scholarly journals The Ion-Translocating NrfD-Like Subunit of Energy-Transducing Membrane Complexes

2021 ◽  
Vol 9 ◽  
Author(s):  
Filipa Calisto ◽  
Manuela M. Pereira

Several energy-transducing microbial enzymes have their peripheral subunits connected to the membrane through an integral membrane protein, that interacts with quinones but does not have redox cofactors, the so-called NrfD-like subunit. The periplasmic nitrite reductase (NrfABCD) was the first complex recognized to have a membrane subunit with these characteristics and consequently provided the family's name: NrfD. Sequence analyses indicate that NrfD homologs are present in many diverse enzymes, such as polysulfide reductase (PsrABC), respiratory alternative complex III (ACIII), dimethyl sulfoxide (DMSO) reductase (DmsABC), tetrathionate reductase (TtrABC), sulfur reductase complex (SreABC), sulfite dehydrogenase (SoeABC), quinone reductase complex (QrcABCD), nine-heme cytochrome complex (NhcABCD), group-2 [NiFe] hydrogenase (Hyd-2), dissimilatory sulfite-reductase complex (DsrMKJOP), arsenate reductase (ArrC) and multiheme cytochrome c sulfite reductase (MccACD). The molecular structure of ACIII subunit C (ActC) and Psr subunit C (PsrC), NrfD-like subunits, revealed the existence of ion-conducting pathways. We performed thorough primary structural analyses and built structural models of the NrfD-like subunits. We observed that all these subunits are constituted by two structural repeats composed of four-helix bundles, possibly harboring ion-conducting pathways and containing a quinone/quinol binding site. NrfD-like subunits may be the ion-pumping module of several enzymes. Our data impact on the discussion of functional implications of the NrfD-like subunit-containing complexes, namely in their ability to transduce energy.

2016 ◽  
Vol 1857 ◽  
pp. e36
Author(s):  
Filipa Calisto ◽  
Patrícia N. Refojo ◽  
Cláudia Bispo ◽  
Cláudia Andrade ◽  
Rui Gardner ◽  
...  

2018 ◽  
Vol 1859 ◽  
pp. e69
Author(s):  
Janet Vonck ◽  
Joana S. Sousa ◽  
Filipa Calisto ◽  
Deryck J. Mills ◽  
Julian D. Langer ◽  
...  

2004 ◽  
Vol 70 (11) ◽  
pp. 6525-6534 ◽  
Author(s):  
A. V. Palumbo ◽  
J. C. Schryver ◽  
M. W. Fields ◽  
C. E. Bagwell ◽  
J.-Z. Zhou ◽  
...  

ABSTRACT Genomic techniques commonly used for assessing distributions of microorganisms in the environment often produce small sample sizes. We investigated artificial neural networks for analyzing the distributions of nitrite reductase genes (nirS and nirK) and two sets of dissimilatory sulfite reductase genes (dsrAB 1 and dsrAB 2) in small sample sets. Data reduction (to reduce the number of input parameters), cross-validation (to measure the generalization error), weight decay (to adjust model parameters to reduce generalization error), and importance analysis (to determine which variables had the most influence) were useful in developing and interpreting neural network models that could be used to infer relationships between geochemistry and gene distributions. A robust relationship was observed between geochemistry and the frequencies of genes that were not closely related to known dissimilatory sulfite reductase genes (dsrAB 2). Uranium and sulfate appeared to be the most related to distribution of two groups of these unusual dsrAB-related genes. For the other three groups, the distributions appeared to be related to pH, nickel, nonpurgeable organic carbon, and total organic carbon. The models relating the geochemical parameters to the distributions of the nirS, nirK, and dsrAB 1 genes did not generalize as well as the models for dsrAB 2. The data also illustrate the danger (generating a model that has a high generalization error) of not using a validation approach in evaluating the meaningfulness of the fit of linear or nonlinear models to such small sample sizes.


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