BMC Med Inform Decis Mak - Efficient protein structure search using indexing methods.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ inform(2794) health(2639) internet(1427) }
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{ research(1085) discuss(1038) issu(1018) }
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{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Understanding functions of proteins is one of the most important challenges in many studies of biological processes. The function of a protein can be predicted by analyzing the functions of structurally similar proteins, thus finding structurally similar proteins accurately and efficiently from a large set of proteins is crucial. A protein structure can be represented as a vector by 3D-Zernike Descriptor (3DZD) which compactly represents the surface shape of the protein tertiary structure. This simplified representation accelerates the searching process. However, computing the similarity of two protein structures is still computationally expensive, thus it is hard to efficiently process many simultaneous requests of structurally similar protein search. This paper proposes indexing techniques which substantially reduce the search time to find structurally similar proteins. In particular, we first exploit two indexing techniques, i.e., iDistance and iKernel, on the 3DZDs. After that, we extend the techniques to further improve the search speed for protein structures. The extended indexing techniques build and utilize an reduced index constructed from the first few attributes of 3DZDs of protein structures. To retrieve top-k similar structures, top-10 ? k similar structures are first found using the reduced index, and top-k structures are selected among them. We also modify the indexing techniques to support -based nearest neighbor search, which returns data points less than to the query point. The results show that both iDistance and iKernel significantly enhance the searching speed. In top-k nearest neighbor search, the searching time is reduced 69.6%, 77%, 77.4% and 87.9%, respectively using iDistance, iKernel, the extended iDistance, and the extended iKernel. In -based nearest neighbor serach, the searching time is reduced 80%, 81%, 95.6% and 95.6% using iDistance, iKernel, the extended iDistance, and the extended iKernel, respectively.

Resumo Limpo

understand function protein one import challeng mani studi biolog process function protein can predict analyz function structur similar protein thus find structur similar protein accur effici larg set protein crucial protein structur can repres vector dzernik descriptor dzd compact repres surfac shape protein tertiari structur simplifi represent acceler search process howev comput similar two protein structur still comput expens thus hard effici process mani simultan request structur similar protein search paper propos index techniqu substanti reduc search time find structur similar protein particular first exploit two index techniqu ie idist ikernel dzds extend techniqu improv search speed protein structur extend index techniqu build util reduc index construct first attribut dzds protein structur retriev topk similar structur top k similar structur first found use reduc index topk structur select among also modifi index techniqu support base nearest neighbor search return data point less queri point result show idist ikernel signific enhanc search speed topk nearest neighbor search search time reduc respect use idist ikernel extend idist extend ikernel base nearest neighbor serach search time reduc use idist ikernel extend idist extend ikernel respect

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