying these epitopes are time-consuming and expensive; hence immunoinformatics approach came into picture. Immuninformatics approach involves epitope prediction tools, molecular docking, and population coverage analysis in design of desired immunogenic peptides. In order to overcome the antigenic variation of viruses, conserved regions are targeted to find the potential epitopes. The present chapter demonstrates the use of immunoinformatics approach to select potential peptide containing multiple T- (CD8+ and CD4+) and B-cell epitopes from Avian H3N2 M1 Protein. Further, molecular docking (to analyse HLA-peptide interaction) and population coverage analysis have been used to verify the potential of peptide to be presented by polymorphic HLA molecules. In silico approach of epitope prediction has proven to be successful methodology in screening the putative epitopes among numerous possible vaccine targets in a given protein.Discovery of tumor antigenic epitopes is important for cancer vaccine development. Such epitopes can be designed and modified to become more antigenic and immunogenic in order to overcome immunosuppression towards the native tumor antigen. In silico-guided modification of epitope sequences allows predictive discrimination of those that may be potentially immunogenic. Therefore, only candidates predicted with high antigenicity will be selected, constructed, and tested in the lab. Here, we described the employment of in silico tools using a multiparametric approach to assess both potential T-cell epitopes (MHC class I/II binding) and B-cell epitopes (hydrophilicity, surface accessibility, antigenicity, and linear epitope). A scoring and ranking system based on these parameters was developed to shortlist potential mimotope candidates for further development as peptide cancer vaccines.Diseases and infections elicit a multilayered immune response which consists of molecular and cellular interaction cascades. Recent advances in high-throughput technologies have facilitated multiparameter investigation of immune cells involved in human immune responses.  https://www.selleckchem.com/products/dx3-213b.html  These multiparameter investigations generate large-scale datasets and advanced computational techniques are required to gain useful information from them. Networks or graphs offer a practical way to represent complex information and develop advanced algorithms to unveil the underlying mechanisms. Here we discuss ways to assemble and analyze networks using genome-wide transcriptional profiles. Additionally, we discuss ways to integrate information available in primary literature and databases with the networks assembled using large-scale datasets. Finally, we describe ways in which network analysis offers insights into human immune responses.MHC class I proteins present intracellular peptides on the cell's surface, enabling the immune system to recognize tumor-specific neoantigens of early neoplastic cells and eliminate them before the tumor develops further. However, variability in peptide-MHC-I affinity results in variable presentation of oncogenic peptides, leading to variable likelihood of immune evasion across both individuals and mutations. Since the major determinant of peptide-MHC-I affinity in patients is individual MHC-I genotype, we developed a residue-centric presentation score taking both mutated residues and MHC-I genotype into account and hypothesized that high scores (which correspond to poor presentation) would correlate to high mutation frequencies within tumors. We applied our scoring system to 9176 tumor samples from TCGA across 1018 recurrent mutations and found that, indeed, presentation scores predicted mutation probability. These findings open the door to more personalized treatment plans based on simple genotyping. Here, we outline the computational tools and statistical methods used to arrive at this conclusion.Vaccination is the best way to prevent the spread of emerging or reemerging infectious disease. Current research for vaccine development is mainly focused on recombinant-, subunit-, and peptide-based vaccine. At this point, immunoinformatics has been proven as a powerful method for identification of potential vaccine candidates, by analyzing immunodominat B- and T-cell epitopes. This method can reduce the time and cost of experiment to a great extent, by reducing the number of vaccine candidates for experimental testing for their efficacy. This chapter describes the use of immunoinformatics and molecular docking methods to screen potential vaccine candidates by taking Leptospira as a model.With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity.This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli (ETEC). Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design. The predicted peptides were analyzed for conservation and population coverage. HLA distribution analysis for predicted epitopes identified efficient MHC binders. Epitopes were further tested using computational docking studies to bind in MHC-I molecule cleft.