PT  - JOURNAL ARTICLE
AU  - Amir Vajdi
AU  - Kourosh Zarringhalam
AU  - Nurit Haspel
TI  - Patch-DCA: Improved Protein Interface Prediction by utilizing Structural Information and Clustering DCA scores
AID  - 10.1101/656074
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 656074
4099  - http://biorxiv.org/content/early/2019/06/02/656074.short
4100  - http://biorxiv.org/content/early/2019/06/02/656074.full
AB  - Over the past decade there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein-protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein-protein inter-residue contacts remains relatively limited.In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein-protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70 % and 24 % and the precision by 80 % and 36 % in comparison with two state-of-the-art methods PSICOV and GREMLIN.