Analysis of binding properties and specificity through identification of the interface forming residues (IFR) for serine proteases in silico docked to different inhibitors

<p>Abstract</p> <p>Background</p> <p>Enzymes belonging to the same super family of proteins in general operate on variety of substrates and are inhibited by wide selection of inhibitors. In this work our main objective was to expand the scope of studies that consider only the catalytic and binding pocket amino acids while analyzing enzyme specificity and instead, include a wider category which we have named the Interface Forming Residues (IFR). We were motivated to identify those amino acids with decreased accessibility to solvent after docking of different types of inhibitors to sub classes of serine proteases and then create a table (matrix) of all amino acid positions at the interface as well as their respective occupancies. Our goal is to establish a platform for analysis of the relationship between IFR characteristics and binding properties/specificity for bi-molecular complexes.</p> <p>Results</p> <p>We propose a novel method for describing binding properties and delineating serine proteases specificity by compiling an exhaustive table of interface forming residues (IFR) for serine proteases and their inhibitors. Currently, the Protein Data Bank (PDB) does not contain all the data that our analysis would require. Therefore, an <it>in silico </it>approach was designed for building corresponding complexes</p> <p>The IFRs are obtained by "rigid body docking" among 70 structurally aligned, sequence wise non-redundant, serine protease structures with 3 inhibitors: bovine pancreatic trypsin inhibitor (BPTI), ecotine and ovomucoid third domain inhibitor. The table (matrix) of all amino acid positions at the interface and their respective occupancy is created. We also developed a new computational protocol for predicting IFRs for those complexes which were not deciphered experimentally so far, achieving accuracy of at least 0.97.</p> <p>Conclusions</p> <p>The serine proteases interfaces prefer polar (including glycine) residues (with some exceptions). Charged residues were found to be uniquely prevalent at the interfaces between the "miscellaneous-virus" subfamily and the three inhibitors. This prompts speculation about how important this difference in IFR characteristics is for maintaining virulence of those organisms.</p> <p>Our work here provides a unique tool for both structure/function relationship analysis as well as a compilation of indicators detailing how the specificity of various serine proteases may have been achieved and/or could be altered. It also indicates that the interface forming residues which also determine specificity of serine protease subfamily can not be presented in a canonical way but rather as a matrix of alternative populations of amino acids occupying variety of IFR positions.</p

Safety of intravenous ferric carboxymaltose versus oral iron in patients with nondialysis-dependent CKD: an analysis of the 1-year FIND-CKD trial.

Background: The evidence base regarding the safety of intravenous (IV) iron therapy in patients with chronic kidney disease (CKD) is incomplete and largely based on small studies of relatively short duration. Methods: FIND-CKD (ClinicalTrials.gov number NCT00994318) was a 1-year, open-label, multicenter, prospective study of patients with nondialysis-dependent CKD, anemia and iron deficiency randomized (1:1:2) to IV ferric carboxymaltose (FCM), targeting higher (400-600 µg/L) or lower (100-200 µg/L) ferritin, or oral iron. A post hoc analysis of adverse event rates per 100 patient-years was performed to assess the safety of FCM versus oral iron over an extended period. Results: The safety population included 616 patients. The incidence of one or more adverse events was 91.0, 100.0 and 105.0 per 100 patient-years in the high ferritin FCM, low ferritin FCM and oral iron groups, respectively. The incidence of adverse events with a suspected relation to study drug was 15.9, 17.8 and 36.7 per 100 patient-years in the three groups; for serious adverse events, the incidence was 28.2, 27.9 and 24.3 per 100 patient-years. The incidence of cardiac disorders and infections was similar between groups. At least one ferritin level ≥800 µg/L occurred in 26.6% of high ferritin FCM patients, with no associated increase in adverse events. No patient with ferritin ≥800 µg/L discontinued the study drug due to adverse events. Estimated glomerular filtration rate remained the stable in all groups. Conclusions: These results further support the conclusion that correction of iron deficiency anemia with IV FCM is safe in patients with nondialysis-dependent CKD

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to &lt;90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], &gt;300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of &lt;15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P&lt;0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P&lt;0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Cut-off performance dependence of Sting-LDA-WNA classifier.

<p>The performance indicators observed for the classifiers built from DS30. When the cut-off is increased from 0.10 to 0.50, the accuracy increases and reaches its peak, and then it gradually decreases with a further increase in the cut-off value. The precision rate only grows as the cut-off is increased, that is, for higher cut-off values, fewer entries are classified as IFR, leading to more false positives. For sensitivity, the opposite behavior is observed (illustrating the performance trade-off). When increasing the cut-off, more entries are labeled as FSR, and fewer labeled as IFR are misclassified. The highest MCC value occurred when using the same cut-off as that for the highest accuracy, which is 0.50. Box plots were obtained with 10-fold cross validation.</p

Comparing LDA classifiers with ROC analysis.

<p>Performance evaluation using ROC analysis for the tryptophan LDA classifier (a), glycine LDA classifier (b) and the aggregated result (in gray) of the 20 independent amino acid LDA classifiers (d). In blue, 10 ROC curves (from 10-fold cross validation tests) are presented for the classifier that is not specific to the type of amino acid. Ten-fold cross validation was used, and the performance indicators AUC and MCC are displayed for both classifiers in (a) and (b). The results for all generated classifiers are shown in (c): the AUC (white) and MCC (gray). Tryptophan serves as the best classifier but is closely followed by (in AUC criteria) aspartic acid, methionine, isoleucine, leucine and valine. The Sting-LDA aggregated classifier, which uses 20 amino acid-specific classifiers with no WNA descriptors, has an AUC average of 0.828, whereas the amino acid-unspecific classifier has an AUC average of 0.751.</p

Comparison of Sting-LDA-WNA to other methods based on the test set 35Enz and induced fit assessment on benchmark 4.0 cases.

<p>(a) By selecting different thresholds for sensitivity, or coverage, the precision of the methods for IFR classification can be compared. For high interface coverage values (75%), Sting-LDA-WNA (marked in this figure as STING-LDA) has the highest precision among the used methods (37%). For balanced coverage (50%), Sting-LDA ranks third (47%), but not distant from PINUP (48%) and Meta-PPISP (50%) methods. For lower coverage (25%), Meta-PPISP still ranks first achieving 70% precision while PINUP and Sting-LDA have similar precision (59%). (b) Sting-LDA-WNA performance on the “medium” and “difficult” classes of the protein-protein docking benchmark, resulting in 6% decrease as compared to the DS30 performance, by using the AUC rate, achieving 0.72.</p

Comparing LDA classifiers using weighted neighbor averages (WNA) descriptors with ROC analysis.

<p>The results for all generated amino acid classifiers are shown in (a): the AUC (white) and MCC (gray). The amino acid ranking order is similar to that of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087107#pone-0087107-g001" target="_blank">figure 1</a>-c, except for glycine and cysteine LDA models. The Sting-LDA-WNA aggregated classifier from the 20 amino acid-specific classifiers with WNA descriptors has an AUC average of 0.949, whereas the amino acid-unspecific classifier average was 0.944 (indicating that the performance gain while using the formulated amino acid-specific classifiers is statistically relevant and, therefore, recommended for better IFR classification).</p