Temperature dependence of binding and catalysis for the Cdc25B phosphatase

Three new pigment protein tyrosine phosphatases inhibitors from the insect parasite fungus Cordyceps gracilioides: terreusinone A, pinophilin C and cryptosporioptide A

Three new pigment compounds--terreusinone A (1), pinophilin C (2) and cryptosporioptide A (3)-were isolated from a solid culture of Cordyceps gracilioides. The structures of these compounds were determined by extensive spectroscopic analysis including HRESIMS, 1D- and 2D-NMR. The structure of terreusinone A (1) was further confirmed by single-crystal X-ray crystallographic diffraction analysis. In an in vitro activity assay, 1, 2 and 3 exhibited high inhibitory activity against PTP1B, SHP2, CDC25B, LAR and SHP1. Terreusinone A (1) inhibited PTP1B, SHP2, CDC25B, LAR and SHP1 enzyme with IC50 values 12.5, >50, 4.1, 10.6, 5.6 µg/mL, respectively; pinophilin C (2) with IC50 values 6.8, 8.0, 4.5, 4.7, 3.4 µg/mL, respectively; and cryptosporioptide A (3) with IC50 values 7.3, 5.7, 7.6, >50, 4.9 µg/mL, respectively.

Position-specific analysis and prediction for protein lysine acetylation based on multiple features

Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.

Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

BACKGROUND

Tyrosine sulfation is one of the most important posttranslational modifications. Due to its relevance to various disease developments, tyrosine sulfation has become the target for drug design. In order to facilitate efficient drug design, accurate prediction of sulfotyrosine sites is desirable. A predictor published seven years ago has been very successful with claimed prediction accuracy of 98%. However, it has a particularly low sensitivity when predicting sulfotyrosine sites in some newly sequenced proteins.

RESULTS

A new approach has been developed for predicting sulfotyrosine sites using the random forest algorithm after a careful evaluation of seven machine learning algorithms. Peptides are formed by consecutive residues symmetrically flanking tyrosine sites. They are then encoded using an amino acid hydrophobicity scale. This new approach has increased the sensitivity by 22%, the specificity by 3%, and the total prediction accuracy by 10% compared with the previous predictor using the same blind data. Meanwhile, both negative and positive predictive powers have been increased by 9%. In addition, the random forest model has an excellent feature for ranking the residues flanking tyrosine sites, hence providing more information for further investigating the tyrosine sulfation mechanism. A web tool has been implemented at http://ecsb.ex.ac.uk/sulfotyrosine for public use.

CONCLUSION

The random forest algorithm is able to deliver a better model compared with the Hidden Markov Model, the support vector machine, artificial neural networks, and others for predicting sulfotyrosine sites. The success shows that the random forest algorithm together with an amino acid hydrophobicity scale encoding can be a good candidate for peptide classification.

Mechanism of Cdc25B phosphatase with the small molecule substrate p-nitrophenyl phosphate from QM/MM-MFEP calculations

Cdc25B is a dual-specificity phosphatase that catalyzes the dephosphorylation of the Cdk2/CycA protein complex. This enzyme is an important regulator of the human cell cycle and has been identified as a potential anticancer target. In general, protein tyrosine phosphatases are thought to bind the dianionic form of the phosphate and employ general acid catalysis via the Asp residue in the highly conserved WPD-loop. However, the Cdc25 phosphatases form a special subfamily based on their distinct differences from other protein tyrosine phosphatases. Although Cdc25B contains the (H/V)CX(5)R catalytic motif present in all other protein tyrosine phosphatases, it lacks an analogous catalytic acid residue. No crystallographic data currently exist for the complex of Cdc25B with Cdk2/CycA, so in addition to its natural protein substrate, experimental and theoretical studies are often carried out with small molecule substrates. In an effort to gain understanding of the dephosphorylation mechanism of Cdc25B with a commonly used small molecule substrate, we have performed simulations of the rate-limiting step of the reaction catalyzed by Cdc25B with the substrate p-nitrophenyl phosphate using the recently developed QM/MM Minimum Free Energy Path method (Hu et al. J. Chem. Phys. 2008, 034105). We have simulated the first step of the reaction with both the monoanionic and the dianionic forms of the substrate, and our calculations favor a mechanism involving the monoanionic form. Thus, Cdc25 may employ a unique dephosphorylation mechanism among protein tyrosine phosphatases, at least in the case of the small molecule substrate p-nitrophenyl phosphate.