Abstract
We discuss applicability of Principal Component Analysis and Particle Swarm Optimization in protein tertiary structure prediction. The proposed algorithm is based on establishing a low-dimensional space where the sampling (and optimization) is carried out via Particle Swarm Optimizer (PSO). The reduced space is found via Principal Component Analysis (PCA) performed for a set of previously found low-energy protein models. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. Our results show that PSO improves the energy of the best decoy used in the PCA considering an adequate number of PCA terms.
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Acknowledgements
A. K. acknowledges financial support from NSF grant DBI 1661391 and from The Research Institute at Nationwide Children’s Hospital.
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Álvarez, Ó., Fernández-Martínez, J.L., Fernández-Brillet, C., Cernea, A., Fernández-Muñiz, Z., Kloczkowski, A. (2018). On the Use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_10
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