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On the Use of Principal Component Analysis and Particle Swarm Optimization in Protein Tertiary Structure Prediction

  • Óscar Álvarez
  • Juan Luis Fernández-Martínez
  • Celia Fernández-Brillet
  • Ana Cernea
  • Zulima Fernández-Muñiz
  • Andrzej KloczkowskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

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.

Keywords

Principal component analysis Particle swarm optimization Tertiary protein structure Conformational sampling Protein structure refinement 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Óscar Álvarez
    • 1
  • Juan Luis Fernández-Martínez
    • 1
  • Celia Fernández-Brillet
    • 1
  • Ana Cernea
    • 1
  • Zulima Fernández-Muñiz
    • 1
  • Andrzej Kloczkowski
    • 2
    • 3
    Email author
  1. 1.Department of MathematicsUniversity of OviedoOviedoSpain
  2. 2.Battelle Center for Mathematical MedicineNationwide Children’s HospitalColumbusUSA
  3. 3.Department of PediatricsThe Ohio State UniversityColumbusUSA

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