Ensemble molecular dynamics yields submillisecond kinetics and intermediates of membrane fusion

被引:125
作者
Kasson, Peter M.
Kelley, Nicholas W.
Singhal, Nina
Vrljic, Marija
Brunger, Axel T.
Pande, Vijay S. [1 ]
机构
[1] Stanford Univ, Biophys Program, Dept Comp Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Med Sci Training, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Cellular & Mol Physiol, Stanford, CA 94305 USA
[4] Stanford Univ, Dept Chem, Stanford, CA 94305 USA
[5] Stanford Univ, Stanford Synchrotron Radiat Lab, Stanford, CA 94305 USA
[6] Stanford Univ, Howard Hughes Med Inst, Stanford, CA 94305 USA
关键词
Markovian state models; lipid membrane; reaction mechanism; computer simulation; vesicle; INFLUENZA HEMAGGLUTININ; HEMIFUSION; MECHANISM; VESICLES; SIMULATIONS; VIRUS; MODEL; PORE; ENERGETICS; RELEASE;
D O I
10.1073/pnas.0601597103
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Lipid membrane fusion is critical to cellular transport and signaling processes such as constitutive secretion, neurotransmitter release, and infection by enveloped viruses. Here, we introduce a powerful computational methodology for simulating membrane fusion from a starting configuration designed to approximate activated prefusion assemblies from neuronal and viral fusion, producing results on a time scale and degree of mechanistic detail not previously possible to our knowledge. We use an approach to the long time scale simulation of fusion by constructing a Markovian state model with large-scale distributed computing, yielding an understanding of fusion mechanisms on time scales previously impossible to simulate to our knowledge. Our simulation data suggest a branched pathway for fusion, in which a common stalk-like intermediate can either rapidly form a fusion pore or remain in a metastable hemifused state that slowly forms fully fused vesicles. This branched reaction pathway provides a mechanistic explanation both for the biphasic fusion kinetics and the stable hemifused intermediates previously observed experimentally. Our distributed computing and Markovian state model approaches provide sufficient sampling to detect rare transitions, a systematic process for analyzing reaction pathways, and the ability to develop quantitative approximations of reaction kinetics for fusion.
引用
收藏
页码:11916 / 11921
页数:6
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