Comput Biol Chem - Protein fold recognition based on functional domain composition.

Tópicos

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Resumo

Recognition of protein fold types is an important step in protein structure and function predictions and is also an important method in protein sequence-structure research. Protein fold type reflects the topological pattern of the structure's core. Now there are three methods of protein structure prediction, comparative modeling, fold recognition and de novo prediction. Since comparative modeling is limited by sequence similarity and there is too much workload in de novo prediction, fold recognition has the greatest potential. In order to improve recognition accuracy, a recognition method based on functional domain composition is proposed in this paper. This article focuses on the 124 fold types which have more than 2 samples in LIFCA database. We apply the functional domain composition to predict the fold types of a protein or a domain. In order to evaluate our method and its sensibility to the samples involving SCOP family divided, we tested our results from different aspects. The average sensitivity, specificity and Matthew's correlation coefficient (MCC) of the 124 fold types were found to be 94.58%, 99.96% and 0.91, respectively. Our results indicate that the functional domain composition method is a very promising method for protein fold recognition. And though based on simple classification rules, LIFCA database can grasp the functional features of different proteins, reflecting the corresponding relation between protein structure and function.

Resumo Limpo

recognit protein fold type import step protein structur function predict also import method protein sequencestructur research protein fold type reflect topolog pattern structur core now three method protein structur predict compar model fold recognit de novo predict sinc compar model limit sequenc similar much workload de novo predict fold recognit greatest potenti order improv recognit accuraci recognit method base function domain composit propos paper articl focus fold type sampl lifca databas appli function domain composit predict fold type protein domain order evalu method sensibl sampl involv scop famili divid test result differ aspect averag sensit specif matthew correl coeffici mcc fold type found respect result indic function domain composit method promis method protein fold recognit though base simpl classif rule lifca databas can grasp function featur differ protein reflect correspond relat protein structur function

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