Protein-protein interactions were investigated for α-chymotrypsinogen by static and dynamic light scattering (SLS and DLS, respectively), as well as small-angle neutron scattering (SANS), as a function of protein and salt concentration at acidic conditions. Net protein-protein interactions were probed via the Kirkwood-Buff integral G22 and the static structure factor S(q) from SLS and SANS data. G22 was obtained by regressing the Rayleigh ratio versus protein concentration with a local Taylor series approach, which does not require one to assume the underlying form or nature of intermolecular interactions. In addition, G22 and S(q) were further analyzed by traditional methods involving fits to effective interaction potentials. Although the fitted model parameters were not always physically realistic, the numerical values for G22 and S(q → 0) were in good agreement from SLS and SANS as a function of protein concentration. In the dilute regime, fitted G22 values agreed with those obtained via the osmotic second virial coefficient B22 and showed that electrostatic interactions are the dominant contribution for colloidal interactions in α-chymotrypsinogen solutions. However, as protein concentration increases, the strength of protein-protein interactions decreases, with a more pronounced decrease at low salt concentrations. The results are consistent with an effective "crowding" or excluded volume contribution to G22 due to the long-ranged electrostatic repulsions that are prominent even at the moderate range of protein concentrations used here (<40 g/L). These apparent crowding effects were confirmed and quantified by assessing the hydrodynamic factor H(q → 0), which is obtained by combining measurements of the collective diffusion coefficient from DLS data with measurements of S(q → 0). H(q → 0) was significantly less than that for a corresponding hard-sphere system and showed that hydrodynamic nonidealities can lead to qualitatively incorrect conclusions regarding B22, G22, and static protein-protein interactions if one uses only DLS to assess protein interactions.
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http://dx.doi.org/10.1021/jp412301h | DOI Listing |
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School of Clinical Medicine, Guizhou Medical University, Guiyang City, Guizhou, China.
Eugenol, a phenolic natural product with diverse pharmacological activities, remains unexplored in liver cancer. Using network pharmacology, we investigated eugenol's therapeutic mechanisms in liver cancer. We obtained eugenol's molecular structure from PubChem and screened its targets using similarity ensemble approach in Swiss Target Predictiondatabases.
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Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran;
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January 2025
Department of Molecular Genetics, University of Toronto, Ontario, M5S 3K3, Canada.
Motivation: Accurate prediction of protein side-chain conformations is necessary to understand protein folding, protein-protein interactions and facilitate de novo protein design.
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From the Department of Orthopedics, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
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Neurophysiology & Behaviour Lab, University of Castilla-La Mancha, Ciudad Real, Spain.
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