Structural and energetic determinants of tyrosylprotein sulfotransferase sulfation specificity.

MOTIVATION: Tyrosine sulfation is a type of post-translational modification (PTM) catalyzed by tyrosylprotein sulfotransferases (TPST). The modification plays a crucial role in mediating protein-protein interactions in many biologically important processes. There is no well-defined sequence motif for TPST sulfation, and the underlying determinants of TPST sulfation specificity remains elusive. Here, we perform molecular modeling to uncover the structural and energetic determinants of TPST sulfation specificity. RESULTS: We estimate the binding affinities between TPST and peptides around tyrosines of both sulfated and non-sulfated proteins to differentiate them. We find that better differentiation is achieved after including energy costs associated with local unfolding of the tyrosine-containing peptide in a host protein, which depends on both the peptide's secondary structures and solvent accessibility. Local unfolding renders buried peptide-with ordered structures-thermodynamically available for TPST binding. Our results suggest that both thermodynamic availability of the peptide and its binding affinity to the enzyme are important for TPST sulfation specificity, and their interplay results into great variations in sequences and structures of sulfated peptides. We expect our method to be useful in predicting potential sulfation sites and transferable to other TPST variants. Our study may also shed light on other PTM systems without well-defined sequence and structural specificities. AVAILABILITY AND IMPLEMENTATION: All the data and scripts used in the work are available at http://dlab.clemson.edu/research/Sulfation.

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives.

BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server http://www.ces.clemson.edu/compbio/recon that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known.

Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins.

Protein stability and function relies on residues being in their appropriate ionization states at physiological pH. In situ residue pK(a)s also provides a sensitive measure of the local protein environment. Multiconformation continuum electrostatics (MCCE) combines continuum electrostatics and molecular mechanics force fields in Monte Carlo sampling to simultaneously calculate side chain ionization and conformation. The response of protein to charges is incorporated both in the protein dielectric constant (epsilon(prot)) of four and by explicit conformational changes. The pK(a) of 166 residues in 12 proteins was determined. The root mean square error is 0.83 pH units, and >90% have errors of <1 pH units whereas only 3% have errors >2 pH units. Similar results are found with crystal and solution structures, showing that the method's explicit conformational sampling reduces sensitivity to the initial structure. The outcome also changes little with protein dielectric constant (epsilon(prot) 4-20). Multiconformation continuum electrostatics titrations show coupling of conformational flexibility and changes in ionization state. Examples are provided where ionizable side chain position (protein G), Asn orientation (lysozyme), His tautomer distribution (RNase A), and phosphate ion binding (RNase A and H) change with pH. Disallowing these motions changes the calculated pK(a).