{"id":106990,"date":"2026-09-18T18:16:51","date_gmt":"2026-09-18T10:16:51","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/106990.html"},"modified":"2026-09-18T18:16:51","modified_gmt":"2026-09-18T10:16:51","slug":"amber%e5%88%86%e5%ad%90%e5%8a%a8%e5%8a%9b%e5%ad%a6%e6%a8%a1%e6%8b%9f16-amber%e8%bd%a8%e8%bf%b9%e5%88%86%e6%9e%90%e4%b8%8e%e4%bd%9c%e5%9b%be-1","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/106990.html","title":{"rendered":"Amber\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df16: Amber\u8f68\u8ff9\u5206\u6790\u4e0e\u4f5c\u56fe-1"},"content":{"rendered":"

\u672c\u6587\u6536\u5f55\u4e8e\u4e13\u680f\u00a0\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Amber\u00a0\u2014\u2014 \u4e13\u680f\u7cfb\u7edf\u8986\u76d6Amber \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u5168\u6d41\u7a0b，\u70b9\u51fb\u8ba2\u9605\u53ef\u8ddf\u8e2a\u540e\u7eed\u66f4\u65b0\u3002<\/p>\n

\u6458\u8981：\u672c\u6587\u7cfb\u7edf\u68b3\u7406 Amber \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u8f68\u8ff9\u7684\u5e38\u7528\u5206\u6790\u5de5\u5177\u4e0e\u5b8c\u6574\u6d41\u7a0b，\u6db5\u76d6 cpptraj\/ptraj\/VMD \u7684\u9009\u578b\u5bf9\u6bd4，\u4ee5\u53ca RMSD\u3001RMSF\u3001\u56de\u65cb\u534a\u5f84\u3001\u6c22\u952e\u3001\u8ddd\u79bb\/\u4e8c\u9762\u89d2\u3001PCA \u516d\u7c7b\u5e38\u89c1\u5206\u6790\u7684 cpptraj \u8f93\u5165\u6587\u4ef6\u6a21\u677f\u3002\u6587\u7ae0\u8fd8\u7ed9\u51fa\u4ece\u6570\u636e\u5bfc\u51fa\u5230\u8bba\u6587\u7ea7\u4f5c\u56fe\u7684\u56db\u79cd\u65b9\u5f0f（Matplotlib\u3001xmgrace\u3001VMD\u3001Jupyter+NGLview+MDAnalysis），\u5e76\u63d0\u4f9b\u4e00\u4e2a\u4ece\u5bf9\u9f50\u9aa8\u67b6\u5230 RMSD\/RMSF\/Rg\/\u5173\u952e\u6b8b\u57fa\u8ddd\u79bb\u51fa\u56fe\u7684\u5b8c\u6574\u6848\u4f8b\u6559\u7a0b，\u5e2e\u52a9\u8bfb\u8005\u8dd1\u5b8c\u6a21\u62df\u540e\u76f4\u63a5\u5957\u7528\u547d\u4ee4\u4e0e\u811a\u672c\u751f\u6210\u5206\u6790\u56fe\u3002<\/p>\n

\"\"<\/p>\n

\u4f60\u8dd1\u5b8c\u4e00\u6761 MD \u8f68\u8ff9，\u9762\u5bf9 md.nc \u51e0\u4e07\u4e2a\u5e27\u4e0d\u77e5\u9053\u4ece\u54ea\u4e0b\u624b：RMSD\u3001RMSF\u3001Rg\u3001\u6c22\u952e\u3001PCA \u5404\u4ee3\u8868\u4ec0\u4e48\u3001cpptraj \u547d\u4ee4\u600e\u4e48\u5199\u3001\u7b97\u5b8c\u7684 .dat \u53c8\u600e\u4e48\u753b\u6210\u8bba\u6587\u7ea7\u56fe？\u8fd9\u7bc7\u4e0a\u7bc7\u628a\u5e38\u7528\u5206\u6790\u5de5\u5177\u9009\u578b（cpptraj\/ptraj\/VMD）\u3001\u516d\u7c7b\u5e38\u89c1\u5206\u6790\u7684 cpptraj \u8f93\u5165\u6587\u4ef6\u6a21\u677f（rmsd.in\/rmsf.in\/rg.in\/hbond.in\/distance.in\/pca.in）\u3001\u4ee5\u53ca\u4e00\u4e2a\u4ece\u5bf9\u9f50\u9aa8\u67b6\u5230 RMSD\/RMSF\/Rg\/\u5173\u952e\u6b8b\u57fa\u8ddd\u79bb\u51fa\u56fe\u7684\u5b8c\u6574\u6848\u4f8b\u6559\u7a0b\u4e00\u6b21\u7ed9\u4f60\u3002\u4f60\u8dd1\u5b8c\u6a21\u62df\u540e\u7167\u7740\u6284\u547d\u4ee4\u3001\u6362 mask \u5c31\u80fd\u51fa\u81ea\u5df1\u7684\u5206\u6790\u56fe；\u53c2\u6570\u7ea7\u8be6\u89e3\u4e0e DSSP\/\u805a\u7c7b\u7b49\u8fdb\u9636\u5185\u5bb9\u89c1\u4e0b\u7bc7\u3002<\/p>\n

\u76f8\u5173\u6559\u7a0b\u4e0e\u6838\u5fc3\u6587\u732e<\/h3>\n\n\u6559\u7a0b\/\u6587\u6863\u4e0e\u672c\u6587\u5173\u7cfb<\/tr>\n\n\n\n\n
AMBER \u5b98\u65b9\u6559\u7a0b\u603b\u76ee\u5f55<\/td>\n\u672c\u6587\u547d\u4ee4\u6a21\u677f\u7684\u5b98\u65b9\u6e90\u5934，\u542b analysis \u7cfb\u5217\u6559\u7a0b<\/td>\n<\/tr>\n
AMBER Analysis Tutorial 1（RMSD \u5206\u6790）<\/td>\n\u4e0a\u7bc7 RMSD \u90e8\u5206\u7684\u5b98\u65b9\u5bf9\u7167\u5b9e\u64cd<\/td>\n<\/tr>\n
AMBER Hub<\/td>\ncpptraj \u5404\u7c7b\u5206\u6790\u7684\u793e\u533a\u6559\u7a0b\u5e93<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\u6838\u5fc3\u6587\u732e\u4e3a\u4ec0\u4e48\u503c\u5f97\u5148\u8bfb<\/tr>\n\n\n\n
CPPTRAJ: PTRAJ and CPPTRAJ（Roe & Cheatham, JCTC 2013）<\/td>\n\u4e0a\u7bc7\u6240\u6709\u547d\u4ee4\u6240\u5c5e\u5de5\u5177\u7684\u539f\u59cb\u8bba\u6587，\u529f\u80fd\u4e0e\u6027\u80fd\u6743\u5a01\u63cf\u8ff0<\/td>\n<\/tr>\n
ff19SB（Tian et al., JCTC 2019）<\/td>\n\u6848\u4f8b\u6240\u7528\u86cb\u767d\u529b\u573a\u7684\u51fa\u5904，\u5199\u8bba\u6587\u5f15\u7528\u529b\u573a\u65f6\u7528<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n

\u4e00\u3001Amber \u8f68\u8ff9\u5206\u6790\u5e38\u7528\u5de5\u5177<\/h3>\n

1. cpptraj（\u63a8\u8350）<\/h4>\n
    \n
  • \u81ea AmberTools 13（Amber14 \u65f6\u4ee3）\u8d77 cpptraj \u5c31\u662f\u9ed8\u8ba4\u8f68\u8ff9\u5206\u6790\u5de5\u5177（C++ \u91cd\u5199\u7248，\u6bd4 ptraj \u66f4\u5feb\u3001\u529f\u80fd\u66f4\u5f3a）<\/li>\n
  • \u652f\u6301\u811a\u672c\u5316\u3001\u6279\u5904\u7406\u3001\u591a\u7ebf\u7a0b<\/li>\n
  • \u53ef\u76f4\u63a5\u8f93\u51fa\u6570\u636e\u4f9b\u7ed8\u56fe<\/li>\n<\/ul>\n

    2. ptraj（\u65e7\u7248，\u5df2\u9010\u6e10\u6dd8\u6c70）<\/h4>\n
      \n
    • \u529f\u80fd\u7c7b\u4f3c cpptraj，\u4f46\u901f\u5ea6\u6162\u3001\u8bed\u6cd5\u7565\u4e0d\u540c<\/li>\n<\/ul>\n

      3. VMD + MMTK \/ NGLview（Jupyter）<\/h4>\n
        \n
      • \u7528\u4e8e\u53ef\u89c6\u5316\u8f68\u8ff9（\u975e\u5b9a\u91cf\u5206\u6790）<\/li>\n
      • \u53ef\u914d\u5408 Python \u811a\u672c\u63d0\u53d6\u6570\u636e<\/li>\n<\/ul>\n
        \n

        \u4e8c\u3001\u5e38\u89c1\u5206\u6790\u7c7b\u578b\u4e0e cpptraj \u547d\u4ee4\u793a\u4f8b<\/h3>\n

        \u5047\u8bbe\u4f60\u5df2\u6709：<\/p>\n

          \n
        • \u62d3\u6251\u6587\u4ef6：system.prmtop<\/li>\n
        • \u8f68\u8ff9\u6587\u4ef6：md.nc（NetCDF \u683c\u5f0f）\u6216 md.mdcrd<\/li>\n<\/ul>\n

          1. RMSD（\u5747\u65b9\u6839\u504f\u5dee）<\/h4>\n

          # rmsd.in
          \nparm system.prmtop
          \n trajin md.nc
          \n rms first :1-100&!@H= # \u5bf9\u6b8b\u57fa1-100\u7684\u91cd\u539f\u5b50\u8ba1\u7b97\u76f8\u5bf9\u4e8e\u7b2c\u4e00\u5e27\u7684RMSD
          \n rmsd out rmsd.dat first
          \n run <\/p>\n

          \u8fd0\u884c：<\/p>\n

          cpptraj -i rmsd.in <\/p>\n

          \u8f93\u51fa rmsd.dat，\u683c\u5f0f\u4e3a：\u5e27\u53f7 RMSD(\u00c5)<\/p>\n

          \n

          2. RMSF（\u5747\u65b9\u6839\u6da8\u843d）<\/h4>\n

          # rmsf.in
          \nparm system.prmtop
          \ntrajin md.nc
          \nrms first :1-100&!@H= # \u5148\u5bf9\u7ed3\u6784\u505a\u5bf9\u9f50
          \natomicfluct out rmsf.dat :1-100&!@H= byres
          \nrun <\/p>\n

          \u8f93\u51fa\u6bcf\u6b8b\u57fa\u7684 RMSF（\u00c5）<\/p>\n

          \n

          3. \u56de\u65cb\u534a\u5f84（Radius of Gyration, Rg）<\/h4>\n

          # rg.in
          \nparm system.prmtop
          \ntrajin md.nc
          \nradgyr out rg.dat :1-100
          \nrun <\/p>\n

          \n

          4. \u6c22\u952e\u5206\u6790<\/h4>\n

          # hbond.in
          \nparm system.prmtop
          \ntrajin md.nc
          \nhbond out hbonds.dat series
          \nrun <\/p>\n

          \u53ef\u8f93\u51fa\u6c22\u952e\u6570\u91cf\u968f\u65f6\u95f4\u53d8\u5316，\u6216\u5177\u4f53\u6c22\u952e\u5217\u8868<\/p>\n

