RESULTS AND DISCUSSION
Cofactor-type is the primary determinant of redox energetics .
Redox potentials included in ProtRedox span almost 2 V, ranging from the
-675 mV 2[4Fe-4S] binding bacterial ferredoxin of E. coli[55] to the +1301 mV chlorophyll A in PS II within T.
elongatus [56]. Within this broad range, the cofactor type is the
primary determinant of redox potential (Fig. 1 ). Cofactor types
are designated based primarily on the PDB-derived nomenclature.
Cofactors from most reducing to most oxidizing were 4Fe-4S (SF4), 2Fe-2S
(FES), flavins, mononuclear iron sites (Fe), iron-bound hemes (HEM) and
copper sites (Cu). These ranges are consistent with previous analyses of
protein electron carriers [8].
Molecular features that determine energetics . Protein redox
potential is a complex property that is affected by features of the
redox site first and second shell environment: solvation,
hydrogen-bonding, ligand interactions, metal coordination, electrostatic
interactions [57, 58] and corresponding enthalpic and entropic
energy terms [59]. Redox potential can be directly calculated from
first principle quantum mechanics calculations [60, 61], however
these calculations are expensive and are not practical for protein
design. To better understand the protein features that determine redox
potential, we calculated the correlation between 433 physicochemical
features (including energy and geometry features) and reduction
potentials (Fig. 2 ) for copper proteins with ReProDox.
The categories of features with best correlations tended to be those
related to electrostatics and solvation. These include solvation
features that describe Lazaridis-Karplus solvation energies both
isotropic and anisotropic contributions for various distance cutoffs
within 9 Å. The significant electrostatic features include calculations
for Coulombic electrostatic potential as well as features describing the
theoretical titration curve of surrounding residues. In contrast, other
categories of features are more statistical. For example, eight of the
nine significant “amino acid angle” features are Dunbrack rotamer
energies of residues within 5 Å, indicating the use of some more common
and some less common rotamers. In addition to further evaluating
significant features that correlate with protein redox potentials found
in ProtRedox, we expect these features can be used to train models
[37, 53] for high throughput redox active protein design.
Coupling redox energetics to pH. Comparing protein
redox potentials is challenging due to the numerous experimental
conditions under which redox potentials are measured. Experimental pH is
known to be a significant factor affecting redox processes accompanied
by protonation/deprotonation events [62], which is commonly observed
among Cu redox proteins [62-65]. To compare experimental redox
potentials values are normalized to a reference pH (7.0) using the
Nernst equation,
Eq. 1: \(E_{\text{red}}\ =\ E+\ (59.16\ mV*n*(pH\ -7))\)
where 59.16 mV is the Nernst constant relating pH to redox potential.
Ered is the normalized reduction potential of each
protein at pH 7 and E is the reduction potential measured at the
literature pH. The variable n, assumed to be one, is the electron and
proton ratio involved in the redox reaction, respectively.
For copper proteins with an azurin fold, we observed a correlation
between pH and redox potential with a slope of -51 mV/pH unit
(Fig 3A ), near what is expected if the reactions followed
Nernstian behavior (-59 mV/pH unit), assuming one electron transfer per
reaction. Normalization removes the slope of this correlation
(Fig. 3B ). Experimental pH conditions showed the strongest
positive Pearson coefficient with redox potential, above the computed
factors from structure described earlier. However, there is a very large
variance in observed potentials, clearly indicating that no one
parameter can fully explain redox energetics.
Redox gradients and oxidoreductase evolution. Many of
the ProtReDox entries are associated with an experimentally determined
three-dimensional structure deposited in the PDB. This allowed us to map
the redox energetics onto the SpAN – an existing network mapping
electron transfer pathways in oxidoreductases of known structure [16,
17]. Nodes in the SpAN correspond to redox-cofactor binding protein
microenvironments – termed modules. Edges reflect the existence spatial
proximity of cofactor atoms in a pair of modules in one or many
structures, providing a pathway for electron transfer. Cofactor
edge-to-edge distances less than 14.0 Å were considered
electron-transfer competent [7].
The full SpAN contains 133 modules [17]. We identified 18 modules
with specified redox energetics (Fig. 4, Table 1 ). These
modules formed a fully connected sub-graph within the SpAN with the
exception of the heme-binding cytochrome-C fold module 140. Within this
network, there is a clear downhill redox energetic gradient, starting
from 4Fe-4S coordinating ferredoxin folds (module 85) with an average
potential of -430 mV and ending with more oxidizing hemes (modules 1737
at +168 mV; 1746 at +70 mV), the molybdenum containing module 16 (+204
mV) and copper module 72 at +325 mV. One can envision electrons
percolating from the center of this network to the periphery, driving
redox-coupled reactions along a metabolic pathway.
Multiple features of the SpAN suggest its structure provides insight
into the evolution of oxidoreductases in addition to their metabolic
function. Network models of growing systems indicate that nodes with
high centrality and connectivity are the first to arise [66-68]. In
the ProtReDox annotated sub-graph of the SpAN, flavin module 7 and
4Fe-4S module 85 are reducing such that they are energetically matched
with the early Earth redox environment. It is informative that the
annotated modules form a connected sub-graph within the SpAN. Most of
these modules correspond both to isolated protein electron carriers
[45] as well as being domains within larger oxidoreductases.
Assignment of potentials is easier within an isolated domain versus a
larger, multi-cofactor enzyme. Small, isolated modules would be useful
building blocks of larger enzymes, forming multi-domain structures
through duplication and diversification. Metal utilization for central
versus peripheral modules is largely consistent with metal availability
through geologic history [21, 69, 70], with early folds
incorporating iron-containing cofactors and later folds binding
molybdenum and copper.