Ronesh Sharma1, Shiu Kumar2, Tatsuhiko Tsunoda3, Thirumananseri Kumarevel4, Alok Sharma5. 1. School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji. Electronic address: sharmaronesh@yahoo.com. 2. School of Electrical and Electronics Engineering, Fiji National University, Suva, Fiji. Electronic address: shiu748@gmail.com. 3. Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; Laboratory of Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, University of Tokyo, Tokyo, 113-0033, Japan. Electronic address: tsunoda@bs.s.u-tokyo.ac.jp. 4. Laboratory for Transcription Structural Biology, RIKEN Center for Biosystems Dynamics Research, 1-7-22 Suehiro, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan. Electronic address: kumarevel.thirumananseri@riken.jp. 5. Laboratory of Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan; Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University (TMDU), Tokyo, 113-8510, Japan; School of Engineering and Physics, The University of the South Pacific, Suva, Fiji; Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD, Australia. Electronic address: alok.fj@gmail.com.
Abstract
BACKGROUND: DNA-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. RESULTS: Performance 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. CONCLUSION: The 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.
BACKGROUND: DNA-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. RESULTS: Performance 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. CONCLUSION: The 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.