Partitioning of amino acid side chains into lipid bilayers: results from computer simulations and comparison to experiment.
Partitioning of amino acid side chains into lipid bilayers: results from computer simulations and comparison to experiment.
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DOI:
10.1085/jgp.200709745
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发表时间:
2007-05
期刊:
影响因子:
--
通讯作者:
Tieleman DP
中科院分区:
文献类型:
--
作者:
MacCallum JL;Bennett WF;Tieleman DP
The free energy of partitioning an amino acid side chain from water into the cell membrane is one of the critical parameters for understanding and predicting membrane protein stability, and understanding membrane protein function. Transmembrane segments are generally very hydrophobic, but may contain hydrophilic residues that are important for the structure or function of the protein. Experimental and theoretical studies have shown that the presence of polar residues, such as Asn, can lead to the formation of helical aggregates (Stockner et al., 2004; Tatko et al., 2006). The crystal structures of the voltage-gated potassium channels KvAP (Jiang et al., 2003a) and Kv1. 2 (Long et al., 2005) have caused vigorous debate in the ion channel community as some models proposed based on the crystal structures would have the arginine gating charges exposed to the lipid environment (Jiang et al., 2003b). After the publication of the KvAP crystal structure, it was argued that it was next to impossible to put an arginine in a lipid-exposed environment, and that the activation energy for such a model would be far too high to be realistic (Grabe et al., 2004). However, a recent experimental study has shown that the S4 segment of KvAP, which contains the gating charges, is able to insert into the membrane as a marginally stable transmembrane helix (Hessa et al., 2005b). We currently have a limited understanding of the partitioning behavior of amino acids into lipid bilayers. Numerous experimental scales have been derived using a variety of model systems and the results of such experiments have proven very useful in the prediction of membrane protein stability. The Perspectives from White, Wolfenden, and von Heijne in this issue outline several experimental scales and discuss their importance for understanding the membrane environment. Molecular dynamics (MD) computer simulations provide a complementary view of side chain partitioning, providing a level of detail that is not accessible to experiment. We have recently performed a systematic set of calculations (unpublished data) on the distributions of 17 of 20 amino acids (Pro, Gly, and His excluded). Here, we will compare the results of these simulations to several experimental scales.Computational Results We will focus on the results of our recent work. Following Wolfenden’s experimental studies (see Perspective in this issue), we simulated small molecule analogues of the amino acid side chains. The side chains were truncated at the β-carbon with the α-carbon replaced by a proton. For example, phenylalanine becomes toluene, and isoleucine becomes butane. For simplicity, we will refer to the compounds by the three-letter code of the corresponding amino acid. Simulations were performed on a system containing 64 lipid molecules, 2,804 water molecules, and two side chains. An umbrella sampling protocol was employed to determine the potential of mean force for the side chain in the lipid bilayer. A total of 37 simulations were performed for each residue, with each simulation having a minimum length of 30 ns, for a total of 1.1 μs per residue. For some residues, such as Trp and Arg, the simulations were extended up to 80 ns in order to improve the accuracy of the calculation. Based on the calculated free energy profiles we have determined two scales: one for the center of the membrane and one for the interfacial region, summarized in Table I. Fig. 1 A shows a partial density profile, indicating the location of various lipid functional groups; Fig. 1 B shows the potential of mean force (PMF) for the charged and neutral forms of Arg. The aliphatic residues (Ala, Val, Leu, Ile) partition favorably to both the …
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影响因子:
--
作者:
Aliste MP;Tieleman DP
通讯作者:
Tieleman DP
影响因子:
64.8
作者:
Jiang, YX;Ruta, V;MacKinnon, R
通讯作者:
MacKinnon, R
影响因子:
56.9
作者:
Long, SB;Campbell, EB;MacKinnon, R
通讯作者:
MacKinnon, R
影响因子:
3.4
作者:
Norman, Kristen E.;Nymeyer, Hugh
通讯作者:
Nymeyer, Hugh
DOI:
10.1073/pnas.0507618102
发表时间:
2005-10-18
影响因子:
11.1
作者:
Freites, JA;Tobias, DJ;White, SH
通讯作者:
White, SH