In contrast to the "lock-and-key" model underlying the long-term success of structural biology and rational drug design, intrinsically disordered proteins (IDPs) exist in an ensemble of highly heterogeneous conformations even after binding with small-molecule ligands. It remains controversial how to characterize the thermodynamics of such fuzzy interactions. Here, we derive an ensemble-based thermodynamic framework to analyze the apparent affinity between IDPs and ligands. It is shown that the apparent affinity is related to the interaction free energy between the individual conformation and ligand in a way similar to Jarzynski's equality in nonequilibrium statistics. The oncoprotein c-Myc is adopted as an example to demonstrate the related properties, for example, the distribution of conformation-ligand interaction free energy, the entropic contribution from the ensemble, the conformation shift under ligand binding, and how to control the error under a limited number of sampled conformations.
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Sci Rep
March 2025
Institute of Fluid Dynamics and Thermodynamics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technická 4, Prague, 166 07, Czech Republic.
Efficient heat dissipation is crucial for various industrial and technological applications, ensuring system reliability and performance. Advanced thermal management systems rely on materials with superior thermal conductivity and stability for effective heat transfer. This study investigates the thermal conductivity, viscosity, and stability of hybrid AlO-CuO nanoparticles dispersed in Therminol 55, a medium-temperature heat transfer fluid.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
Attention deficit hyperactivity disorder (ADHD) is the most common condition affecting the development of neurons in children. Therefore, early and accurate diagnosis of ADHD in young children is of paramount importance. In this study, the 8-channel wireless wearable EEG measurement device was employed to record EEG data from 30 children diagnosed with ADHD and 30 typical development (TD) young children aged 4-7 years.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.
Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter-network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls.
View Article and Find Full Text PDFSci Rep
January 2025
Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Budh Nagar, Noida, 201313, India.
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).
View Article and Find Full Text PDFJ Food Sci
January 2025
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, China.
To enhance the drying quality of peony flowers, this study developed an integrated intelligent control and monitoring system. The system incorporates computer vision technology to enable real-time continuous monitoring and analysis of the total color change (ΔE) and shrinkage rate (SR) of the material. Additionally, by integrating drying time and temperature data, a hybrid neural network model combining convolutional neural networks, long short-term memory, and attention mechanisms (CNN-LSTM-Attention) was employed to accurately predict the moisture ratio (MR) of peony flowers.
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