Taksonomik çeşitlilik tabanlı protein altünite etkileşim tahmini

Protein altünite-altünite etkileşimlerinin (AAE) belirlenmesi, proteinlerin fonksiyonel ve yapısal rollerinin anlaşılmasında önemli bir adımdır. MirrorTree, etkileşen proteinlerin birlikte-evrimi prensibine dayanan, bir AAE tahmin yöntemidir. Ancak bu yöntem, AAE tahmin etmek için karşılaştırılan iki protein homolog kümesindeki taksonomik çeşitliliğe ve evrimsel açıklığa duyarlıdır. Bu çalışmada Taksonomik Çeşitliliğe Dayalı Protein Altünite Etkileşimi Tahmini (TAXDIP) olarak adlandırılan MirrorTree tabanlı yeni bir protein AAE tahmin yöntemi önermekteyiz. TAXDIP, iki protein homolog kümesini karşılaştırmadan önce, bunlarda daha yüksek düzeydeki taksonomik sıraların (ör. Tür yerine Aile) temsil edilmesini destekleyen bir örnekleme adımı ekleyerek, protein homolog kümeleri içindeki evrimsel kapsamın artmasını sağlar. TAXDIP öncelikle deneysel olarak doğrulanmış 6.514 pozitif (etkileşimli) altünite çiftini ve aynı sayıda, bilinen etkileşimleri olmayan, rastgele oluşturulmuş negatif (etkileşmeyen) altünite çiftini içeren bir küme kullanılarak değerlendirildi. TAXDIP bu kümede %71,0 duyarlılık ve %63,0 özgüllük elde etti. Daha sonra, TAXDIP'in performansının ME ve RDFF adlı AAE tahmin yöntemiyle karşılaştırılması için, 500 etkileşimli ve 500 etkileşmeyen altünite çiftini içeren, bir kıyaslama kümesi kullanıldı. TAXDIP RDFF’den daha iyi duyarlılık ve özgüllük gösterdi. TAXDIP’in duyarlılığı ME’den daha iyi olsa da, özgüllüğü ME’nin altında kaldı. Sonuç olarak, TAXDIP göstermiş olduğu performansla mevcut tahmin yöntemlerine uygun bir alternatiftir. Ayrıca, TAXDIP’in diğer tahmin yöntemleriyle örtüşen ve dahası onları tamamlayan doğru AAE tahminleri, onu birçok yöntemi bir araya getiren bir meta-AAE tahmin yönteminin parçası olma konusunda güçlü bir konuma getirmektedir.

Anahtar Kelimeler:

Protein altünite-altünite etkileşimleri, Protein birlikte evrimi, Protein fonksiyon analizi

Taxonomic diversity-based domain interaction prediction

Identification of protein domain-domain interactions (DDIs) is an essential step in understanding proteins’ functional and structural roles. MirrorTree is a DDI prediction method that is based on the principle of interacting proteins’ co-evolution. However, this method is sensitive to taxonomic diversity and evolutionary span within the two protein homolog sets compared to predict DDI. In this work, we propose a new MirrorTree-based DDI prediction method, namely Taxonomic Diversity-based Domain Interaction Prediction (TAXDIP). TAXDIP improves the MirrorTree method by adding a sampling step that favors representation of higher-level taxonomic ranks (e.g. family over species) in two protein homolog sets prior to their comparison. This additional step ensures increased evolutionary span within protein homolog sets. TAXDIP is first assessed using a set containing 6,514 positive (interacting) domain pairs and a negative (non-interacting) set of equal size containing randomly generated domain pairs with no known interactions. TAXDIP achieved 71.0% sensitivity and 63.0% specificity on this set. Next, a benchmark-set containing 500 interacting and 500 non-interacting domain pairs is used to compare the performance of TAXDIP against DDI prediction methods ME and RDFF. TAXDIP showed better sensitivity and specificity than RDFF. While TAXDIP’s sensitivity is better than ME, its specificity remained below ME. In conclusion, TAXDIP, with its performance, is a viable alternative to existing prediction methods. Furthermore, given TAXDIP’s true predictions are overlapping with, and furthermore, complementing other DDI prediction methods, TAXDIP has a strong position in becoming part of a meta-DDI prediction method that combines multiple methods to build a consensus prediction.

Keywords:

Protein domain-domain interactions, Protein co-evolution, Protein functional analysis,

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