An important, and rapidly growing class of drugs are antibodies which can be discovered through phage display technology. In this technique, antibodies are typically first enriched through consecutive rounds of selection on a target antigen with amplification in bacteria between each selection round. Thereafter, a subset of random individual clones is analyzed for binding in a screening procedure. This results in discovery of the most abundant antibodies in the pool. However, there are multiple factors affecting the enrichment of antibodies during the selection resulting in a very complex output pool of antibodies. A few antibodies are present in many copies and others only in a few copies, where the most abundant antibodies are not necessarily the functionally best ones. In order to utilize the full potential of the output from a phage display selection, and enable discovery of low abundant, potentially functionally important clones, deep mining technologies are needed. In this chapter, two methods for deep mining of an antibody pool are described, protein depletion and antibody blocking. The methods can be applied both when the target is a single antigen and on complex antigen mixtures such as whole cells and tissues.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/978-1-0716-3381-6_22 | DOI Listing |
Sci Data
January 2025
Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK.
Automatic Compliance Checking (ACC) within the Architecture, Engineering, and Construction (AEC) sector necessitates automating the interpretation of building regulations to achieve its full potential. Converting textual rules into machine-readable formats is challenging due to the complexities of natural language and the scarcity of resources for advanced Machine Learning (ML). Addressing these challenges, we introduce CODE-ACCORD, a dataset of 862 sentences from the building regulations of England and Finland.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Computer Science and engineering, Central South University, Changsha, 410083, China.
Motivation: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding patterns between TCRs and peptides is critical for advancing these clinical applications. While several computational tools have been developed, they neglect the directional semantics inherent in sequence data, which are essential for accurately characterizing TCR-peptide interactions.
View Article and Find Full Text PDFMed Image Anal
January 2025
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Recently, a multi-scale representation attention based deep multiple instance learning method has proposed to directly extract patch-level image features from gigapixel whole slide images (WSIs), and achieved promising performance on multiple popular WSI datasets. However, it still has two major limitations: (i) without considering the relations among patches, thereby possibly restricting the model performance; (ii) unable to handle retrieval tasks, which is very important in clinic diagnosis. To overcome these limitations, in this paper, we propose a novel end-to-end MIL-based deep hashing framework, which is composed of a multi-scale representation attention based deep network as the backbone, patch-based dynamic graphs and hashing encoding layers, to simultaneously handle classification and retrieval tasks.
View Article and Find Full Text PDFBioresour Bioprocess
January 2025
Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
Feruloyl esterases (FEs, EC 3.1.1.
View Article and Find Full Text PDFMicrolife
January 2025
Environmental Metagenomics, Research Center One Health Ruhr of the University Alliance Ruhr, Faculty of Chemistry, University of Duisburg-Essen, 45141 Essen, Germany.
Oil reservoirs are society's primary source of hydrocarbons. While microbial communities in industrially exploited oil reservoirs have been investigated in the past, pristine microbial communities in untapped oil reservoirs are little explored, as are distribution patterns of respective genetic signatures. Here, we show that a pristine oil sample contains a complex community consisting of bacteria and fungi for the degradation of hydrocarbons.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!