RMSD Protein Tertiary Structure Prediction with Soft Computing

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Mohammad Saber Iraji 1,* Hakimeh Ameri 2

1. Department of Computer Engineering and Information Technology, Payame Noor University, I.R. of Iran

2. Computer Engineering and Information Technology, Payame Noor University, I.R. of Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijmsc.2016.02.03

Received: 1 Jan. 2016 / Revised: 7 Feb. 2016 / Accepted: 3 Mar. 2016 / Published: 8 Apr. 2016

Index Terms

Root-mean-square-deviation (RMSD), protein, native structure, neural network, fuzzy


Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed approach to reduce predicted RMSD Error Than the actual amount for RMSD and calculate mean absolute error (MAE), through feed forward neural network, adaptive neuro fuzzy method. ANFIS is achieved better and more accurate results.

Cite This Paper

Mohammad Saber Iraji, Hakimeh Ameri,"RMSD Protein Tertiary Structure Prediction with Soft Computing", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.2, No.2, pp.24-33, 2016.DOI: 10.5815/ijmsc.2016.02.03


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