Abstract
The significance of determining the structure of proteins comes up from the importance of their role in the body, making proteins the primary target of drugs and key to developing new drugs. There are several experimental methods to determine PS ‘Protein structure’, but a reliable and direct prediction method to determine PS is not yet available. Hence, it was necessary to use intelligent systems that predicted PS based on the sequence of amino acids. Predicting the SSP “secondary structure of protein” is a very important step. It has increasingly become the basis for a number of ways to predict protein structure and thus know its function.
The aim of this research is to design and implement a high-performance method for predicting SSP from the AAS “amino acid sequence” and calculating prediction accuracy based on recorded results. To achieve this goal, the work was done in two phases: first, to construct an SSP system, based solely on the AAS for the protein using ANFIS “Adaptive Neuro-Fuzzy Inference Systems” This system achieves accuracy of up to 65% and is a good accuracy compared to many SSP systems.
The second stage was the construction of two SSP systems to study the effect of coding the data used on the system’s input on its accuracy. Both systems had same designs and structures using artificial neural network. The input in the second is coded using Profiles, which showed a significant improvement in system accuracy exceeded 10%, bringing the accuracy of total prediction up to 70.5%.
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Imanov, E., Shaheen, R. (2021). Soft Computing for Prediction of Secondary Structure of the Protein. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020 . ICAFS 2020. Advances in Intelligent Systems and Computing, vol 1306. Springer, Cham. https://doi.org/10.1007/978-3-030-64058-3_56
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