Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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http://dx.doi.org/10.1007/978-1-0716-3890-3_17 | DOI Listing |
PLoS Comput Biol
December 2024
Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Canada.
Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations.
View Article and Find Full Text PDFPlant Cell Rep
December 2024
Department of Biotechnology, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603 203, India.
CesA proteins response to arsenic stress in rice involves structural and regulatory mechanisms, highlighting the role of BES1/BZR1 transcript levels under arsenate exposure and significant downregulation of BZR1 protein expression. Plants interact with several hazardous metalloids during their life cycle through root and soil connection. One such metalloid, is arsenic and its perilous impact on rice cultivation is a well-known threat.
View Article and Find Full Text PDFJ Xenobiot
December 2024
Pharmacy Department, CEU Cardenal Herrera University, CEU Universities C/Ramón y Cajal s/n, Alfara del Patriarca, 46115 Valencia, Spain.
In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology.
View Article and Find Full Text PDFBioTech (Basel)
December 2024
Departamento de Farmacobiología, Universidad de Guadalajara, CUCEI, Blvd. Marcelino García Barragán 1421, Olímpica, Guadalajara 44430, Jalisco, Mexico.
plants have been widely investigated for their specific compounds with medicinal properties. These bioactive compounds exert preventive and curative effects on non-communicable and infectious diseases. However, extracts have barely been investigated, although they constitute an affordable option to treat human diseases.
View Article and Find Full Text PDFAntibodies (Basel)
December 2024
OncoOne Research & Development GmbH, Karl-Farkas-Gasse 22, A-1030 Vienna, Austria.
Background: Rigorous assessment of antibody developability is crucial for optimizing lead candidates before progressing to clinical studies. Recent advances in predictive tools for protein structures, surface properties, stability, and immunogenicity have streamlined the development of new biologics. However, accurate prediction of the impact of single amino acid substitutions on antibody structures remains challenging, due to the diversity of complementarity-determining regions (CDRs), particularly CDR3s.
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