Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles

Abstract

BackgroundDNA-binding proteins perform important roles in cellular processes and are involved in many biological activities. These proteins include crucial protein-DNA binding domains and can interact with single-stranded or double-stranded DNA, and accordingly classified as single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Computational prediction of SSBs and DSBs helps in annotating protein functions and understanding of protein-binding domains. ResultsPerformance is reported using the DNA-binding protein dataset that was recently introduced by Wang et al., [1]. The proposed method achieved a sensitivity of 0.600, specificity of 0.792, AUC of 0.758, MCC of 0.369, accuracy of 0.744, and F-measure of 0.536, on the independent test set. ConclusionThe proposed method with the hidden Markov model (HMM) profiles for feature extraction, outperformed the benchmark method in the literature and achieved an overall improvement of approximately 3%. The source code and supplementary information of the proposed method is available at https://github.com/roneshsharma/Predict-DNA-binding-proteins/wiki.