Literature DB >> 18609674

Estimation of melting curves from enzymatic activity-temperature profiles.

D J Hei1, D S Clark.   

Abstract

Measuring the reversible thermal unfolding of enzymes is valuable for quantifying the effects of environmental factors on the thermodynamic stability of proteins. The thermal unfolding behavior of enzymes is typically studied using calorimetry or optical techniques such as circular dichroism, fluorescence, or light scattering. These techniques often have practical limitations and usually require the protein to be electrophoretically pure. An alternative technique for analyzing the thermodynamic stability of enzymes is to estimate the melting curve from temperature-activity data. This technique does not require electrophoretically pure enzyme, provided the sample does not have competing enzymatic activities or proteins which can affect enzyme stability (e.g., proteases). Moreover, small amounts of contaminant proteins should not affect the results as long as enzymatic assays are performed at low protein concentrations where nonspecific protein-protein interactions are negligible. To illustrate this technique, the melting curve for beta-galactosidase from Escherichia coli in the presence of 1 mM EDTA, and the shift caused by adding 1 mM Mg(+2), were calculated from activity-temperature data. Melting temperatures predicted from activity-temperature data compared closely with those obtained using other techniques. Application of this analysis to multisubstrate enzymes is illustrated by estimating the melting profiles for partially purified hydrogenases from several thermophilic Methanococcii. Limitations and important considerations for estimating melting profiles from activity-temperature data are discussed. (c) 1993 John Wiley & Sons, Inc.

Entities:  

Year:  1993        PMID: 18609674     DOI: 10.1002/bit.260421015

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  4 in total

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3.  Evaluating Protein Engineering Thermostability Prediction Tools Using an Independently Generated Dataset.

Authors:  Peishan Huang; Simon K S Chu; Henrique N Frizzo; Morgan P Connolly; Ryan W Caster; Justin B Siegel
Journal:  ACS Omega       Date:  2020-03-20

4.  Reaction and diffusion thermodynamics explain optimal temperatures of biochemical reactions.

Authors:  Mark E Ritchie
Journal:  Sci Rep       Date:  2018-07-23       Impact factor: 4.379

  4 in total

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