In the era of structural genomics, comparing a large number of protein structures can be a dauntingly time-consuming task. Traditional structural alignment methods, although offer accurate comparison, are not fast enough. Therefore, a number of databases storing pre-computed structural similarities are created to handle structural comparison queries efficiently. However, these databases cannot be updated in a timely fashion due to the sheer burden of computational requirements, thus offering only a rigid classification by some predefined parameters. Therefore, there is an increasingly urgent need for algorithms that can rapidly compare a large set of structures. Recently proposed projection methods, e.g., [1,2,3,4,5], show good promise for the development of fast structural database search solutions. Projection methods map a structure into a point in a high dimensional space and compare two structures by measuring distance between their projected points. These methods offer a tremendous increase in speed over residue-level structural alignment methods. However, current projection methods are not practical, partly because they are unable to identify local similarities. We propose a new projection-based approach that can rapidly detect global as well as local structural similarities. Local structural search is enabled by a topology-based writhe decomposition protocol (inspired by [4]) that produces a small number of fragments while ensuring that similar structures are cut in a similar manner. In a benchmark test for local structural similarity detection, we show that our method, Writher, dramatically improves accuracy over current leading projection methods [4,5] in terms of recognizing SCOP domains out of multidomain proteins.
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