Predicting the protein-protein interactions using primary structures with predicted protein surface

<p>Abstract</p> <p>Background</p> <p>Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.</p> <p>Results</p> <p>This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.</p> <p>Conclusion</p> <p>This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an <it>F-measure </it>of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.</p

Extremely stable graphene electrodes doped with macromolecular acid

Although conventional p-type doping using small molecules on graphene decreases its sheet resistance (Rsh), it increases after exposure to ambient conditions, and this problem has been considered as the biggest impediment to practical application of graphene electrodes. Here, we report an extremely stable graphene electrode doped with macromolecular acid (perfluorinated polymeric sulfonic acid (PFSA)) as a p-type dopant. The PFSA doping on graphene provides not only ultra-high ambient stability for a very long time (&gt; 64 days) but also high chemical/thermal stability, which have been unattainable by doping with conventional small-molecules. PFSA doping also greatly increases the surface potential (similar to 0.8 eV) of graphene, and reduces its Rsh by similar to 56%, which is very important for practical applications. High-efficiency phosphorescent organic light-emitting diodes are fabricated with the PFSA-doped graphene anode (similar to 98.5 cd A(-1) without out-coupling structures). This work lays a solid platform for practical application of thermally-/chemically-/air-stable graphene electrodes in various optoelectronic devices

Measurement of the mass difference m(D-s(+))-m(D+) at CDF II

We present a measurement of the mass difference m(D-s(+))-m(D+), where both the D-s(+) and D+ are reconstructed in the phipi(+) decay channel. This measurement uses 11.6 pb(-1) of data collected by CDF II using the new displaced-track trigger. The mass difference is found to be m(D-s(+))-m(D+)=99.41+/-0.38(stat)+/-0.21(syst) MeV/c(2)