Cold-adapted enzymes from psychrophilic species show the general characteristics of being more heat labile, and having a different balance between enthalpic and entropic contributions to free energy barrier of the catalyzed reaction compared to mesophilic orthologs. Among cold-adapted enzymes, there are also examples that show an enigmatic inactivation at higher temperatures before unfolding of the protein occurs. Here, we analyze these phenomena by extensive computer simulations of the catalytic reactions of psychrophilic and mesophilic α-amylases. The calculations yield temperature dependent reaction rates in good agreement with experiment, and also elicit the anomalous rate optimum for the cold-adapted enzyme, which occurs about 15 °C below the melting point. This result allows us to examine the structural basis of thermal inactivation, which turns out to be caused by breaking of a specific enzyme-substrate interaction. This type of behaviour is also likely to be relevant for other enzymes displaying such anomalous temperature optima.
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http://dx.doi.org/10.1038/s41467-020-16341-2 | DOI Listing |
Radiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
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January 2025
Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, Padova 35131, Italy.
During the last 20 years, the fragment-based drug discovery approach gained popularity in both industrial and academic settings due to its efficient exploration of the chemical space. This bottom-up approach relies on identifying high-efficiency small ligands (fragments) that bind to a target binding site and then rationally evolve them into mature druglike compounds. To achieve such a task, researchers rely on accurate information about the ligand binding mode, usually obtained through experimental techniques, such as X-ray crystallography or computer simulations.
View Article and Find Full Text PDFBiometrics
January 2025
Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, United States.
In the era of big data, increasing availability of data makes combining different data sources to obtain more accurate estimations a popular topic. However, the development of data integration is often hindered by the heterogeneity in data forms across studies. In this paper, we focus on a case in survival analysis where we have primary study data with a continuous time-to-event outcome and complete covariate measurements, while the data from an external study contain an outcome observed at regular intervals, and only a subset of covariates is measured.
View Article and Find Full Text PDFFront Immunol
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January 2025
Exercise Physiology and Rehabilitation Laboratory, University of Picardy Jules Verne, Amiens, Hauts-de-France, France.
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