Charge environments around phosphorylation sites in proteins

Background: Phosphorylation is a central feature in many biological processes. Structural analyses have identified the importance of charge-charge interactions, for example mediating phosphorylation-driven allosteric change and protein binding to phosphopeptides. Here, we examine computationally the prevalence of charge stabilisation around phosphorylated sites in the structural database, through comparison with locations that are not phosphorylated in the same structures. Results: A significant fraction of phosphorylated sites appear to be electrostatically stabilised, largely through interaction with sidechains. Some examples of stabilisation across a subunit interface are evident from calculations with biological units. When considering the immediately surrounding environment, in many cases favourable interactions are only apparent after conformational change that accompanies phosphorylation. A simple calculation of potential interactions at longer-range, applied to non-phosphorylated structures, recovers the separation exhibited by phosphorylated structures. In a study of sites in the Phospho.ELM dataset, for which structural annotation is provided by non-phosphorylated proteins, there is little separation of the known phospho-acceptor sites relative to background, even using the wider interaction radius. However, there are differences in the distributions of patch polarity for acceptor and background sites in the Phospho.ELM dataset. Conclusion: In this study, an easy to implement procedure is developed that could contribute to the identification of phospho-acceptor sites associated with charge-charge interactions and conformational change. Since the method gives information about potential anchoring interactions subsequent to phosphorylation, it could be combined with simulations that probe conformational change. Our analysis of the Phospho.ELM dataset also shows evidence for mediation of phosphorylation effects through (i) conformational change associated with making a solvent inaccessible phospho-acceptor site accessible, and (ii) modulation of protein-protein interactions

John Warwicker

John Warwicker, graphic designer, typographer, photographer and writer discusses his work

Delineation of RAID1, the RACK1 interaction domain located within the unique N-terminal region of the cAMP-specific phosphodiesterase, PDE4D5

Background The cyclic AMP specific phosphodiesterase, PDE4D5 interacts with the β-propeller protein RACK1 to form a signaling scaffold complex in cells. Two-hybrid analysis of truncation and mutant constructs of the unique N-terminal region of the cAMP-specific phosphodiesterase, PDE4D5 were used to define a domain conferring interaction with the signaling scaffold protein, RACK1. Results Truncation and mutagenesis approaches showed that the RACK1-interacting domain on PDE4D5 comprised a cluster of residues provided by Asn-22/Pro-23/Trp-24/Asn-26 together with a series of hydrophobic amino acids, namely Leu-29, Val-30, Leu-33, Leu-37 and Leu-38 in a 'Leu-Xaa-Xaa-Xaa-Leu' repeat. This was done by 2-hybrid analyses and then confirmed in biochemical pull down analyses using GST-RACK1 and mutant PDE4D5 forms expressed in COS cells. Mutation of Arg-34, to alanine, in PDE4D5 attenuated its interaction with RACK1 both in 2-hybrid screens and in pull down analyses. A 38-mer peptide, whose sequence reflected residues 12 through 49 of PDE4D5, bound to RACK1 with similar affinity to native PDE4D5 itself (Ka circa 6 nM). Conclusions The RACK1 Interaction Domain on PDE4D5, that we here call RAID1, is proposed to form an amphipathic helical structure that we suggest may interact with the C-terminal β-propeller blades of RACK1 in a manner akin to the interaction of the helical G-γ signal transducing protein with the β-propeller protein, G-β

Visualization of poly(ADP-ribose) bound to PARG reveals inherent balance between exo- and endo-glycohydrolase activities

Poly-ADP-ribosylation is a post-translational modification that regulates processes involved in genome stability. Breakdown of the poly(ADP-ribose) (PAR) polymer is catalysed by poly(ADP-ribose) glycohydrolase (PARG), whose endo-glycohydrolase activity generates PAR fragments. Here we present the crystal structure of PARG incorporating the PAR substrate. The two terminal ADP-ribose units of the polymeric substrate are bound in exo-mode. Biochemical and modelling studies reveal that PARG acts predominantly as an exo-glycohydrolase. This preference is linked to Phe902 (human numbering), which is responsible for low-affinity binding of the substrate in endo-mode. Our data reveal the mechanism of poly-ADP-ribosylation reversal, with ADP-ribose as the dominant product, and suggest that the release of apoptotic PAR fragments occurs at unusual PAR/PARG ratios

Simulation of non-specific protein–mRNA interactions

Protein–nucleic acid interactions exhibit varying degrees of specificity. Relatively high affinity, sequence-specific interactions, can be studied with structure determination, but lower affinity, non-specific interactions are also of biological importance. We report simulations that predict the population of nucleic acid paths around protein surfaces, and give binding constant differences for changes in the protein scaffold. The method is applied to the non-specific component of interactions between eIF4Es and messenger RNAs that are bound tightly at the cap site. Adding a fragment of eIF4G to the system changes both the population of mRNA paths and the protein–mRNA binding affinity, suggesting a potential role for non-specific interactions in modulating translational properties. Generally, the free energy simulation technique could work in harness with characterized tethering points to extend analysis of nucleic acid conformation, and its modulation by protein scaffolds

