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Warwicker Group
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Research
In studies over several years, originating in collaborations with Paul Gane, Robert Freedman and Jim Bardwell, we have chosen the thioredoxin superfamily to develop and test our model for calculating the modulation of redox potential by protein environment. The geometry around the core Cxx  C redox motif is generally conserved
within the superfamily, putting the emphasis on the amino acids changes in the
neighbourhood. In this figure cysteine pKa (which tracks redox
potential) varies according to both identity of the xx residues
and the framework structure with the superfamily.Our continuum electrostatics model can progress in two directions (i) t  hrough combination with
more detailed models for redox potential variation, in providing a scheme that accounts for chemical as
well as structural change and (ii) through wider scale application
to structures and models. Following this second line, in a
superfamily with conserved structural motif, it should be possible to
build comparative models, in which it is largely the sequence and amino
acid sidechain variation that dictates functional differences. For a
larger superfamily, with thousands of representatives in the sequence
databases, this gives an opportunity to make large-scale predictions of
redox function, adding a structural element to sequence-based
functional annotation.We are also interested in how redox potential and substrate specificity combine in particular sub-families, such as the Dsb proteins and the PDI family. The era of Stru  ctural Genomics has reminded us how little
we understand about structure-based, as opposed to purely
sequence-based, annotation of protein function. Some things are
clear, such as the observation that enzyme active sites tend
to occur in relatively large clefts. We have found that this can
be quantitated neatly with a pseudo-charge calculation, where a field
is calculated from a protein that is uniformly-charged over its volume.
This allows us to find optimal values for predicting enzyme/non-enzyme,
and also gives an accurate method for locating an active site. We
can also address the question we started out wanting to answer - what
physical and chemical characteristics make an enzyme active site.
For example, the intermediate plots here are catalytic and
non-catalytic antibodies. In these terms, neither Ab version
appears particularly well-suited to catalysis. Application of this method to proteins of unknown function, but known structure yields an estimate that well under half of these are enzyme (to the right of vertical dotted line in this figure). It is possible to use any property in our pseudo-charge calculation, such as sequence profile values obtained from a multiple alignment. This fusion of evolutionary trace and physics-based methods can be a useful addition to functional site finding algorithms. Ultimately we want to get back to the question we originally asked (but didn't answer), not for functional sites in general: What energetic properties can we calculate to give clues to function?  Ion
channels represent a crossover between several areas of research,
physiology and biophysics, and more lately structural biology and
molecular simulation. For potassium channels, a variety of functional 
differences such as gating and conductance
are superposed on what appears to be a relatively uniform mechanism of
ion translocation, based on the selectivity filter structure. One
of the variations is pH-dependence of conductance, and (in a
collaboration with Mark Boyett) we have used calculations and sequence
analysis to rationalise pH properties where they are known in the Kv1
family, and to predict in other cases.A further collaboration, with James Magee, has allowed us to model differences in free energy of binding for prote  in-nucleic acid systems, in the first
instance mRNA and eIF4E. With a simplified
model for the non-specific component in complexes that exhibit a
tethering point, we predict how the binding energy varies as charge co mplementarity changes. In our test case, this was between
different eIF4E forms. Within the constraints of our mode, we see
the balancing of enthalpic and
entropic contributions and how the
average mRNA path can vary quite dramatically, according
to the underlying protein charge distribution. This different
eIF4E isoforms may have different affinities for capped mRNAs.
Our current RNA model does not include secondary structure,
and therefore needs improvement to study whether there could also be
some selectivity on the RNA side, perhaps mediated by base-pairing and
charge density.Our latest study in the biophysics area compares proteins from mesophiles and thermophiles. This is well-trodden path computationally, but we have come across a couple of novel observations. Firstly that the predicted average increase in stability due to interactions between ionisable groups, for proteins from hyperthermophiles, does not correlate with the number of ionisable groups. And secondly that, perhaps contray to expectation, amino acids with bulky non-polar sidechains (such as tryptophan) apopear on average to be more solvent exposed in proteins from higher growth temperature organisms.  A
major use of continuum electrostatics models has been the analysis of
pH-dependent properties, such as the free energy of folding (schematic
figure to the right). Although individual salt-bridges are often
relatively weak, their cumulative effect is demonstrated by the extent
to which many protein folded states lose stability at acidic pH.
This latter feature is one feature of interest in the analysis of
mis-folding diseases, but the influence of pH-dependence has a wide
molecular and physiological base, for example in control of enzyme
activity, uncoating for some viruses, and intracellular trafficking. Our group has been involved with the development
of methods to calculate pKa values and pH-dependence. It is important to
devise a scheme in which the larger pKa shifts of more buried ionisable groups are handled
alongside the more typical smaller shifts for groups that are
surrounded by water. We have combined Finite Difference Poisson-Boltzmann
and Debye-Huckel methods
into a hybrid FD/DH algorithm that accomplishes this task. This
work underpins much of our analysis into various systems, for example
comparison of proteins from thermophilic and mesophilic organisms,
potassium binding in ion channels, and study of proteins according to
subcellular location.  It is simple to test a calculated property against functional divisions that are populated in the PDB. In this example, we derive a set of protein structures that are annotated by subcellular location. We then see that the pH at which these proteins are predicted to be most stable appear, on average, to track with the pH of the subcellular compartment. A recurring theme in structural bioinformatics, given the number of coordinate files available, is to search for structure-function correlations that become evident over sets of proteins. |