SYNTHESIS OF POLY(3,4-AZOPYRIDYLENE)AND OXYGEN-BINDING AFFINITY OF ITS COMPLEX WITH COBALTPORPHYRIN

Synthesis and characteristics of poly(3,4-azopyridylene)(PAP),conductivity and oxygen-binding affinity of its complex with meso-α,α,α,α-tetrakis(o-pivalamidophenyl)porphyrinatocobalt(II)(COP)were studied.PAP was prepared by oxidative polymerization of 3,4-diaminopyridine(DAP)in DMF solution using CuCl/pyridine as the catalyst.IR and NMR results showed that the peak of amido group in DAP was converted to the azo group in PAP and a π conjugated polymer was synthesized.The average molecular weight of PAP was determined to be 5.0×10~3.The PAP-CoP complex was prepared by complexing the pyridyl group of PAP with the fifth coordination site of CoP in DMF solution.In comparison with the CoP complex with a non-π conjugated polymer,the PAP-CoP complex shows good electroconductivity of 5.8×10^(-6) S cm^(-1).The PAP-CoP complex displays a reversible change in the UV-Visible absorption spectrum from the deoxy form to the oxy or oxygen-binding one with an isosbestic point,in response to the partial oxygen pressure of the atmosphere.The oxygen- response behavior was monitored at the absorbance ascribed to the oxy form at 548 nm to give the oxygen-binding affinity. The oxygen-binding equilibrium curves of PAP-CoP complex obey a Langmuir isotherm.DMF has great effects on the oxygen-binding properties of the PAP-CoP complex.The oxygen-binding affinity of PAP-CoP complex in the solid state is higher than that in DMF solution.With decreasing temperature,the oxygen-binding affinity of the PAP-CoP complex increases.

Oxypred:Prediction and Classification of Oxygen-Binding Proteins

This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine （SVM） modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins （available from http：//www.imtech.res.in/raghava/oxypred/）.