Support vector machines within a bivariate mixed-integer linear programming framework

Support vector machines (SVMs) are a powerful machine learning paradigm, performing supervised learning for classification and regression analysis. A number of SVM models in the literature have made use of advances in mixed-integer linear programming (MILP) techniques in order to perform this task efficiently. In this work, we present three new models for SVMs that make use of piecewise linear (PWL) functions. This allows effective separation of data points where a simple linear SVM model may not be sufficient. The models we present make use of binary variables to assign data points to SVM segments, and hence fit within a recently presented framework for machine learning MILP models. Alongside presenting an inbuilt feature selection operator, we show that the models can benefit from robust inbuilt outlier detection. Experimental results show when each of the presented models is effective, and we present guidelines on which of the models are preferable in different scenarios

Mechanisms for stabilisation and the maintenance of solubility in proteins from thermophiles

BACKGROUND: The database of protein structures contains representatives from organisms with a range of growth temperatures. Various properties have been studied in a search for the molecular basis of protein adaptation to higher growth temperature. Charged groups have emerged as key distinguishing factors for proteins from thermophiles and mesophiles. RESULTS: A dataset of 291 thermophile-derived protein structures is compared with mesophile proteins. Calculations of electrostatic interactions support the importance of charges, but indicate that increases in charge contribution to folded state stabilisation do not generally correlate with the numbers of charged groups. Relative propensities of charged groups vary, such as the substitution of glutamic for aspartic acid sidechains. Calculations suggest an energetic basis, with less dehydration for longer sidechains. Most other properties studied show weak or insignificant separation of proteins from moderate thermophiles or hyperthermophiles and mesophiles, including an estimate of the difference in sidechain rotameric entropy upon protein folding. An exception is increased burial of alanine and proline residues and decreased burial of phenylalanine, methionine, tyrosine and tryptophan in hyperthermophile proteins compared to those from mesophiles. CONCLUSION: Since an increase in the number of charged groups for hyperthermophile proteins is separable from charged group contribution to folded state stability, we hypothesise that charged group propensity is important in the context of protein solubility and the prevention of aggregation. Accordingly we find some separation between mesophile and hyperthermophile proteins when looking at the largest surface patch that does not contain a charged sidechain. With regard to our observation that aromatic sidechains are less buried in hyperthermophile proteins, further analysis indicates that the placement of some of these groups may facilitate the reduction of folding fluctuations in proteins of the higher growth temperature organisms

Efficient continuous piecewise linear regression for linearising univariate non-linear functions

Due to their flexibility and ability to incorporate non-linear relationships, Mixed-Integer Non-Linear Programming (MINLP) approaches for optimization are commonly presented as a solution tool for real-world problems. Within this context, piecewise linear (PWL) approximations of non-linear continuous functions are useful, as opposed to non-linear machine learning-based approaches, since they enable the application of Mixed-Integer Linear Programming techniques in the MINLP framework, as well as retaining important features of the approximated non-linear functions, such as convexity. In this work, we extend upon fast algorithmic approaches for modeling discrete data using PWL regression by tuning them to allow the modeling of continuous functions. We show that if the input function is convex, then the convexity of the resulting PWL function is guaranteed. An analysis of the runtime of the presented algorithm shows which function characteristics affect the efficiency of the model, and which classes of functions can be modeled very quickly. Experimental results show that the presented approach is significantly faster than five existing approaches for modeling non-linear functions from the literature, at least 11 times faster on the tested functions, and up to a maximum speedup of more than 328,000. The presented approach also solves six benchmark problems for the first time

A unified framework for bivariate clustering and regression problems via mixed-integer linear programming

Clustering and regression are two of the most important problems in data analysis and machine learning. Recently, mixed-integer linear programs (MILPs) have been presented in the literature to solve these problems. By modelling the problems as MILPs, they are able to be solved very quickly by commercial solvers. In particular, MILPs for bivariate clusterwise linear regression (CLR) and (continuous) piecewise linear regression (PWLR) have recently appeared. These MILP models make use of binary variables and logical implications modelled through big-$\mathcal{M}$ constraints. In this paper, we present these models in the context of a unifying MILP framework for bivariate clustering and regression problems. We then present two new formulations within this framework, the first for ordered CLR, and the second for clusterwise piecewise linear regression (CPWLR). The CPWLR problem concerns simultaneously clustering discrete data, while modelling each cluster with a continuous PWL function. Extending upon the framework, we discuss how outlier detection can be implemented within the models, and how specific decomposition methods can be used to find speedups in the runtime. Experimental results show when each model is the most effective