Dong Qiwen, Wang Xiaolong, Lin Lei, Guan Yi
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
BMC Bioinformatics. 2007 May 5;8:147. doi: 10.1186/1471-2105-8-147.
Recognition of binding sites in proteins is a direct computational approach to the characterization of proteins in terms of biological and biochemical function. Residue preferences have been widely used in many studies but the results are often not satisfactory. Although different amino acid compositions among the interaction sites of different complexes have been observed, such differences have not been integrated into the prediction process. Furthermore, the evolution information has not been exploited to achieve a more powerful propensity.
In this study, the residue interface propensities of four kinds of complexes (homo-permanent complexes, homo-transient complexes, hetero-permanent complexes and hetero-transient complexes) are investigated. These propensities, combined with sequence profiles and accessible surface areas, are inputted to the support vector machine for the prediction of protein binding sites. Such propensities are further improved by taking evolutional information into consideration, which results in a class of novel propensities at the profile level, i.e. the binary profiles interface propensities. Experiment is performed on the 1139 non-redundant protein chains. Although different residue interface propensities among different complexes are observed, the improvement of the classifier with residue interface propensities can be negligible in comparison with that without propensities. The binary profile interface propensities can significantly improve the performance of binding sites prediction by about ten percent in term of both precision and recall.
Although there are minor differences among the four kinds of complexes, the residue interface propensities cannot provide efficient discrimination for the complicated interfaces of proteins. The binary profile interface propensities can significantly improve the performance of binding sites prediction of protein, which indicates that the propensities at the profile level are more accurate than those at the residue level.
识别蛋白质中的结合位点是一种直接的计算方法，可从生物学和生化功能方面对蛋白质进行表征。残基偏好性已在许多研究中广泛使用，但结果往往不尽人意。尽管已观察到不同复合物相互作用位点之间存在不同的氨基酸组成，但这种差异尚未整合到预测过程中。此外，尚未利用进化信息来获得更强有力的倾向。
在本研究中，对四种复合物（同型永久复合物、同型瞬时复合物、异型永久复合物和异型瞬时复合物）的残基界面倾向进行了研究。这些倾向与序列谱和可及表面积相结合，输入到支持向量机中用于预测蛋白质结合位点。通过考虑进化信息进一步改进了这些倾向，从而在谱水平上产生了一类新的倾向，即二元谱界面倾向。对1139条非冗余蛋白质链进行了实验。尽管观察到不同复合物之间存在不同的残基界面倾向，但与不使用倾向的情况相比，使用残基界面倾向的分类器的改进可以忽略不计。二元谱界面倾向在精度和召回率方面都能显著提高结合位点预测的性能约10%。
尽管四种复合物之间存在细微差异，但残基界面倾向不能为蛋白质复杂界面提供有效的区分。二元谱界面倾向可以显著提高蛋白质结合位点预测的性能，这表明谱水平的倾向比残基水平的倾向更准确。
