Influence of the molecular environment on phosphorylated amino acid models: a density functional theory study

Single-Molecule Force Spectroscopy Reveals Adhesion-by-Demand in Statherin at the Protein-Hydroxyapatite Interface

Achieving strong adhesion in wet environments remains a technological challenge in biomedical applications demanding biocompatibility. Attention for adhesive motifs meeting such demands has largely been focused on marine organisms. However, bioadhesion to inorganic surfaces is also present in the human body, in the hard tissues of teeth and bones, and is mediated through serines (S). The specific amino acid sequence DpSpSEEKC has been previously suggested to be responsible for the strong binding abilities of the protein statherin to hydroxyapatite, where pS denotes phosphorylated serine. Notably, similar sequences are present in the non-collagenous bone protein osteopontin (OPN) and the mussel foot protein 5 (Mefp5). OPN has previously been shown to promote fracture toughness and physiological damage formation. Here, we investigated the adhesion strength of the motif D(pS)(pS)EEKC on substrates of hydroxyapatite, TiO₂, and mica using atomic force microscopy (AFM) single-molecule force spectroscopy (SMFS). Specifically, we investigated the dependence of adhesion force on phosphorylation of serines by comparing findings with the unphosphorylated variant DSSEEKC. Our results show that high adhesion forces of over 1 nN on hydroxyapatite and on TiO₂ are only present for the phosphorylated variant D(pS)(pS)EEKC. This warrants further exploitation of this motif or similar residues in technological applications. Further, the dependence of adhesion force on phosphorylation suggests that biological systems potentially employ an adhesion-by-demand mechanism via expression of enzymes that up- or down-regulate phosphorylation, to increase or decrease adhesion forces, respectively.

Correlations between the structure and the vibrational spectrum of the phosphate group. Implications for the analysis of an important functional group in phosphoproteins

Density functional theory calculations were used to establish correlations between the structure and the vibrational spectrum of the phosphate group in model compounds for phosphorylated amino acids. The model compounds were acetyl phosphate, methyl phosphate, and p-tolyl phosphate, which represented the phosphorylated amino acids aspartyl phosphate, serine or threonine phosphate, and tyrosine phosphate, respectively. The compounds were placed in different environments consisting of one or several HF or H₂O molecules, which modeled interactions of phosphorylated amino acids in the protein environment. The calculations were performed with the B3LYP functional and the 6-311++G(3df, 3pd) basis set. In general, the wavenumbers (or frequencies) of the stretching vibrations of the terminal P–O bonds correlated better with bond lengths of the phosphate group than with its bond angles. The best correlations were obtained with the shortest and the mean terminal P–O bond lengths with standard deviations from the trend line of only 0.2 pm. Other useful correlations were observed with the bond length difference between the shortest and longest terminal P–O bond and with the bond length of the bridging P–O bond.

Vibrational frequencies of phosphate are sensitive to bond length changes on the sub-picometer scale.

Quantifying bond distortions in transient enzyme species by a combination of density functional theory calculations and time-resolved infrared difference spectroscopy. Implications for the mechanism of dephosphorylation of the sarcoplasmic reticulum Ca(2+)-ATPase (SERCA1a)

The sarcoplasmic Ca(2+)-ATPase (SERCA1a) forms two phosphoenzyme intermediates during Ca(2+) pumping. The second intermediate E2P hydrolyzes rapidly, which is essential for the rapid removal of Ca(2+) from the cytosol of muscle cells. The present work studies whether a weakening of the scissile PO bond in the E2P ground state facilitates dephosphorylation. To this end, the experimentally known vibrational spectrum of the E2P phosphate group was calculated with density functional theory (DFT) using structural models at two levels of structural complexity: (i) Models of acetyl phosphate in simple environments and (ii) ~150 atom models of the catalytic site. It was found that the enzyme environment distorts the structure of the phosphate group: one of the terminal PO bonds is shorter in the catalytic site indicating weaker interactions than in water. However, the bond that bridges phosphate and Asp351 is unaffected. This indicates that the scissile PO bond is not weakened by the enzyme environment of E2P. A second finding was that the catalytic site of the E2P state in aqueous solution appears to adopt a structure as in the crystals with BeF3(-), where the ATPase is in a non-reactive conformation. The reactant state of the dephosphorylation reaction differs from the E2P ground state: Glu183 faces Asp351 and positions the attacking water molecule. This state has a 0.04Å longer, and thus weaker, bridging PO bond. The reactant state is not detected in our experiments, indicating that its energy is at least 1kcal/mol higher than that of the E2P ground state.

Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique

As a widespread type of protein post-translational modifications (PTMs), succinylation plays an important role in regulating protein conformation, function and physicochemical properties. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of succinylation sites are much desirable due to their convenient and fast speed. Currently, numerous computational models have been developed to identify PTMs sites through various types of two-class machine learning algorithms. These methods require both positive and negative samples for training. However, designation of the negative samples of PTMs was difficult and if it is not properly done can affect the performance of computational models dramatically. So that in this work, we implemented the first application of positive samples only learning (PSoL) algorithm to succinylation sites prediction problem, which was a special class of semi-supervised machine learning that used positive samples and unlabeled samples to train the model. Meanwhile, we proposed a novel succinylation sites computational predictor called SucPred (succinylation site predictor) by using multiple feature encoding schemes. Promising results were obtained by the SucPred predictor with an accuracy of 88.65% using 5-fold cross validation on the training dataset and an accuracy of 84.40% on the independent testing dataset, which demonstrated that the positive samples only learning algorithm presented here was particularly useful for identification of protein succinylation sites. Besides, the positive samples only learning algorithm can be applied to build predictors for other types of PTMs sites with ease. A web server for predicting succinylation sites was developed and was freely accessible at http://59.73.198.144:8088/SucPred/.

PGluS: prediction of protein S-glutathionylation sites with multiple features and analysis

S-Glutathionylation is a reversible protein post-translational modification, which generates mixed disulfides between glutathione (GSH) and cysteine residues, playing an important role in regulating protein stability, activity, and redox regulation. To fully understand S-glutathionylation mechanisms, identification of substrates and specific S-glutathionylated sites is crucial. Compared with the labor-intensive and time-consuming experimental approaches, computational predictions of S-glutathionylated sites are very desirable due to their convenience and high speed. Therefore, in this study, a new bioinformatics tool named PGluS was developed to predict S-glutathionylated sites based on multiple features and support vector machines. The performance of PGluS was measured with an accuracy of 71.41% and a MCC of 0.431 using the 5-fold cross-validation on the training dataset. Additionally, PGluS was evaluated using an independent testing dataset resulting in an accuracy of 71.25%, which demonstrated that PGluS was very promising for predicting S-glutathionylated sites. Furthermore, feature analysis was performed and it was shown that all features adopted in this method contributed to the S-glutathionylation process. A site-specific analysis showed that S-glutathionylation was intimately correlated with the features derived from its surrounding sites. The conclusions derived from this study might help to understand more of the S-glutathionylation mechanism and guide the related experimental validation. For public access, PGluS is freely accessible at .

Charge density distribution and theoretical analysis of low and high energy phosphate esters

There is a strict relation between the energy of hydrolysis of phosphate esters and the extent of interactions between the p ester oxygen lone pair and the antibonding orbitals of the rest of the molecule. Its impact on experimental charge density distribution is analyzed.

Hyperconjugation-mediated solvent effects in phosphoanhydride bonds

Density functional theory and natural bond orbital analysis are used to explore the impact of solvent on hyperconjugation in methyl triphosphate, a model for "energy rich" phosphoanhydride bonds, such as found in ATP. As expected, dihedral rotation of a hydroxyl group vicinal to the phosphoanhydride bond reveals that the conformational dependence of the anomeric effect involves modulation of the orbital overlap between the donor and acceptor orbitals. However, a conformational independence was observed in the rotation of a solvent hydrogen bond. As one lone pair orbital rotates away from an optimal antiperiplanar orientation, the overall magnitude of the anomeric effect is compensated approximately by the other lone pair as it becomes more antiperiplanar. Furthermore, solvent modulation of the anomeric effect is not restricted to the antiperiplanar lone pair; hydrogen bonds involving gauche lone pairs also affect the anomeric interaction and the strength of the phosphoanhydride bond. Both gauche and anti solvent hydrogen bonds lengthen nonbridging O-P bonds, increasing the distance between donor and acceptor orbitals and decreasing orbital overlap, which leads to a reduction of the anomeric effect. Solvent effects are additive with greater reduction in the anomeric effect upon increasing water coordination. By controlling the coordination environment of substrates in an active site, kinases, phosphatases, and other enzymes important in metabolism and signaling may have the potential to modulate the stability of individual phosphoanhydride bonds through stereoelectronic effects.

PMeS: prediction of methylation sites based on enhanced feature encoding scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.