基于相互作用的蛋白质功能预测

Inferring Protein Function from Protein-protein Interaction Networks

蛋白质功能预测是后基因时代研究的热点问题。基于相互作用的蛋白质功能预测方法目前应用比较广泛,但是当"伙伴蛋白质"(interacting partners)数目k较小时,其预测准确率不高。从蛋白质相互作用网络入手,结合"小世界网络"特性,有效解决了k较小时预测准确率不高的问题。对酵母(Saccharomyces cerevisiae)蛋白质的相互作用网络进行预测,当k≤4时其预测准确率比相同条件下的GO(global optimization)方法有一定提高。实验结果表明:该方法能够有效的应用于伙伴蛋白质数目较小时的蛋白质功能预测。

Prediction of protein function is the most challenging problem of the post-genomic era. The methods on the basis of protein-protein interaction networks are widely used at present. But when the interacting partners k is small the success rate is decreased, Here we propose a ＂small-world networks＂-based algorithm （SWN-BA） to infer protein function from Saccharomyces cerevisiae protein-protein interaction network, and get higher success rate with SWN-BA comparing with global optimization （GO） method when k ≤4. The thought of the ＂Small-world Networks＂ can well be used in the study of protein function from protein-protein interaction networks especially when k is small,