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Theory for trivial trajectory parallelization of multicanonical molecular dynamics and application to a polypeptide in water
Author(s) -
Ikebe Jinzen,
Umezawa Koji,
Kamiya Narutoshi,
Sugihara Takanori,
Yonezawa Yasushige,
Takano Yu,
Nakamura Haruki,
Higo Junichi
Publication year - 2011
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.21710
Subject(s) - trajectory , molecular dynamics , a priori and a posteriori , energy landscape , simple (philosophy) , computer science , space (punctuation) , statistical physics , energy (signal processing) , algorithm , mathematics , chemistry , computational chemistry , physics , thermodynamics , quantum mechanics , philosophy , epistemology , operating system
Trivial trajectory parallelization of multicanonical molecular dynamics (TTP‐McMD) explores the conformational space of a biological system with multiple short runs of McMD starting from various initial structures. This method simply connects (i.e., trivially parallelizes) the short trajectories and generates a long trajectory. First, we theoretically prove that the simple trajectory connection satisfies a detailed balance automatically. Thus, the resultant long trajectory is regarded as a single multicanonical trajectory. Second, we applied TTP‐McMD to an alanine decapeptide with an all‐atom model in explicit water to compute a free‐energy landscape. The theory imposes two requirements on the multiple trajectories. We have demonstrated that TTP‐McMD naturally satisfies the requirements. The TTP‐McMD produces the free‐energy landscape considerably faster than a single‐run McMD does. We quantitatively showed that the accuracy of the computed landscape increases with increasing the number of multiple runs. Generally, the free‐energy landscape of a large biological system is unknown a priori. The current method is suitable for conformational sampling of such a large system to reduce the waiting time to obtain a canonical ensemble statistically reliable. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011