Biophysical methods have become established in many areas of drug discovery. Application of these methods was once restricted to a relatively small number of scientists using specialized, low throughput technologies and methods. Now, automated high-throughput instruments are to be found in a growing number of laboratories. Many biophysical methods are capable of measuring the equilibrium binding constants between pairs of molecules crucial for molecular recognition processes, encompassing protein-protein, protein-small molecule, and protein-nucleic acid interactions, and several can be used to measure the kinetic or thermodynamic components controlling these biological processes. For a full characterization of a binding process, determinations of stoichiometry, binding mode, and any conformational changes associated with such interactions are also required. The suite of biophysical methods that are now available represents a powerful toolbox of techniques which can effectively deliver this full characterization.The aim of this chapter is to provide the reader with an overview of the drug discovery process and how biophysical methods, such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), nuclear magnetic resonance, mass spectrometry (MS), and thermal unfolding methods can answer specific questions in order to influence project progression and outcomes. The selection of these examples is based upon the experiences of the authors at AstraZeneca, and relevant approaches are highlighted where they have utility in a particular drug discovery scenario.
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Cell Commun Signal
January 2025
Centre of Postgraduate Medical Education, Centre of Translation Research, Department of Biochemistry and Molecular Biology, ul. Marymoncka 99/103, Warsaw, 01-813, Poland.
Background: Renal cell cancer (RCC) is the most common and highly malignant subtype of kidney cancer. Mesenchymal stromal cells (MSCs) are components of tumor microenvironment (TME) that influence RCC progression. The impact of RCC-secreted small non-coding RNAs (sncRNAs) on TME is largely underexplored.
View Article and Find Full Text PDFSci Rep
January 2025
Hannover Centre for Optical Technologies (HOT), Leibniz University Hannover, Hannover, Germany.
Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts.
View Article and Find Full Text PDFJ Photochem Photobiol B
December 2024
Anne Bates Leach Eye Center, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL, United States of America; Ocular Microbiology Laboratory, Bascom Palmer Eye Institute, Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, FL, United States of America.
Introduction: Fungal keratitis is a leading cause of corneal blindness, with current antifungal treatments having limited efficacy. One promising treatment modality is Rose Bengal (RB) photodynamic antimicrobial therapy (PDAT) that has shown mixed success against fungal keratitis. Therefore, there is a need to explore the antimicrobial efficacy of other green-light activated photosensitizers that have deep penetration in the cornea to combat the deep fungal infections, such as Erythrosin B (EB) and Eosin Y (EY).
View Article and Find Full Text PDFCurr Opin Struct Biol
January 2025
Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637, USA. Electronic address:
Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-grain' electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling.
View Article and Find Full Text PDFACS Nano
January 2025
School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China.
Ferroptosis is a classic type of programmed cell death characterized by iron dependence, which is closely associated with many diseases such as cancer, intestinal ischemic diseases, and nervous system diseases. Transferrin (Tf) is responsible for ferric-ion delivery owing to its natural Fe binding ability and plays a crucial role in ferroptosis. However, Tf is not considered as a classic druggable target for ferroptosis-associated diseases since systemic perturbation of Tf would dramatically disrupt blood iron homeostasis.
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