          \n

          5. \u8ddd\u79bb\/\u4e8c\u9762\u89d2\/\u89d2\u5ea6<\/h4>\n

          # distance.in
          \nparm system.prmtop
          \ntrajin md.nc
          \ndistance d1 :10@CA :20@CA out dist_CA.dat
          \nrun <\/p>\n

          \n

          6. PCA（\u4e3b\u6210\u5206\u5206\u6790）<\/h4>\n

          # pca.in
          \nparm system.prmtop
          \ntrajin md.nc
          \nrms first :1-100&!@H=
          \nmatrix covar name mycovar :1-100&!@H=
          \nrun
          \nrunanalysis diagmatrix mycovar out evecs.dat vecs 3 name pc
          \nprojection modes pc beg 1 end 3 :1-100&!@H= out pca.dat
          \nrun <\/p>\n

          \u540e\u7eed\u53ef\u7528 projection \u6295\u5f71\u5230\u4e3b\u6210\u5206\u7a7a\u95f4<\/p>\n

          \n

          \u4e09\u3001\u4f5c\u56fe\u65b9\u5f0f<\/h3>\n

          cpptraj \u672c\u8eab\u4e0d\u7ed8\u56fe，\u9700\u5bfc\u51fa\u6570\u636e\u540e\u7528\u5176\u4ed6\u5de5\u5177\u7ed8\u56fe\u3002<\/p>\n

          \u65b9\u5f0f1：Python + Matplotlib（\u63a8\u8350）<\/h4>\n

          \u793a\u4f8b：\u7ed8\u5236 RMSD<\/h5>\n

          import matplotlib.pyplot as plt
          \nimport numpy as np
          \ndata = np.loadtxt('rmsd.dat', comments=['#', '@'])
          \ntime = data[:, 0] * 0.002 # \u5047\u8bbe\u6bcf\u5e27\u95f4\u96942 ps，\u5355\u4f4d ns
          \nrmsd = data[:, 1]
          \nplt.figure(figsize=(8, 5))
          \nplt.plot(time, rmsd, color='blue')
          \nplt.xlabel('Time (ns)')
          \nplt.ylabel('RMSD (\u00c5)')
          \nplt.title('Backbone RMSD over Time')
          \nplt.grid(True)
          \nplt.tight_layout()
          \nplt.savefig('rmsd.png', dpi=300)
          \nplt.show() <\/p>\n

          \u793a\u4f8b：RMSF（\u6309\u6b8b\u57fa）<\/h5>\n

          data = np.loadtxt('rmsf.dat', comments=['#', '@'])
          \nresidues = data[:, 0]
          \nrmsf = data[:, 1]
          \nplt.bar(residues, rmsf, width=1.0, color='lightcoral')
          \nplt.xlabel('Residue Number')
          \nplt.ylabel('RMSF (\u00c5)')
          \nplt.title('Per-residue RMSF')
          \nplt.savefig('rmsf.png', dpi=300) <\/p>\n

          \n

          \u65b9\u5f0f2：xmgrace（Grace）<\/h4>\n
            \n
          • \u9002\u7528\u4e8e\u5feb\u901f\u67e5\u770b .dat \u6587\u4ef6<\/li>\n
          • \u5728 Linux \u4e0b\u5b89\u88c5：sudo apt install grace<\/li>\n
          • \u547d\u4ee4：xmgrace rmsd.dat<\/li>\n<\/ul>\n

            \u53ef\u624b\u52a8\u8c03\u6574\u6837\u5f0f\u3001\u5bfc\u51fa EPS\/PNG<\/p>\n

            \n

            \u65b9\u5f0f3：VMD（\u53ef\u89c6\u5316+\u7b80\u5355\u5206\u6790）<\/h4>\n
              \n
            • \u6253\u5f00 VMD \u2192 Load system.prmtop \u548c md.nc<\/li>\n
            • \u4f7f\u7528 Extensions \u2192 Analysis \u4e2d\u7684\u5de5\u5177（\u5982 RMSD Trajectory Tool）<\/li>\n
            • \u53ef\u5bfc\u51fa\u6570\u636e，\u4f46\u4e0d\u5982 cpptraj \u7075\u6d3b<\/li>\n<\/ul>\n
              \n

              \u65b9\u5f0f4：Jupyter Notebook + NGLview + MDAnalysis（\u9ad8\u7ea7）<\/h4>\n

              \u9002\u5408\u4ea4\u4e92\u5f0f\u5206\u6790，\u4f46\u9700\u989d\u5916\u5b89\u88c5\u5e93：<\/p>\n

              pip install nglview mdanalysis matplotlib
              \nimport MDAnalysis as mda
              \nimport matplotlib.pyplot as plt
              \nfrom MDAnalysis.analysis import rms, rmsf
              \nu = mda.Universe('system.prmtop', 'md.nc')
              \nR = rms.RMSD(u, select="backbone")
              \nR.run()
              \nplt.plot(R.results.rmsd[:, 1], R.results.rmsd[:, 2]) <\/p>\n

              \n

              \u56db\u3001\u5b8c\u6574\u6848\u4f8b\u6559\u7a0b：\u86cb\u767d\u8d28 MD \u8f68\u8ff9\u5206\u6790\u4e0e\u7ed8\u56fe<\/h3>\n

              \u6b65\u9aa41：\u51c6\u5907\u6587\u4ef6<\/h4>\n

              protein.prmtop<\/p>\n

                \n
              • md.nc（100 ns，\u6bcf 10 ps \u4fdd\u5b58\u4e00\u5e27，\u5171 10,000 \u5e27）<\/li>\n<\/ul>\n

