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A method for probabilistic mapping between protein structure and function taxonomies through cross training

機(jī)譯：通過交叉訓(xùn)練在蛋白質(zhì)結(jié)構(gòu)和功能分類學(xué)之間進(jìn)行概率映射的方法

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Background: Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP [1]) and classes of functional classification databases (e.g. PROSITE [2]), structure and function of proteins could be probabilistically related. Results: We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees. Conclusion: We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.
機(jī)譯：背景：基于結(jié)構(gòu)的蛋白質(zhì)功能預(yù)測(cè)，反之亦然，這是一個(gè)部分解決的問題，主要在生物物理學(xué)和生物化學(xué)領(lǐng)域。這是解決計(jì)算問題和生物信息學(xué)方法的基礎(chǔ)。關(guān)于蛋白質(zhì)分類的大量有組織的潛在知識(shí)以獨(dú)立創(chuàng)建的蛋白質(zhì)分類數(shù)據(jù)庫的形式存在。通過在結(jié)構(gòu)分類數(shù)據(jù)庫的類別（例如SCOP [1]）和功能分類數(shù)據(jù)庫的類別（例如PROSITE [2]）之間創(chuàng)建概率圖，蛋白質(zhì)的結(jié)構(gòu)和功能可以概率相關(guān)。結(jié)果：盡管有獨(dú)立的分類方案，但我們證明PROSITE和SCOP具有明顯的語義重疊。通過使用PROSITE的類作為屬性來訓(xùn)練SCOP的分類器，反之亦然，SCOP和PROSITE的支持向量機(jī)分類器的準(zhǔn)確性得到了提高。新穎的屬性，二維彈性輪廓和塊用于改善時(shí)間復(fù)雜度和準(zhǔn)確性。使用決策樹在SCOP和PROSITE的類之間提取了許多關(guān)系。結(jié)論：我們證明了所提出的方法可以發(fā)現(xiàn)不同分類法類別之間的新概率關(guān)系，并提供了更準(zhǔn)確的分類?？梢詣?chuàng)建現(xiàn)有蛋白質(zhì)分類數(shù)據(jù)庫之間的廣泛映射，以鏈接大量組織的數(shù)據(jù)。在SCOP和PROSITE的類之間創(chuàng)建了概率圖，從而可以使用函數(shù)預(yù)測(cè)結(jié)構(gòu)，反之亦然。在我們的實(shí)驗(yàn)中，我們還發(fā)現(xiàn)，功能與結(jié)構(gòu)的關(guān)系確實(shí)比結(jié)構(gòu)與功能的關(guān)系更緊密。

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