Drosophila Alcohol Dehydrogenase: Acetate–Enzyme Interactions and Novel Insights into the Effects of Electrostatics on Catalysis

Benach, J.; Winberg, Jan‐Olof; Svendsen, John S.; Atrian, Sı́lvia; Gonzàlez‐Duarte, Roser; Ladenstein, Rudolf

doi:10.1016/j.jmb.2004.10.028

Cited by 23 publications

(31 citation statements)

References 73 publications

(125 reference statements)

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“…This results in transfer of the pro-S hydrogen in ethanol to the coenzyme. However, in the further oxidation of aldehydes to acids, the alkyl chain of the aldehyde binds to the R 2 part of the active site [32], a finding that supported the suggestion [43] that the gem-diol form of aldehydes binds to the binary enzyme-NAD + complex in the oxidation of an aldehyde to the corresponding carboxylic acid. The crystal structure data also indicate that one of the hydroxyl groups in the gem-diol are at hydrogen bonding distance to the hydroxyl group in the side chain of the active site Ser138 and Tyr51, while the other hydroxyl group points into the R 1 b cavity close to the C7-amide group of the nicotinamide moiety of the coenzyme.…”

Section: Introductionmentioning

confidence: 58%

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Medium- and short-chain dehydrogenase/reductase gene and protein families

Ladenstein¹,

Winberg

Benach

2008

Cell. Mol. Life Sci.

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The structure-function relationships of alcohol dehydrogenases from the large family of short-chain dehydrogenase/reductase (SDR) enzymes are described. It seems that while mammals evolved with a medium-chain alcohol dehydrogenase family (MDR), fruit flies utilized an ancestral SDR enzyme. They have modified its function into an efficient alcohol dehydrogenase to aid them in colonizing the emerging ecological niches that appeared around 65 million years ago. To the scientific community, Drosophila has now served as a model organism for quite some time, and Drosophila alcohol dehydrogenase is one of the best-studied members of the SDR family. The availability of a number of high-resolution structures, accurate and thorough kinetic work, and careful theoretical calculations have enabled an understanding of the structure-function relationships of this metal-free alcohol dehydrogenase. In addition, these studies have given rise to various hypotheses about the mechanism of action of this enzyme and contribute to the detailed knowledge of the large superfamily of SDR enzymes.

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Section: Introductionmentioning

confidence: 58%

“…From the ternary and binary complexes, the conformation of the coenzyme bound to the enzyme can be determined. The oxidized or reduced coenzyme, NAD + [34] or NADH [32], binds to DADH in an extended conformation with the adenine ring in anti and the nicotinamide in syn conformation (Fig. 1B).…”

Section: Introductionmentioning

confidence: 99%

Medium- and short-chain dehydrogenase/reductase gene and protein families

Ladenstein¹,

Winberg

Benach

2008

Cell. Mol. Life Sci.

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“…The Drosophila Adh is the most studied enzyme that catalyzes the oxidation of alcohols to aldehydes/ketones [48]. It has been extensive reported that several functionally important residues reside in the Adh gene: tyrosine-152, lysine-156 and serine-139 are conserved in homologous dehydrogenases and have important roles in catalysis [49][50][51][52][53]; glycine-130, glycine-133 and glycine-184 contribute substantially to the structure of the active form [50]; and aspartic acid-64 lies within a coenzyme-binding domain [51].…”

Section: Significance Of Profiling Heterogeneitymentioning

confidence: 99%

Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences

Zhang¹,

Townsend²

2009

SciVee

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A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. The clustering model may be evaluated via model selection using the Akaike Information Criterion, the corrected Akaike Information Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.

