Simulations of Sec61 with a substrate-selective inhibitor

Simulation inputs and outputs for manuscript "Signal peptide mimicry primes Sec61 for client-selective inhibition" by Rehan et al. Nature Chemical Biology 19, pages 1054–1062 (2023). DOI: 10.1038/s41589-023-01326-1. The Sec61 complex, embedded in a lipid bilayer mimicking ER in composition [1–4], was simulated in the presence ("Sec61_KZR8445", 5×1 µs) and absence ("Sec61_noinhibitor", 3×1 µs) of the cotransin KZR-8445 inhibitor. Additionally, a N300A mutant of Sec61α ("Sec61_KZR8445_N300A") was simulated in the presence of KZR-8445 for 1 µs. The replicas are labeled with "R". The GROMACS-compatible files include: Run input files (.tpr) Trajectory with coordinates written every 1 ns (.xtc) Energy file (.edr) Final coordinates after 1 µs of simulation (.gro) Continue points for extending the simulation (.cpt) Additionally, for each type of simulation (with KZR8445, without KZR8445, N300A mutation), common files are included: Index file (.ndx) Topology file (.top) The run parameter file (md.mdp) is common for all systems. The topologies (.itp) referred to by the top files are compressed into the TOP.tar archive. Additional details on the methodology used in the simulations is described below: We used the CHARMM36m protein force field [5,6], the CHARMM36 lipid force field [7], the CGenFF force field for the inhibitor with the ligand containing a positive dummy particle describing the bromobenzyl sigma hole [8,9], and CHARMM-specific TIP(S)3P model for water [10,11]. The systems were generated in CHARMM-GUI [12,13], including the protein positioning using PPM 2.0 [14] and the ligand parametrization within CHARMM-GUI [15]. The leap-frog integrator was used with a time step of 2 fs. Buffered Verlet lists were used [16]. The Lennard-Jones forces were switched to zero between 1.0 and a cut-off distance of 1.2 nm. Long-range electrostatic interactions were included by the smooth particle mesh Ewald algorithm [17,18]. Temperatures of the protein (including the inhibitor), the lipids, and the solvent (water and ions) were separately coupled to a Nosé–Hoover thermostat [19,20] with a target temperature of 310 K and a relaxation time of 1 ps. The pressure was maintained at 1 bar with a semi-isotropic Parrinello–Rahman barostat [21]. The target pressure was set to 1 bar, the compressibility to 4.5 × 10–5 bar–1 and the relaxation time constant 5 ps. Bonds involving hydrogens were constrained with p-LINCS [22,23]. [1] Bollen, I. C. & Higgins, J. A. Phospholipid asymmetry in rough- and smooth-endoplasmic-reticulum membranes of untreated and phenobarbital-treated rat liver. Biochem. J 189, 475–480 (1980). [2] Colbeau, A., Nachbaur, J. & Vignais, P. M. Enzymac characterization and lipid composition of rat liver subcellular membranes. Biochim. Biophys. Acta 249, 462–492 (1971). [3] Davison, S. C. & Wills, E. D. Studies on the lipid composition of the rat liver endoplasmic reticulum after induction with phenobarbitone and 20-methylcholanthrene. Biochem. J 140, 461–468 (1974). [4] Casares, D., Escribá, P. V. & Rosselló, C. A. Membrane Lipid Composition: Effect on Membrane and Organelle Structure, Function and Compartmentalization and Therapeutic Avenues. Int. J. Mol. Sci. 20, (2019). [5] Huang, J. & MacKerell, A. D., Jr. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013). [6] Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017). [7] Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010). [8] Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010). [9] Soteras Gutiérrez, I. et al. Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand-protein interactions. Bioorg. Med. Chem. 24, 4812–4825 (2016). [10] Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983). [11] Durell, S. R., Brooks, B. R. & Ben-Naim, A. Solvent-Induced Forces between Two Hydrophilic Groups. J. Phys. Chem. 98, 2198–2202 (1994). [12] Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008). [13] Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014). [14] Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–6 (2012). [15] Kim, S. et al. CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules. J. Comput. Chem. 38, 1879–1886 (2017). [16] Páll, S. & Hess, B. A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 184, 2641–2650 (2013). [17] Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993). [18] Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995). [19] Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984). [20] Hoover, W. G. