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Protein Surface Characterization Using an Invariant Descriptor

机译:使用不变描述符的蛋白质表面表征

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Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database. Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors. Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included.
机译:目标。要开发用于表征蛋白质表面的新的不变描述符,适用于各种分析任务,例如蛋白质功能分类以及在大型数据库中搜索和检索蛋白质表面。方法。我们从蛋白质表面上选定的圆形补丁的局部描述符开始。描述符记录中心残基和补丁中残基之间的距离分布,并跟踪补丁中特定成对残基共现的数量。然后,通过结合来自局部描述符的信息来构建整个蛋白质表面的全局描述符。我们的方法在关注残基特异性距离分布以及使用残基距离共现作为提出的蛋白质表面描述符的基础上是新颖的。结果。给出了蛋白质分类和检索三个蛋白质家族的结果。对于这三个家族,我们在曲线下获得了一个面积,其精确度和召回率范围为0.6494(无残基共存)至0.6683(有残基共存)。使用其他两个蛋白质家族的大规模筛选将相关家族成员放在首位,还检索了许多未表征的蛋白质。包括与其他建议方法的比较结果。

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