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“…8). Electrostatic effects have been seen to be significant in enzymatic catalysis (9,(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). Direct molecular dynamics simulations of protein dynamics are typically restricted to tens of nanoseconds, and so a detailed dynamical analysis of the observed millisecond rate fluctuations via an integration of the equations of motion of the atoms is presently beyond the scope of such simulations.…”

Section: ؊1mentioning

confidence: 99%

“…Previous studies of a different nature have included the role of the electric field on time-dependent vibrational Stark shift at the heme site of myoglobin (29) and of the nitrile stretching mode in human aldose reductase (30), both in the picosecond regime. Considering the importance of electrostatics in enyzme catalysis (9,(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) and in spectral diffusion (␦ 0 (t)), we explore the possibility that ␦k(t) and ␦ 0 (t) may be related to each other via the time-dependent fluctuations in the local electrostatic interaction energy, ␦E(t).The present work on correlation functions of different enzyme properties was prompted by an experimental observation of the autocorrelation functions of catalysis rate of oxidation of cholesterol (C k (t)) and the spectral diffusion (C 0 (t)) for cholesterol oxidase (1). The two autocorrelation functions are compared in the present Fig.…”

mentioning

confidence: 99%

An interpretation of fluctuations in enzyme catalysis rate, spectral diffusion, and radiative component of lifetimes in terms of electric field fluctuations

Prakash

Marcus

2007

Proc. Natl. Acad. Sci. U.S.A.

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Time-dependent fluctuations in the catalysis rate (␦k(t)) observed in single-enzyme experiments were found in a particular study to have an autocorrelation function decaying on the same time scale as that of spectral diffusion ␦0(t). To interpret this similarity, the present analysis focuses on a factor in enzyme catalysis, the local electrostatic interaction energy (E) at the active site and its effect on the activation free energy barrier. We consider the slow fluctuations of the electrostatic interaction energy (␦E(t)) as a contributor to ␦k(t) and relate the latter to ␦0(t). The resulting relation between ␦k(t) and ␦0(t) is a dynamic analog of the solvatochromism used in interpreting solvent effects on organic reaction rates. The effect of the postulated ␦E(t) on fluctuations in the radiative component (␦␥ r ؊1(t)) of the fluorescence decay of chromophores in proteins also is examined, and a relation between ␦␥ r ؊1 (t) and ␦0(t) is obtained. Experimental tests will determine whether the correlation functions for ␦k(t), ␦0(t), and ␦␥ r ؊1 are indeed similar for any enzyme. Measurements of dielectric dispersion, (), for the enzyme discussed elsewhere will provide further insight into the correlation function for ␦E(t). They also will determine whether fluctuations in the nonradiative component ␥ nr ؊1 of the lifetime decay has a different origin, fluctuations in distance for example.fluorescence ͉ single enzyme S ingle-molecule experiments on proteins have revealed novel phenomena, including fluctuations in the rates of enzyme catalysis (1-3), on-off blinking behavior in proteins (4-6), and oscillations under a near-denatured condition of a green fluorescent protein (7). The on-off blinking behavior of proteins and the fluctuations in the enzyme catalysis rate in the experiments were interpreted in terms of fluctuations of the protein/enzyme between various conformational substates (2, 3). The rates of enzyme-catalyzed reactions themselves occur typically on the milliseconds-to-seconds time scale, and the millisecond dynamics of enzymes are believed to contribute to the ''functional dynamics'' of the enzyme (8). Single-molecule enzyme experiments on cholesterol oxidase revealed an autocorrelation function of the rate of catalysis that decayed on a time scale about the same as that for the decay of the autocorrelation function of the spectral diffusion of a chromophore in the same enzyme in the absence of the substrate (1), thus suggesting a common origin for the two fluctuations.Significant insight into the functioning of enzymes has been achieved by using computer simulations (9-11) and empirical valence bond (9) and hybrid quantum mechanical/molecular mechanics (10) methods, among others. In these studies the catalysis is affected by several factors: electrostatic effects of the enzyme on the substrate (9) and coupling of protein fluctuations with motions of the substrate-coenzyme pair in the transition state (e.g., ref. 8). Electrostatic effects have been seen to be significant in enzymatic catalysis (9,(1...

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Drosophila Alcohol Dehydrogenase: Acetate–Enzyme Interactions and Novel Insights into the Effects of Electrostatics on Catalysis

Cited by 23 publications

References 73 publications

Medium- and short-chain dehydrogenase/reductase gene and protein families

Medium- and short-chain dehydrogenase/reductase gene and protein families

Maximum-Likelihood Model Averaging To Profile Clustering of Site Types across Discrete Linear Sequences

An interpretation of fluctuations in enzyme catalysis rate, spectral diffusion, and radiative component of lifetimes in terms of electric field fluctuations

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