                \u6b65\u9aa42：\u7f16\u5199 cpptraj \u811a\u672c analysis.in<\/h4>\n

                parm protein.prmtop
                \ntrajin md.nc
                \n\u5bf9\u9f50\u9aa8\u67b6
                \nrms first :1-150&!@H=
                \nRMSD
                \nrmsd out rmsd.dat first :1-150&!@H=
                \nRMSF
                \natomicfluct out rmsf.dat :1-150&!@H= byres
                \nRg
                \nradgyr out rg.dat :1-150
                \n\u5173\u952e\u6b8b\u57fa\u8ddd\u79bb（\u5982\u6d3b\u6027\u4f4d\u70b9）
                \ndistance d1 :45@CA :120@CA out dist_45_120.dat
                \nrun <\/p>\n

                \u6b65\u9aa43：\u8fd0\u884c\u5206\u6790<\/h4>\n

                cpptraj -i analysis.in > analysis.log <\/p>\n

                \u6b65\u9aa44：Python \u7ed8\u56fe（plot_all.py）<\/h4>\n

                import numpy as np
                \nimport matplotlib.pyplot as plt
                \n\u8bbe\u7f6e\u5168\u5c40\u5b57\u4f53
                \nplt.rcParams.update({'font.size': 12})
                \nRMSD
                \nt, rmsd = np.loadtxt('rmsd.dat', unpack=True, comments=['#','@'])
                \nt_ns = t * 0.01 # \u5047\u8bbe\u6bcf\u5e27 10 ps
                \nplt.figure()
                \nplt.plot(t_ns, rmsd)
                \nplt.xlabel('Time (ns)'); plt.ylabel('RMSD (\u00c5)')
                \nplt.title('RMSD')
                \nplt.savefig('rmsd.png')
                \nRMSF
                \nres, rmsf = np.loadtxt('rmsf.dat', unpack=True, comments=['#','@'])
                \nplt.figure()
                \nplt.plot(res, rmsf, 'r-')
                \nplt.xlabel('Residue'); plt.ylabel('RMSF (\u00c5)')
                \nplt.title('RMSF')
                \nplt.savefig('rmsf.png')
                \nRg
                \nt, rg = np.loadtxt('rg.dat', unpack=True, comments=['#','@'])
                \nplt.figure()
                \nplt.plot(t*0.01, rg)
                \nplt.xlabel('Time (ns)'); plt.ylabel('Rg (\u00c5)')
                \nplt.title('Radius of Gyration')
                \nplt.savefig('rg.png')
                \nDistance
                \nt, d = np.loadtxt('dist_45_120.dat', unpack=True, comments=['#','@'])
                \nplt.figure()
                \nplt.plot(t*0.01, d)
                \nplt.xlabel('Time (ns)'); plt.ylabel('Distance (\u00c5)')
                \nplt.title('Distance between Res 45 and 120 CA')
                \nplt.savefig('dist_45_120.png') <\/p>\n

                \u8fd0\u884c：<\/p>\n

                python plot_all.py <\/p>\n

                \n

                \u4e94\u3001\u8fdb\u9636\u5efa\u8bae<\/h3>\n
              • \u8f68\u8ff9\u9884\u5904\u7406：\u4f7f\u7528 strip \u53bb\u9664\u6c34\/\u79bb\u5b50，\u51cf\u5c0f\u6587\u4ef6\u4f53\u79ef<\/li>\n

                parm system.prmtop
                \ntrajin md.nc
                \nstrip :WAT,Na+,Cl-
                \ntrajout md_noWat.nc netcdf
                \nrun <\/p>\n

              • \u591a\u91cd\u590d\u5b9e\u9a8c：\u5bf9\u591a\u4e2a\u8f68\u8ff9\u5206\u522b\u5206\u6790，\u53d6\u5e73\u5747\u00b1\u6807\u51c6\u5dee<\/li>\n
              • \u81ea\u7531\u80fd\u666f\u89c2：\u7ed3\u5408 PCA \u4e0e\u4e8c\u7ef4\u76f4\u65b9\u56fe（np.histogram2d）<\/li>\n
              • \u81ea\u52a8\u5316\u811a\u672c：\u7528 Bash\/Python \u6279\u91cf\u5904\u7406\u591a\u4e2a\u4f53\u7cfb<\/li>\n
                \n

                \u516d\u3001cpptraj \u6279\u5904\u7406\u4e0e\u4ea4\u4e92\u6a21\u5f0f<\/h3>\n

                1. CPPTRAJ – Amber\u5b98\u65b9\u8f68\u8ff9\u5206\u6790\u5de5\u5177<\/h4>\n

                CPPTRAJ\u662fAmberTools\u4e2d\u5904\u7406\u5750\u6807\u8f68\u8ff9\u548c\u6570\u636e\u6587\u4ef6\u7684\u4e3b\u8981\u7a0b\u5e8f，\u652f\u6301\u6279\u91cf\u5904\u7406\u548c\u4ea4\u4e92\u5f0f\u4e24\u79cd\u6a21\u5f0f\u3002<\/p>\n

                \u542f\u52a8\u65b9\u5f0f：<\/p>\n

                bash<\/p>\n

                # \u6279\u91cf\u6a21\u5f0f（\u63a8\u8350\u7528\u4e8e\u751f\u4ea7\u5206\u6790）
                \ncpptraj -p topology.prmtop -i analysis.in
                \n\u4ea4\u4e92\u6a21\u5f0f（\u9002\u5408\u8c03\u8bd5\u548c\u6d4b\u8bd5）
                \ncpptraj -p topology.prmtop
                \ncpptraj> [\u547d\u4ee4] <\/p>\n

                \n

                \u4e03\u3001RMSD\/RMSF \u7684\u6279\u5904\u7406\u5199\u6cd5\u4e0e\u7f8e\u5316\u56fe<\/h3>\n

                1. RMSD（\u5747\u65b9\u6839\u504f\u5dee）\u5206\u6790<\/h4>\n

                \u8861\u91cf\u7ed3\u6784\u76f8\u5bf9\u4e8e\u53c2\u8003\u7ed3\u6784\u7684\u504f\u79bb\u7a0b\u5ea6，\u8bc4\u4f30\u6a21\u62df\u7a33\u5b9a\u6027\u3002<\/p>\n

                CPPTRAJ\u8f93\u5165\u6587\u4ef6 (rmsd.in)：<\/p>\n

                bash<\/p>\n

                # \u52a0\u8f7d\u62d3\u6251\u548c\u8f68\u8ff9
                \nparm protein.prmtop
                \ntrajin production.nc
                \n\u81ea\u52a8\u955c\u50cf\u5904\u7406（\u5468\u671f\u6027\u8fb9\u754c\u6761\u4ef6）
                \nautoimage
                \n\u8ba1\u7b97\u9aa8\u67b6\u539f\u5b50RMSD（\u4ee5\u7b2c\u4e00\u5e27\u4e3a\u53c2\u8003）
                \nrmsd rmsd_backbone :1-300@CA,C,N out rmsd_backbone.dat time 0.1
                \n\u8ba1\u7b97\u5168\u539f\u5b50RMSD（\u4ee5\u6676\u4f53\u7ed3\u6784\u4e3a\u53c2\u8003）
                \nreference crystal.pdb [crystal]
                \nrmsd rmsd_all :* reference [crystal] out rmsd_all.dat time 0.1
                \n\u62df\u5408\u5e76\u8f93\u51fa\u53e0\u52a0\u540e\u7684\u8f68\u8ff9（\u53ef\u9009）
                \ntrajout fitted.nc netcdf <\/p>\n

                Python\u4f5c\u56fe (Matplotlib)：<\/p>\n

                Python<\/p>\n

                import pandas as pd
                \nimport matplotlib.pyplot as plt
                \nimport numpy as np
                \n\u8bfb\u53d6CPPTRAJ\u8f93\u51fa
                \ndata = pd.read_csv('rmsd_backbone.dat', delim_whitespace=True,
                \ncomment='#', names=['Frame', 'Time', 'RMSD'])
                \nfig, ax = plt.subplots(figsize=(10, 6))
                \n\u7ed8\u5236RMSD\u66f2\u7ebf
                \nax.plot(data['Time'], data['RMSD'], color='#2E86AB', linewidth=1.5, alpha=0.8)
                \n\u6dfb\u52a0\u79fb\u52a8\u5e73\u5747（\u5e73\u6ed1\u66f2\u7ebf）
                \nwindow = 50
                \nma = data['RMSD'].rolling(window=window).mean()
                \nax.plot(data['Time'], ma, color='#A23B72', linewidth=2.5, label=f'{window}\u5e27\u79fb\u52a8\u5e73\u5747')
                \n\u6807\u6ce8\u5173\u952e\u533a\u57df
                \nax.axhline(y=data['RMSD'].mean(), color='gray', linestyle='–', alpha=0.5, label=f'\u5e73\u5747\u503c: {data["RMSD"].mean():.2f} \u00c5')
                \nax.fill_between(data['Time'], data['RMSD'].mean() – data['RMSD'].std(),
                \ndata['RMSD'].mean() + data['RMSD'].std(), alpha=0.2, color='gray')
                \nax.set_xlabel('Time (ns)', fontsize=12)
                \nax.set_ylabel('RMSD (\u00c5)', fontsize=12)
                \nax.set_title('Backbone RMSD vs Time', fontsize=14, fontweight='bold')
                \nax.legend()
                \nax.grid(True, alpha=0.3)
                \nplt.tight_layout()
                \nplt.savefig('rmsd_analysis.png', dpi=300, bbox_inches='tight')
                \nplt.show() <\/p>\n

                \n

                2. RMSF（\u5747\u65b9\u6839\u6da8\u843d）\u5206\u6790<\/h4>\n

                \u8bc6\u522b\u86cb\u767d\u8d28\u67d4\u6027\u533a\u57df，\u5e38\u7528\u4e8eB\u56e0\u5b50\u8ba1\u7b97\u3002<\/p>\n

                CPPTRAJ\u8f93\u5165\u6587\u4ef6 (rmsf.in)：<\/p>\n

                bash<\/p>\n

                parm protein.prmtop
                \ntrajin production.nc
                \nautoimage
                \nrms reference [average] :1-300@CA,C,N
                \n\u6309\u6b8b\u57fa\u8ba1\u7b97RMSF（\u9700\u5148\u8ba1\u7b97\u5e73\u5747\u7ed3\u6784）
                \naverage crdset MyAverage
                \nrun
                \nrms ref MyAverage :1-300@CA,C,N
                \natomicfluct out rmsf_byres.dat :1-300@CA byres
                \n\u6216\u8ba1\u7b97B\u56e0\u5b50（\u4e0e\u6676\u4f53\u5b66B\u56e0\u5b50\u6bd4\u8f83）
                \natomicfluct out bfactor.dat :1-300@CA byres bfactor <\/p>\n

                Python\u4f5c\u56fe（\u70ed\u529b\u56fe+\u67f1\u72b6\u56fe）：<\/p>\n

                Python<\/p>\n

                import matplotlib.pyplot as plt
                \nimport seaborn as sns
                \n\u8bfb\u53d6RMSF\u6570\u636e
                \nrmsf_data = pd.read_csv('rmsf_byres.dat', delim_whitespace=True,
                \ncomment='#', names=['Residue', 'RMSF'])
                \nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), height_ratios=[3, 1])
                \n\u4e0a\u56fe：RMSF\u66f2\u7ebf
                \ncolors = plt.cm.RdYlBu_r(rmsf_data['RMSF'] \/ rmsf_data['RMSF'].max())
                \nax1.bar(rmsf_data['Residue'], rmsf_data['RMSF'], color=colors, width=0.8, edgecolor='none')
                \nax1.plot(rmsf_data['Residue'], rmsf_data['RMSF'], color='black', linewidth=1, alpha=0.5)
                \n\u6807\u6ce8\u9ad8\u67d4\u6027\u533a\u57df（\u5982loop\u533a）
                \nthreshold = rmsf_data['RMSF'].quantile(0.9)
                \nhigh_flex = rmsf_data[rmsf_data['RMSF'] > threshold]
                \nax1.scatter(high_flex['Residue'], high_flex['RMSF'], color='red', s=50, zorder=5, label=f'\u9ad8\u67d4\u6027\u533a\u57df (>90%\u5206\u4f4d\u6570)')
                \nax1.set_ylabel('RMSF (\u00c5)', fontsize=12)
                \nax1.set_title('Per-Residue Root Mean Square Fluctuation', fontsize=14, fontweight='bold')
                \nax1.legend()
                \nax1.grid(True, alpha=0.3, axis='y')
                \n\u4e0b\u56fe：\u4e8c\u7ea7\u7ed3\u6784\u793a\u610f\u56fe（\u793a\u4f8b）
                \n\u5b9e\u9645\u5e94\u7528\u4e2d\u53ef\u4eceDSSP\u5206\u6790\u5bfc\u5165
                \nss_data = np.random.choice([0, 1, 2], size=len(rmsf_data)) # 0=coil, 1=helix, 2=sheet
                \nss_colors = {0: '#FFFFFF', 1: '#FF6B6B', 2: '#4ECDC4'}
                \nfor i, ss in enumerate(ss_data):
                \nax2.barh(0, 1, left=i, color=ss_colors[ss], height=0.5, edgecolor='black', linewidth=0.5)
                \nax2.set_xlim(0, len(rmsf_data))
                \nax2.set_ylim(-0.5, 0.5)
                \nax2.set_xlabel('Residue Number', fontsize=12)
                \nax2.set_yticks([])
                \nax2.set_title('Secondary Structure (Red=Helix, Cyan=Sheet, White=Coil)', fontsize=10)
                \nplt.tight_layout()
                \nplt.savefig('rmsf_analysis.png', dpi=300, bbox_inches='tight') <\/p>\n

                \n

                \u516b\u3001\u6c22\u952e\u7f51\u7edc\u4e0e PCA \u8fdb\u9636<\/h3>\n

                3. \u6c22\u952e\u5206\u6790<\/h4>\n

                \u5206\u6790\u86cb\u767d\u8d28\u5185\u90e8\u6216\u86cb\u767d-\u914d\u4f53\u95f4\u7684\u6c22\u952e\u7f51\u7edc\u3002<\/p>\n

                CPPTRAJ\u8f93\u5165\u6587\u4ef6 (hbond.in)：<\/p>\n

                bash<\/p>\n

                parm complex.prmtop
                \ntrajin production.nc
                \nautoimage
                \n\u86cb\u767d\u8d28\u5185\u90e8\u6c22\u952e
                \nhbond hb_protein :1-300 out hbond_protein.dat avgout hbond_avg_protein.dat
                \nseries uuseries hbonds_uu.gnu
                \n\u86cb\u767d-\u914d\u4f53\u6c22\u952e（\u5047\u8bbe\u914d\u4f53\u4e3a\u6b8b\u57fa301）
                \nhbond hb_ligand :1-300,301 out hbond_ligand.dat avgout hbond_avg_ligand.dat
                \nseries uuseries hbonds_lig.gnu
                \n\u6eb6\u5242\u6865\u63a5\u6c22\u952e
                \nhbond hb_solvent :1-300 solventdonor :WAT solventacceptor :WAT@O
                \nout hbond_solvent.dat avgout hbond_avg_solvent.dat <\/p>\n

                Python\u4f5c\u56fe（\u6c22\u952e\u65f6\u95f4\u5e8f\u5217）：<\/p>\n

                Python<\/p>\n

                # \u8bfb\u53d6\u6c22\u952e\u5e8f\u5217\u6570\u636e
                \nhb_data = pd.read_csv('hbond_protein.dat', delim_whitespace=True, comment='#')
                \nfig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
                \n\u4e0a\u56fe：\u6c22\u952e\u6570\u91cf\u968f\u65f6\u95f4\u53d8\u5316
                \ntotal_hb = hb_data.iloc[:, 1:].sum(axis=1) # \u5047\u8bbe\u7b2c\u4e00\u5217\u662f\u65f6\u95f4
                \nax1.plot(hb_data.iloc[:, 0], total_hb, color='#2E86AB', linewidth=1.5)
                \nax1.set_ylabel('Number of H-bonds', fontsize=12)
                \nax1.set_title('Total Hydrogen Bonds Over Time', fontsize=14)
                \nax1.grid(True, alpha=0.3)
                \n\u4e0b\u56fe：\u7279\u5b9a\u6c22\u952e\u5b58\u5728\u77e9\u9635（\u70ed\u529b\u56fe）
                \n\u9009\u62e9\u51fa\u73b0\u9891\u7387\u6700\u9ad8\u768410\u4e2a\u6c22\u952e
                \nhb_freq = hb_data.iloc[:, 1:].mean().sort_values(ascending=False).head(10)
                \ntop_hb = hb_data[hb_freq.index]
                \nim = ax2.imshow(top_hb.T, aspect='auto', cmap='Blues', interpolation='nearest')
                \nax2.set_xlabel('Frame', fontsize=12)
                \nax2.set_ylabel('H-bond ID', fontsize=12)
                \nax2.set_title('Top 10 Most Frequent H-bonds Presence Matrix', fontsize=14)
                \nplt.colorbar(im, ax=ax2, label='Present (1) \/ Absent (0)')
                \nplt.tight_layout()
                \nplt.savefig('hbond_analysis.png', dpi=300) <\/p>\n

