Protein folding is a biophysical process by which a protein chain is translated to its native (folded) structure through several intermediate states such that the folded conformation becomes biologically functional. This folded protein can again exist in multiple conformations in its native state and its intrinsic conformational fluctuations are responsible for the protein-ligand recognition and binding to form a specific complex. In this study, we introduce an exactly solvable kinetic model based on a discrete stochastic approach to study the protein-ligand binding by taking into account an arbitrary number of the transient intermediates between the unfolded and the native folded state of the protein. We also examine the conformational fluctuations in the folded state explicitly. The dynamic properties of the system are explicitly evaluated to understand the role of short-lived conformations in the process of protein folding and also conformational fluctuations existing in the folded state of the protein.
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http://dx.doi.org/10.1016/j.bpc.2022.106803 | DOI Listing |
Biophys J
January 2025
Department of Physics and Astronomy, Department of Chemistry, NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, California, USA. Electronic address:
In this work we present a minimal structure-based model of protein diffusional search along local DNA amid protein binding and unbinding events on the DNA, taking into account protein-DNA electrostatic interactions and hydrogen-bonding (HB) interactions or contacts at the interface. We accordingly constructed the protein diffusion-association/dissociation free energy surface and mapped it to 1D as the protein slides along DNA, maintaining the protein-DNA interfacial HB contacts that presumably dictate the DNA sequence information detection. Upon DNA helical path correction, the protein 1D diffusion rates along local DNA can be physically derived to be consistent with experimental measurements.
View Article and Find Full Text PDFJ Mol Biol
January 2025
Department of Applied Bioscience, Kanazawa Institute of Technology. Electronic address:
A variety of potential biological roles of mechanical forces have been proposed in the field of cell biology. In particular, mechanical forces alter the mechanical conditions within cells and their environment, exerting a strong effect on the reorganization of the actin cytoskeleton. Single-molecule imaging studies have provided evidence that an actin filament may act as a mechanosensor.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Polymer Science and Engineering, University of Massachusetts, Amherst, Massachusetts 01003, United States.
Direct translocation of RNA with secondary structures using single-molecule electrophoresis through protein nanopores shows significant fluctuations in the measured ionic current, in contrast to unstructured single-stranded RNA or DNA. We developed a multiscale model combining the oxRNA model for RNA with the 3-dimensional Poisson-Nernst-Planck formalism for electric fields within protein pores, aiming to map RNA conformations to ionic currents as RNA translocates through three protein nanopores: α-hemolysin, CsgG, and MspA. Our findings reveal three distinct stages of translocation (pseudoknot, melting, and molten globule) based on contact maps and current values.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
School of Physics, Zhejiang University, Hangzhou, 310058, PR China.
The self-assembly of intrinsically disordered proteins (IDPs) into condensed phases and the formation of membrane-less organelles (MLOs) can be considered as the phenomenon of collective behavior. The conformational dynamics of IDPs are essential for their interactions and the formation of a condensed phase. From a physical perspective, collective behavior and the emergence of phase are associated with long-range correlations.
View Article and Find Full Text PDFSci Rep
January 2025
State Key Laboratory of Chemical Resources Engineering, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds.
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