Protein structure in 3D space
The EU-funded 'Searching protein structure space' (SPSS) project has fulfilled this need using an innovative 'filter-and-refine' paradigm on large datasets. For fast and accurate retrieval of data on structural similarity, the filter methods need to be indexable. Structural alignment heuristics were used to identify the most promising candidates in the refine stage. Project members successfully designed and validated the novel FragBag filter method where proteins are represented in terms of their backbone fragments. Receiver operating characteristic curve analysis validated the FragBag method for accuracy. Comparisons with other filter methods such as SGM, STRUCTAL and PRIDE demonstrated the superiority of FragBag in terms of speed and accuracy. Moreover, this method covers structure prediction as a structure is represented as a collection of sub-structures. Fixed-size vectors enable 3D spatial representation of structure. The SPSS scientists revealed previously undescribed fundamental properties by mapping the proteins to study spatial distribution as well as structural and functional diversity. A key finding was that functional diversity varies considerably across structure space. SPSS members have delivered an innovative tool to study and describe protein structures in spatial dimensions. In the future, researchers can study protein evolution andstructure-function relationship using this novel and unique tool.
Keywords
Protein, data bank, search system, structural query, indexable, FragBag filter, structure prediction, vector, spatial representation, mapping, structure-function relationship
