Protein docking prediction using predicted protein-protein interface

<p>Abstract</p> <p>Background</p> <p>Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations.</p> <p>Results</p> <p>We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering.</p> <p>Conclusion</p> <p>We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.</p

'Gut health': a new objective in medicine?

'Gut health' is a term increasingly used in the medical literature and by the food industry. It covers multiple positive aspects of the gastrointestinal (GI) tract, such as the effective digestion and absorption of food, the absence of GI illness, normal and stable intestinal microbiota, effective immune status and a state of well-being. From a scientific point of view, however, it is still extremely unclear exactly what gut health is, how it can be defined and how it can be measured. The GI barrier adjacent to the GI microbiota appears to be the key to understanding the complex mechanisms that maintain gut health. Any impairment of the GI barrier can increase the risk of developing infectious, inflammatory and functional GI diseases, as well as extraintestinal diseases such as immune-mediated and metabolic disorders. Less clear, however, is whether GI discomfort in general can also be related to GI barrier functions. In any case, methods of assessing, improving and maintaining gut health-related GI functions are of major interest in preventive medicine

Association of Preoperative Growth Differentiation Factor-15 Concentrations and Postoperative Cardiovascular Events after Major Noncardiac Surgery.

BACKGROUND: The association between growth differentiation factor-15 concentrations and cardiovascular disease has been well described. The study hypothesis was that growth differentiation factor-15 may help cardiac risk stratification in noncardiac surgical patients, in addition to clinical evaluation. METHODS: The objective of the study was to determine whether preoperative serum growth differentiation factor-15 is associated with the composite primary outcome of myocardial injury after noncardiac surgery and vascular death at 30 days and can improve cardiac risk prediction in noncardiac surgery. This is a prospective cohort study of patients 45 yr or older having major noncardiac surgery. The association between preoperative growth differentiation factor-15 and the primary outcome was determined after adjusting for the Revised Cardiac Risk Index. Preoperative N-terminal-pro hormone brain natriuretic peptide was also added to compare predictive performance with growth differentiation factor-15. RESULTS: Between October 27, 2008, and October 30, 2013, a total of 5,238 patients were included who had preoperative growth differentiation factor-15 measured (median, 1,325; interquartile range, 880 to 2,132 pg/ml). The risk of myocardial injury after noncardiac surgery and vascular death was 99 of 1,705 (5.8%) for growth differentiation factor-15 less than 1,000 pg/ml, 161 of 1,332 (12.1%) for growth differentiation factor-15 1,000 to less than 1,500 pg/ml, 302 of 1476 (20.5%) for growth differentiation factor-15 1,500 to less than 3,000 pg/ml, and 247 of 725 (34.1%) for growth differentiation factor-15 concentrations 3,000 pg/ml or greater. Compared to patients who had growth differentiation factor-15 concentrations less than 1,000 pg/ml, the corresponding adjusted hazard ratio for each growth differentiation factor-15 category was 1.93 (95% CI, 1.50 to 2.48), 3.04 (95% CI, 2.41 to 3.84), and 4.8 (95% CI, 3.76 to 6.14), respectively. The addition of growth differentiation factor-15 improved cardiac risk classification by 30.1% (301 per 1,000 patients) compared to Revised Cardiac Risk Index alone. It also provided additional risk classification beyond the combination of preoperative N-terminal-pro hormone brain natriuretic peptide and Revised Cardiac Risk Index (16.1%; 161 per 1,000 patients). CONCLUSIONS: Growth differentiation factor-15 is strongly associated with 30-day risk of major cardiovascular events and significantly improved cardiac risk prediction in patients undergoing noncardiac surgery

Preoperative N-Terminal Pro-B-Type Natriuretic Peptide and Cardiovascular Events After Noncardiac Surgery A Cohort Study

Background: Preliminary data suggest that preoperative N-terminal pro–B-type natriuretic peptide (NT-proBNP) may improve risk prediction in patients undergoing noncardiac surgery. Objective: To determine whether preoperative NT-proBNP has additional predictive value beyond a clinical risk score for the composite of vascular death and myocardial injury after noncardiac surgery (MINS) within 30 days after surgery. Design: Prospective cohort study. Setting: 16 hospitals in 9 countries. Patients: 10 402 patients aged 45 years or older having inpatient noncardiac surgery. Measurements: All patients had NT-proBNP levels measured before surgery and troponin T levels measured daily for up to 3 days after surgery. Results: In multivariable analyses, compared with preoperative NT-proBNP values less than 100 pg/mL (the reference group), those of 100 to less than 200 pg/mL, 200 to less than 1500 pg/mL, and 1500 pg/mL or greater were associated with adjusted hazard ratios of 2.27 (95% CI, 1.90 to 2.70), 3.63 (CI, 3.13 to 4.21), and 5.82 (CI, 4.81 to 7.05) and corresponding incidences of the primary outcome of 12.3% (226 of 1843), 20.8% (542 of 2608), and 37.5% (223 of 595), respectively. Adding NT-proBNP thresholds to clinical stratification (that is, the Revised Cardiac Risk Index [RCRI]) resulted in a net absolute reclassification improvement of 258 per 1000 patients. Preoperative NT-proBNP values were also statistically significantly associated with 30-day all-cause mortality (less than 100 pg/mL [incidence, 0.3%], 100 to less than 200 pg/mL [incidence, 0.7%], 200 to less than 1500 pg/mL [incidence, 1.4%], and 1500 pg/mL or greater [incidence, 4.0%]). Limitation: External validation of the identified NT-proBNP thresholds in other cohorts would reinforce our findings. Conclusion: Preoperative NT-proBNP is strongly associated with vascular death and MINS within 30 days after noncardiac surgery and improves cardiac risk prediction in addition to the RCRI

Fractal dimension as a measure of surface roughness of G protein-coupled receptors: implications for structure and function

Protein surface roughness is a structural property associated with ligand-protein and protein-protein binding interfaces. In this work we apply for the first time the concept of surface roughness, expressed as the fractal dimension, to address structure and function of G protein-coupled receptors (GPCRs) which are an important group of drug targets. We calculate the exposure ratio and the fractal dimension for helix-forming residues of the β(2) adrenergic receptor (β(2)AR), a model system in GPCR studies, in different conformational states: in complex with agonist, antagonist and partial inverse agonists. We show that both exposure ratio and roughness exhibit periodicity which results from the helical structure of GPCRs. The pattern of roughness and exposure ratio of a protein patch depends on its environment: the residues most exposed to membrane are in general most rough whereas parts of receptors mediating interhelical contacts in a monomer or protein complex are much smoother. We also find that intracellular ends (TM3, TM5, TM6 and TM7) which are relevant for G protein binding and thus receptor signaling, are exposed but smooth. Mapping the values of residual fractal dimension onto receptor 3D structures makes it possible to conclude that the binding sites of orthosteric ligands as well as of cholesterol are characterized with significantly higher roughness than the average for the whole protein. In summary, our study suggests that identification of specific patterns of roughness could be a novel approach to spot possible binding sites which could serve as original drug targets for GPCRs modulation

Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl--like molecules binding.

International audienceBACKGROUND: Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides. METHOD: We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives. RESULTS: Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots. CONCLUSIONS: The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins