The folding and stability of proteins is a fundamental problem in several research fields. In the present paper, we have used different computational approaches to study the effects caused by changes in pH and for charged mutations in cold shock proteins from (Bs-CspB). First, we have investigated the contribution of each ionizable residue for these proteins to their thermal stability using the TKSA-MC, a Web server for rational mutation via optimizing the protein charge interactions. Based on these results, we have proposed a new mutation in an already optimized Bs-CspB variant. We have evaluated the effects of this new mutation in the folding energy landscape using structure-based models in Monte Carlo simulation at constant pH, SBM-CpHMC. Our results using this approach have indicated that the charge rearrangements already in the unfolded state are critical to the thermal stability of Bs-CspB. Furthermore, the conjunction of these simplified methods was able not only to predict stabilizing mutations in different pHs but also to provide essential information about their effects in each stage of protein folding.
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http://dx.doi.org/10.1021/acs.jctc.9b00894 | DOI Listing |
Biomed Pharmacother
January 2025
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Medicina y Biología Experimental de Cuyo (IMBECU), Argentina; Universidad Nacional de Cuyo, Facultad de Ciencias Médicas, Instituto de Bioquímica y Biotecnología, Avda. Libertador 80, Mendoza CP5500, Argentina. Electronic address:
A hypertonic solution of Ibuprofen (Ibu) was designed to nebulize, associating a low concentration of Ibu with L-Arginine (AR), to increase solubility and serve as a nitric oxide donor. To provide preclinical research human bronchial epithelial cells derived from a cystic fibrosis patient homozygous for the ΔF508 CFTR mutation (CFBE41o-) and mouse RAW 264.7 macrophages were pre-treated with Ibu (10-100 μM), AR (20 and 200 μM), or the combination Ibu-AR (10-100 μM).
View Article and Find Full Text PDFRev Physiol Biochem Pharmacol
January 2025
Institute of Medical Sciences, University of Aberdeen, Aberdeen, Scotland, UK.
Ribosomes use multiple electrical forces to regulate new protein construction, to ensure efficient protein cotranslation, chaperoning, and folding. When these electrical regulatory forces are disrupted as in point charge mutations, specific disease occurs from aberrantly folded proteins. α1 antitrypsin deficiency is perhaps the best-known misfolded protein disease and is covered in some detail.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Program of Cell and Gene Therapy, Division of Experimental and Translational Research, Brazilian National Cancer Institute (INCA), Rio de Janeiro, Brazil.
Antigen recognition by CD8+ T-cell receptors (TCR) is crucial for immune responses to pathogens and tumors. TCRs are cross-reactive, a single TCR can recognize multiple peptide-Human Leukocyte Antigen (HLA) complexes. The study of cross-reactivity can support the development of therapies focusing on immune modulation, such as the expansion of pre-existing T-cell clones to fight pathogens and tumors.
View Article and Find Full Text PDFTransl Lung Cancer Res
December 2024
Department of Pathology, Aberdeen Royal Infirmary, Aberdeen, UK.
Background: Anti-angiogenic agents, such as nintedanib and ramucirumab, when combined with docetaxel, are subsequent treatment options in patients with non-small cell lung cancer (NSCLC) who have failed on first-line chemotherapy or immunochemotherapy. However, to date, there are no validated predictive biomarkers for efficacy of anti-angiogenic therapies in this setting. The aim of this study was to explore whether genetic or genomic markers, alone or combined with clinical covariates, could be used to predict overall survival (OS) in patients with NSCLC who are eligible for treatment with nintedanib plus docetaxel.
View Article and Find Full Text PDFAlchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data.
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