Multiscale network model for large protein dynamics

Hyoseon Jang, Sungsoo Na, Kilho Eom

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    Protein dynamics is essential for gaining insight into biological functions of proteins. Although protein dynamics is well delineated by molecular model, the molecular model is computationally prohibited for simulating large protein structures. In this work, we provide a multiscale network model (MNM) that allows the efficient computation on low-frequency normal modes related to structural deformation of proteins as well as dynamic behavior of functional sites. Specifically, MNM consists of two regions, one of which is described as a low-resolution structure, while the other is dictated by a high-resolution structure. The high-resolution regions using all alpha carbons of the protein are mainly binding site parts, which play a critical function in molecules, while the low-resolution parts are constructed from a further coarse-grained model (not using all alpha carbons). The feasibility of MNM to observe the cooperative motion of a protein structure was validated. It was shown that the MNM enables us to understand functional motion of proteins with computational efficiency.

    Original languageEnglish
    Article number245106
    JournalJournal of Chemical Physics
    Volume131
    Issue number24
    DOIs
    Publication statusPublished - 2009

    Bibliographical note

    Funding Information:
    This work was supported in part by Basic Research Program of the Korea Science and Engineering Foundation under Grant No. R01-2007-000-10497-0, National Research Foundation of Korea under Grant No. NRF:2009-0063170 (S.N.), and National Research Foundation of Korea under Grant No. 2009-0071246 (K.E.).

    ASJC Scopus subject areas

    • General Physics and Astronomy
    • Physical and Theoretical Chemistry

    Fingerprint

    Dive into the research topics of 'Multiscale network model for large protein dynamics'. Together they form a unique fingerprint.

    Cite this