                \n

                4. \u4e3b\u6210\u5206\u5206\u6790 (PCA)<\/h4>\n

                \u63d0\u53d6\u6784\u8c61\u53d8\u5316\u7684\u4e3b\u8981\u6a21\u5f0f\u3002<\/p>\n

                CPPTRAJ\u8f93\u5165\u6587\u4ef6 (pca.in)：<\/p>\n

                bash<\/p>\n

                parm protein.prmtop
                \ntrajin production.nc
                \nautoimage
                \nrms reference :1-300@CA,C,N
                \n\u534f\u65b9\u5dee\u77e9\u9635\u8ba1\u7b97
                \nmatrix covar name covmat :1-300@CA
                \nrun
                \n\u5bf9\u89d2\u5316（runanalysis \u8ba9\u5b83\u5728\u89e3\u6790\u9636\u6bb5\u7acb\u5373\u6267\u884c）
                \nrunanalysis diagmatrix covmat out evecs.dat vecs 10 name pc
                \n\u6295\u5f71\u5230PC\u7a7a\u95f4（modes \u63a5\u6570\u636e\u96c6\u540d，\u4e0d\u662f\u6587\u4ef6\u540d）
                \nprojection proj out projections.dat modes pc beg 1 end 3 :1-300@CA
                \nrun <\/p>\n

                Python\u4f5c\u56fe（PCA\u81ea\u7531\u80fd\u666f\u89c2）：<\/p>\n

                Python<\/p>\n

                from scipy.stats import gaussian_kde
                \nproj = pd.read_csv('projections.dat', delim_whitespace=True, comment='#',
                \nnames=['Frame', 'PC1', 'PC2', 'PC3'])
                \nfig, axes = plt.subplots(1, 3, figsize=(18, 5))
                \nPC1 vs PC2 \u81ea\u7531\u80fd\u56fe
                \nxy = np.vstack([proj['PC1'], proj['PC2']])
                \nkde = gaussian_kde(xy)
                \nxmin, xmax = proj['PC1'].min(), proj['PC1'].max()
                \nymin, ymax = proj['PC2'].min(), proj['PC2'].max()
                \nX, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
                \npositions = np.vstack([X.ravel(), Y.ravel()])
                \nZ = np.reshape(kde(positions).T, X.shape)
                \nZ = -np.log(Z + 1e-10) # \u8f6c\u6362\u4e3a\u81ea\u7531\u80fd (kT)
                \nim1 = axes[0].contourf(X, Y, Z, levels=20, cmap='viridis')
                \naxes[0].scatter(proj['PC1'], proj['PC2'], c='white', s=1, alpha=0.3)
                \naxes[0].set_xlabel('PC1', fontsize=12)
                \naxes[0].set_ylabel('PC2', fontsize=12)
                \naxes[0].set_title('Free Energy Landscape (PC1 vs PC2)', fontsize=14)
                \nplt.colorbar(im1, ax=axes[0], label='Free Energy (kT)')
                \nPC1\u65f6\u95f4\u5e8f\u5217
                \naxes[1].plot(proj['Frame'], proj['PC1'], color='#E63946', linewidth=1)
                \naxes[1].set_xlabel('Frame', fontsize=12)
                \naxes[1].set_ylabel('PC1', fontsize=12)
                \naxes[1].set_title('PC1 Projection Over Time', fontsize=14)
                \naxes[1].grid(True, alpha=0.3)
                \n\u7279\u5f81\u503c\u5206\u5e03（\u65b9\u5dee\u89e3\u91ca\u7387）
                \neigvals = pd.read_csv('evecs.dat', skiprows=1, nrows=10, delim_whitespace=True, header=None)
                \naxes[2].bar(range(1, 11), eigvals[0], color='#457B9D')
                \naxes[2].set_xlabel('Principal Component', fontsize=12)
                \naxes[2].set_ylabel('Eigenvalue', fontsize=12)
                \naxes[2].set_title('Variance Explained by PCs', fontsize=14)
                \nplt.tight_layout()
                \nplt.savefig('pca_analysis.png', dpi=300) <\/p>\n

                \n

                \u4e5d\u3001\u80fd\u91cf\u7ec4\u5206\u63d0\u53d6\u4e0e\u591a\u9762\u677f\u56fe<\/h3>\n

                5. \u80fd\u91cf\u7ec4\u5206\u63d0\u53d6\u4e0e\u4f5c\u56fe<\/h4>\n

                \u63d0\u53d6\u80fd\u91cf（\u4f7f\u7528process_mdout.perl\u6216cpptraj）：<\/p>\n

                bash<\/p>\n

                # \u4f7f\u7528Amber\u5de5\u5177\u63d0\u53d6
                \nprocess_mdout.perl production.out
                \n\u6216\u4f7f\u7528cpptraj
                \ncpptraj -p topology.prmtop << EOF
                \nreaddata production.out
                \nwritedata energy.dat production.out[Etot] production.out[TEMP] production.out[PRESS] production.out[VOLUME] time 0.1
                \nEOF <\/p>\n

