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An Analysis of Protein Interaction and Its Methods, Metabolite Pathway and Drug Discovery
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
According to the environment of a broad area of protein, various efficient methods are to be improved to predict protein interactions. Some of the new ethics belong to the existing methods with developed techniques. It helps to know how to use the interpreted methods in experimental processes for the protein interaction prediction [85]. Figure 13.9 shows the encoded genetic method for cross-linking studies of ncAAs for protein interaction with ribosomal interpretation of live cells. It contains the suppressor tRNA with orthogonal AARS charges of ncAAs, that delivers to the ribosome, which incorporates to a responsible in-frame amber codon with nascent protein. In this way cross-linking ncAA characterize the protein features. Protein interaction helps to identify the mechanism of infection, drug development and the solution to the infection with treatment [86]. In protein–protein interaction relates to target regions and it helps to identify the functions and drug design of the proteins [87]. The SIM tool is used to find the site of the protein interaction with the unbounded protein structures [88]. By using interacting and non-interacting pairs of proteins, structural features are classified into binding or unbinding behaviour of proteins [89].
Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
For the drug development, understanding protein–protein interaction is important. Protein–protein interaction is involved in host–pathogen interaction such as human-cell and bacterial–cell interaction, antigen–antibody interaction, T-cell interaction with dead cells and foreign-bodies, signaling pathway control, transport of nutrients across a cell-membrane, metabolic processes involved in the degradation and synthesis of biomolecules, muscle contraction and cellular process regulation. There are a huge number of protein–protein interactions involved among the proteins in a human-body.
Imaging Cellular Networks and Protein-Protein Interactions In Vivo
Published in Martin G. Pomper, Juri G. Gelovani, Benjamin Tsui, Kathleen Gabrielson, Richard Wahl, S. Sam Gambhir, Jeff Bulte, Raymond Gibson, William C. Eckelman, Molecular Imaging in Oncology, 2008
Snehal Naik, Britney L. Moss, David Piwnica-Worms, Andrea Pichler-Wallace
Compared with studies of protein interactions in cultured cells, strategies to interrogate protein-protein interactions in living organisms impose even further constraints on reporter systems and mechanisms of detection. Most strategies for detecting protein-protein interactions in intact cells are based on fusion of the pair of interacting molecules to defined protein elements to reconstitute a biological or biochemical function. Examples of reconstituted activities include activation of transcription, repression of transcription, activation of signal transduction pathways, or reconstitution of a disrupted enzymatic activity (4). A variety of these techniques have been developed to investigate protein-protein interactions in cultured cells. The two-hybrid system is the most widely applied method to identify and characterize protein interactions.
Approaches to expand the conventional toolbox for discovery and selection of antibodies with drug-like physicochemical properties
Published in mAbs, 2023
Hristo L. Svilenov, Paolo Arosio, Tim Menzen, Peter Tessier, Pietro Sormanni
It is desirable to prepare antibody therapeutics as concentrated liquid formulations for subcutaneous delivery, but different antibody variants can display highly variable and difficult-to-predict solution properties, including large differences in their viscosity, opalescence, and/or aggregation properties. Therefore, there is broad interest in early-stage assays that can be used for selecting antibody candidates with low risk for possessing undesirable solution properties. Attractive intermolecular interactions, characterized for instance by the self-association parameter (kD) and second virial coefficient (B22), have been shown to correlate to a good extent with viscosity and opalescent behavior at high antibody concentrations.27 There is therefore interest in developing assays capable to measure intermolecular protein–protein interactions with high throughput and consuming a limited amount of sample.
Epitope mapping of anti-drug antibodies to a clinical candidate bispecific antibody
Published in mAbs, 2022
Arthur J. Schick, Victor Lundin, Justin Low, Kun Peng, Richard Vandlen, Aaron T. Wecksler
The epitope mapping workflow was as follows: Purified ADAs from goat and cyno were mixed with BsAb1 to facilitate ADA-BsAb1 complex formation, separated using size-exclusion chromatography (SE-HPLC), and subjected to HRF analysis. SE-HPLC not only enables enrichment of the complexes but also provides buffer exchange into a suitable buffer (phosphate) for HRF analysis. The HRF technology used in this study was Fast Photochemical Oxidation of Proteins (FPOP), a bench-top technology ideally suited for epitope mapping and characterizing protein–protein interactions.18,19 HDX-MS is a well-established technique used in industry for epitope mapping, but as with all bottom-up MS technology, there are certain limitations, including the time-dependent labeling requirements, pH-dependent labeling effects, and deglycosylation challenges. FPOP can circumvent some of these challenges due to the stable addition of the hydroxyl radical.20 However, the combination of both of these technologies may provide the most comprehensive understanding of protein–protein interactions.21–23
Proteomic characterization of the human lens and Cataractogenesis
Published in Expert Review of Proteomics, 2021
Bioinformatic analysis of whole-proteome MS datasets has not been performed in part due to limitations with cohort size and measurement approach used. To date, no whole-proteome datasets of human lenses have been analyzed in sufficient quantity or quantitative quality for network-based analysis of ARNC formation. Several studies in other tissues have highlighted effective methodology for detection of age-related or pathology-related changes [152–156]. Significant trends between these and similar reports include 1) Differential expression analysis, 2) Dimensional reduction, 3) Ontological overexpression, 4) Ontological enrichment, and 5) Protein–protein interaction network analysis. There is a significant positive impact for each of these analyses when a greater quantity of biological replicates is used. Successful application of each of these adjustments to previous studies of ARNC proteomic approaches will then enhance the quality of protein–protein interaction network analysis. It is possible that a successful study would provide a network-level interpretation of age-related changes and classification of those changes as normal processes in aging or pathological ARNC formative networks.