Literature DB >> 27497171

Conformational Sub-states and Populations in Enzyme Catalysis.

P K Agarwal1, N Doucet2, C Chennubhotla3, A Ramanathan4, C Narayanan2.   

Abstract

Enzyme function involves substrate and cofactor binding, precise positioning of reactants in the active site, chemical turnover, and release of products. In addition to formation of crucial structural interactions between enzyme and substrate(s), coordinated motions within the enzyme-substrate complex allow reaction to proceed at a much faster rate, compared to the reaction in solution and in the absence of enzyme. An increasing number of enzyme systems show the presence of conserved protein motions that are important for function. A wide variety of motions are naturally sampled (over femtosecond to millisecond time-scales) as the enzyme complex moves along the energetic landscape, driven by temperature and dynamical events from the surrounding environment. Areas of low energy along the landscape form conformational sub-states, which show higher conformational populations than surrounding areas. A small number of these protein conformational sub-states contain functionally important structural and dynamical features, which assist the enzyme mechanism along the catalytic cycle. Identification and characterization of these higher-energy (also called excited) sub-states and the associated populations are challenging, as these sub-states are very short-lived and therefore rarely populated. Specialized techniques based on computer simulations, theoretical modeling, and nuclear magnetic resonance have been developed for quantitative characterization of these sub-states and populations. This chapter discusses these techniques and provides examples of their applications to enzyme systems.
© 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational modeling; Conformational dynamics; Conformational population; Conformational substates; Enzyme dynamics; Nuclear magnetic resonance; Protein relaxation

Mesh:

Substances:

Year:  2016        PMID: 27497171      PMCID: PMC4977995          DOI: 10.1016/bs.mie.2016.05.023

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  37 in total

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