title: Efficient prediction of protein conformational pathways based on the hybrid elastic network model.
abstract: Various computational models have gained immense attention by analyzing the dynamic characteristics of proteins. Several models have achieved recognition by fulfilling either theoretical or experimental predictions. Nonetheless, each method possesses limitations, mostly in computational outlay and physical reality. These limitations remind us that a new model or paradigm should advance theoretical principles to elucidate more precisely the biological functions of a protein and should increase computational efficiency. With these critical caveats, we have developed a new computational tool that satisfies both physical reality and computational efficiency. In the proposed hybrid elastic network model (HENM), a protein structure is represented as a mixture of rigid clusters and point masses that are connected with linear springs. Harmonic analyses based on the HENM have been performed to generate normal modes and conformational pathways. The results of the hybrid normal mode analyses give new physical insight to the 70S ribosome. The feasibility of the conformational pathways of hybrid elastic network interpolation (HENI) was quantitatively evaluated by comparing three different overlap values proposed in this paper. A remarkable observation is that the obtained mode shapes and conformational pathways are consistent with each other. Our timing results show that HENM has some advantage in computational efficiency over a coarse-grained model, especially for large proteins, even though it takes longer to construct the HENM. Consequently, the proposed HENM will be one of the best alternatives to the conventional coarse-grained ENMs and all-atom based methods (such as molecular dynamics) without loss of physical reality.
authors.affiliation: SKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
authors.forename: Sangjae
authors.initials: S
authors.lastname: Seo
authors.affiliation: Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA 01003, USA.
authors.forename: Yunho
authors.initials: Y
authors.lastname: Jang
authors.affiliation: School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
authors.forename: Pengfei
authors.initials: P
authors.lastname: Qian
authors.affiliation: Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA.
authors.forename: Wing Kam
authors.initials: WK
authors.lastname: Liu
authors.affiliation: SKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon 440-746, Republic of Korea; School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
authors.forename: Jae-Boong
authors.initials: JB
authors.lastname: Choi
authors.affiliation: School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea.
authors.forename: Byeong Soo
authors.initials: BS
authors.lastname: Lim
authors.affiliation: SKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon 440-746, Republic of Korea; School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, Republic of Korea. Electronic address: mkkim@me.skku.ac.kr.
authors.forename: Moon Ki
authors.initials: MK
authors.lastname: Kim
mesh_terms: D000465:Algorithms; D006801:Humans; D008958:Models, Molecular; D008962:Models, Theoretical; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D012270:Ribosomes / Q000737:chemistry
chemical_list: D011506:Proteins
keywords: Elastic network interpolation; Elastic network model; Normal mode analysis; Pathway generation; Protein dynamics