Protein Conformation Motion Modeling Using Sep-CMA-ES

Maxim Buzdalov, Sergey Knyazev, Yury Porozov

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


The problem of protein conformation motion modeling is an open problem in the structural computational biology. It is difficult to solve it using methods of molecular dynamics or quantum physics because these methods deal with time intervals of nanoseconds or microseconds, while conformation motions take time of millisecond order. In addition, these methods cannot take external forces into consideration. To deal with these problems, numerous approximated and coarse-grained methods are developed, which use ideas from geometry and motion planning. We present a new coarse-grained method of modeling the protein motion between two given conformations. The method is based on optimization of a cost function similar to the one in the Monge-Kantorovich mass transfer problem. The optimization is performed using sep-CMA-ES, which makes the running time of an iteration linear in the number of amino acids in a protein. The proposed method is compared with some of the existing methods on several molecules. It is shown that the results of the proposed method are more accurate than of the other methods.
Original languageEnglish
Title of host publicationICMLA '14
Subtitle of host publicationProceedings of the 2014 13th International Conference on Machine Learning and Applications
PublisherIEEE Press
Number of pages6
ISBN (Electronic)978-1-4799-7415-3
Publication statusPublished - 03 Dec 2014
Event2014 13th International Conference on Machine Learning and Applications (ICMLA) - Detroit, United States of America
Duration: 03 Dec 201406 Dec 2014


Conference2014 13th International Conference on Machine Learning and Applications (ICMLA)
Country/TerritoryUnited States of America
Period03 Dec 201406 Dec 2014


  • cma-es
  • protein conformation
  • conformation motion
  • mass transfer


Dive into the research topics of 'Protein Conformation Motion Modeling Using Sep-CMA-ES'. Together they form a unique fingerprint.

Cite this