Soluble expression of proteins correlates with a lack of positively-charged surface.

Prediction of protein solubility is gaining importance with the growing use of protein molecules as therapeutics, and ongoing requirements for high level expression. We have investigated protein surface features that correlate with insolubility. Non-polar surface patches associate to some degree with insolubility, but this is far exceeded by the association with positively-charged patches. Negatively-charged patches do not separate insoluble/soluble subsets. The separation of soluble and insoluble subsets by positive charge clustering (area under the curve for a ROC plot is 0.85) has a striking parallel with the separation that delineates nucleic acid-binding proteins, although most of the insoluble dataset are not known to bind nucleic acid. Additionally, these basic patches are enriched for arginine, relative to lysine. The results are discussed in the context of expression systems and downstream processing, contributing to a view of protein solubility in which the molecular interactions of charged groups are far from equivalent.

SitesIdentify: a protein functional site prediction tool.

BACKGROUND: The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. RESULTS: Here we present a functional site prediction tool (SitesIdentify), based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. CONCLUSION: SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

Evidence for the adaptation of protein pH-dependence to subcellular pH.

BACKGROUND: The availability of genome sequences, and inferred protein coding genes, has led to several proteome-wide studies of isoelectric points. Generally, isoelectric points are distributed following variations on a biomodal theme that originates from the predominant acid and base amino acid sidechain pKas. The relative populations of the peaks in such distributions may correlate with environment, either for a whole organism or for subcellular compartments. There is also a tendency for isoelectric points averaged over a subcellular location to not coincide with the local pH, which could be related to solubility. We now calculate the correlation of other pH-dependent properties, calculated from 3D structure, with subcellular pH. RESULTS: For proteins with known structure and subcellular annotation, the predicted pH at which a protein is most stable, averaged over a location, gives a significantly better correlation with subcellular pH than does isoelectric point. This observation relates to the cumulative properties of proteins, since maximal stability for individual proteins follows the bimodal isoelectric point distribution. Histidine residue location underlies the correlation, a conclusion that is tested against a background of proteins randomised with respect to this feature, and for which the observed correlation drops substantially. CONCLUSION: There exists a constraint on protein pH-dependence, in relation to the local pH, that is manifested in the pKa distribution of histidine sub-proteomes. This is discussed in terms of protein stability, pH homeostasis, and fluctuations in proton concentration.

Subcellular pH and predicted pH-dependent features of proteins.

A characteristic of two-dimensional proteomics gels is a general bimodal distribution of isoelectric (pI) values. Discussion of this feature has focussed on the balance of acidic and basic ionisable residues, and potential relationships between pI distributions and organism classification or protein subcellular location. Electrostatics calculations on a set of protein structures with known subcellular location show that predicted folded state pI are similar to those calculated from sequence alone, but adjusted according to a general stabilising effect from interactions between ionisable groups. Bimodal distributions dominate both pI and the predicted pH of maximal stability. However, there are significant differences between these features. The average pH of maximal stability generally follows organelle pH. Average pI values are well removed from organelle pH in most subcellular environments, consistent with the view that proteins have evolved to carry (on average) net charge in a given subcellular location, and relevant to discussion of solubility in crowded environments. Correlation of the predicted pH of maximum stability with subcellular pH suggests an evolutionary pressure to adjust folded state interactions according to environment. Finally, our analysis of ionisable group contributions to stability suggests that Golgi proteins have the largest such term, although this dataset is small.