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A Gen. Phys. 31, 1695–1697 (1985). [21] Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981). [22] Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 4, 116–122 (2008). [23] Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).本数据集对应Rehan等人发表于《Nature Chemical Biology》2023年第19卷第1054–1062页的论文"Signal peptide mimicry primes Sec61 for client-selective inhibition"，DOI: 10.1038/s41589-023-01326-1。 将嵌入组成上模拟内质网（ER）的脂质双分子层中的Sec61复合物（Sec61 complex）[1–4]，分别在存在cotransin类抑制剂KZR-8445（分组为Sec61_KZR8445，共5次1微秒模拟）以及不存在该抑制剂（分组为Sec61_noinhibitor，共3次1微秒模拟）的条件下进行分子动力学模拟。此外，针对Sec61α的N300A突变体（分组为Sec61_KZR8445_N300A），在添加KZR-8445的条件下进行了1次1微秒的模拟。所有模拟复本均以"R"进行标记。 适配GROMACS的文件包含以下类型：模拟运行输入文件（.tpr）、每1 ns保存一次坐标的轨迹文件（.xtc）、能量文件（.edr）、模拟结束后的最终坐标文件（.gro）、用于延长模拟的续算文件（.cpt）。 此外，针对每一类模拟体系（添加KZR8445组、无抑制剂组、N300A突变组），均包含通用文件：索引文件（.ndx）、拓扑文件（.top）。所有体系共用运行参数文件md.mdp。拓扑文件（.top）所引用的拓扑子文件（.itp）已打包压缩至TOP.tar归档文件中。 模拟所采用的方法学细节如下：本研究使用CHARMM36m蛋白质力场[5,6]、CHARMM36脂质力场[7]、适配该抑制剂的CGenFF力场（该力场为配体设置了正虚粒子以描述溴苄σ空穴[8,9]），以及针对水分子的CHARMM专属TIP(S)3P模型[10,11]。所有模拟体系均通过CHARMM-GUI平台构建[12,13]，包括使用PPM 2.0工具完成蛋白质的膜定位[14]，以及在CHARMM-GUI中完成配体的参数化[15]。模拟采用2 fs的时间步长，使用蛙跳（leap-frog）积分器，并采用缓冲Verlet列表算法[16]。范德华（Lennard-Jones）作用力在1.0 nm至1.2 nm的截断距离范围内被平滑切换至0。长程静电相互作用通过平滑粒子网格Ewald（smooth particle mesh Ewald）算法计算[17,18]。分别对蛋白质（含抑制剂）、脂质以及溶剂（水与离子）进行温度耦合，采用Nosé–Hoover恒温器[19,20]，目标温度设为310 K，弛豫时间为1 ps。体系压力通过半各向同性Parrinello–Rahman恒压器[21]维持在1 bar，目标压力设为1 bar，压缩率为4.5 × 10^–5 bar^–1，弛豫时间常数为5 ps。所有含氢化学键均通过p-LINCS算法进行约束[22,23]。 [1] Bollen, I. C. & Higgins, J. A. Phospholipid asymmetry in rough- and smooth-endoplasmic-reticulum membranes of untreated and phenobarbital-treated rat liver. Biochem. J 189, 475–480 (1980). [2] Colbeau, A., Nachbaur, J. & Vignais, P. M. Enzymac characterization and lipid composition of rat liver subcellular membranes. Biochim. Biophys. Acta 249, 462–492 (1971). [3] Davison, S. C. & Wills, E. D. Studies on the lipid composition of the rat liver endoplasmic reticulum after induction with phenobarbitone and 20-methylcholanthrene. Biochem. J 140, 461–468 (1974). [4] Casares, D., Escribá, P. V. & Rosselló, C. A. Membrane Lipid Composition: Effect on Membrane and Organelle Structure, Function and Compartmentalization and Therapeutic Avenues. Int. J. Mol. Sci. 20, (2019). [5] Huang, J. & MacKerell, A. D., Jr. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013). [6] Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2017). [7] Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010). [8] Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010). [9] Soteras Gutiérrez, I. et al. Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand-protein interactions. Bioorg. Med. Chem. 24, 4812–4825 (2016). [10] Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983). [11] Durell, S. R., Brooks, B. R. & Ben-Naim, A. Solvent-Induced Forces between Two Hydrophilic Groups. J. Phys. Chem. 98, 2198–2202 (1994). [12] Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008). [13] Wu, E. L. et al. CHARMM-GUI Membrane Builder toward realistic biological membrane simulations. J. Comput. Chem. 35, 1997–2004 (2014). [14] Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I. & Lomize, A. L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 40, D370–6 (2012). [15] Kim, S. et al. CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules. J. Comput. Chem. 38, 1879–1886 (2017). [16] Páll, S. & Hess, B. A flexible algorithm for calculating pair interactions on SIMD architectures. Comput. Phys. Commun. 184, 2641–2650 (2013). [17] Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993). [18] Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995). [19] Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984). [20] Hoover, W. G. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A Gen. Phys. 31, 1695–1697 (1985). [21] Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981). [22] Hess, B. P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 4, 116–122 (2008). [23] Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).