                Python\u591a\u9762\u677f\u80fd\u91cf\u56fe：<\/p>\n

                Python<\/p>\n

                energy = pd.read_csv('energy.dat', delim_whitespace=True,
                \n names=['Time', 'Etot', 'Temp', 'Press', 'Volume'])
                \nfig, axes = plt.subplots(2, 2, figsize=(14, 10))
                \n\u603b\u80fd\u91cf
                \naxes[0,0].plot(energy['Time'], energy['Etot']\/1000, color='#264653', linewidth=1)
                \naxes[0,0].set_ylabel('Total Energy (10\u00b3 kcal\/mol)', fontsize=11)
                \naxes[0,0].set_title('Total Energy', fontsize=12, fontweight='bold')
                \naxes[0,0].grid(True, alpha=0.3)
                \n\u6e29\u5ea6
                \naxes[0,1].plot(energy['Time'], energy['Temp'], color='#E76F51', linewidth=1)
                \naxes[0,1].axhline(y=300, color='gray', linestyle='–', label='Target (300K)')
                \naxes[0,1].set_ylabel('Temperature (K)', fontsize=11)
                \naxes[0,1].set_title('System Temperature', fontsize=12, fontweight='bold')
                \naxes[0,1].legend()
                \naxes[0,1].grid(True, alpha=0.3)
                \n\u538b\u529b
                \naxes[1,0].plot(energy['Time'], energy['Press'], color='#2A9D8F', linewidth=1, alpha=0.7)
                \n\u6dfb\u52a0\u79fb\u52a8\u5e73\u5747
                \npress_ma = energy['Press'].rolling(window=100).mean()
                \naxes[1,0].plot(energy['Time'], press_ma, color='#264653', linewidth=2, label='100-pt MA')
                \naxes[1,0].set_ylabel('Pressure (bar)', fontsize=11)
                \naxes[1,0].set_xlabel('Time (ps)', fontsize=11)
                \naxes[1,0].set_title('System Pressure', fontsize=12, fontweight='bold')
                \naxes[1,0].legend()
                \naxes[1,0].grid(True, alpha=0.3)
                \n\u5bc6\u5ea6（\u4ece\u4f53\u79ef\u8ba1\u7b97，\u5047\u8bbe\u5df2\u77e5\u8d28\u91cf\u548c\u76d2\u5b50\u8f6c\u6362）
                \n\u793a\u4f8b：\u7b80\u5355\u4f53\u79ef\u56fe
                \naxes[1,1].plot(energy['Time'], energy['Volume']\/1000, color='#F4A261', linewidth=1)
                \naxes[1,1].set_ylabel('Volume (10\u00b3 \u00c5\u00b3)', fontsize=11)
                \naxes[1,1].set_xlabel('Time (ps)', fontsize=11)
                \naxes[1,1].set_title('System Volume', fontsize=12, fontweight='bold')
                \naxes[1,1].grid(True, alpha=0.3)
                \nplt.suptitle('Molecular Dynamics Equilibration Monitoring', fontsize=16, fontweight='bold', y=1.02)
                \nplt.tight_layout()
                \nplt.savefig('energy_components.png', dpi=300, bbox_inches='tight') <\/p>\n

                \n

                \u5341\u30013D \u53ef\u89c6\u5316：VMD \u4e0e PyMOL<\/h3>\n

                6. VMD\u53ef\u89c6\u5316\u811a\u672c<\/h4>\n

                \u751f\u6210VMD\u53ef\u89c6\u5316\u72b6\u6001\u7684Tcl\u811a\u672c：<\/p>\n

                tcl<\/p>\n

                # save_visualization.vmd
                \n# \u52a0\u8f7d\u7ed3\u6784
                \nmol new protein.prmtop type parm7
                \nmol addfile production.nc type netcdf first 0 last -1 step 10 waitfor all
                \n\u663e\u793a\u8bbe\u7f6e
                \nmol delrep 0 top
                \nmol representation NewCartoon 0.3 10.0 4.1 0
                \nmol color ColorID 0
                \nmol selection {protein}
                \nmol addrep top
                \n\u6309RMSF\u7740\u8272（\u9700\u5148\u5bfc\u5165RMSF\u6570\u636e）
                \nmol representation NewCartoon 0.3 10.0 4.1 0
                \nmol color Beta
                \nmol selection {protein}
                \nmol addrep top
                \n\u8bbe\u7f6e\u989c\u8272\u8303\u56f4（\u4f4eRMSF=\u84dd，\u9ad8RMSF=\u7ea2）
                \nmol scaleminmax top 1 0.5 3.0
                \n\u914d\u4f53\u663e\u793a（\u5982\u679c\u6709）
                \nmol representation Licorice 0.3 12.0 12.0
                \nmol color Name
                \nmol selection {resname LIG}
                \nmol addrep top
                \n\u4fdd\u5b58\u56fe\u7247
                \nrender TachyonInternal rmsf_colored.png <\/p>\n

                \u547d\u4ee4\u884c\u6e32\u67d3：<\/p>\n

                bash<\/p>\n

                vmd -e save_visualization.vmd -dispdev text<\/p>\n

                \n

                7. PyMOL\u9ad8\u7ea7\u53ef\u89c6\u5316<\/h4>\n

                PyMOL\u811a\u672c (visualize.pml)：<\/p>\n

                Python<\/p>\n

                # \u52a0\u8f7d\u7ed3\u6784（PyMOL \u4e0d\u8ba4 prmtop，\u5148\u5728 cpptraj \u91cc trajout \u5bfc\u51fa PDB \u518d\u52a0\u8f7d）
                \nload protein.pdb, complex
                \nload_traj production.nc, complex
                \n\u8bbe\u7f6e\u6837\u5f0f
                \nas cartoon
                \ncolor marine, complex
                \n\u6309B\u56e0\u5b50（RMSF）\u7740\u8272
                \nspectrum b, blue_white_red, minimum=0.5, maximum=3.0
                \n\u663e\u793a\u914d\u4f53
                \nshow sticks, resn LIG
                \ncolor atomic, resn LIG
                \n\u8bbe\u7f6e\u89c6\u89d2
                \nset_view (
                \n0.5, 0.3, 0.8,
                \n-0.2, 0.9, -0.4,
                \n-0.8, 0.3, 0.5,
                \n0.0, 0.0, -50.0)
                \n\u4fdd\u5b58\u9ad8\u8d28\u91cf\u56fe\u7247
                \nset ray_trace_mode, 1
                \nset ray_shadows, off
                \nbg_color white
                \nray 2400, 2400
                \nsave rmsf_pymol.png, dpi=300
                \n\u5236\u4f5c\u52a8\u753b（\u53ef\u9009）
                \nmset 1 x100
                \nmplay <\/p>\n

                \n

                \u5341\u4e00\u3001\u5f84\u5411\u5206\u5e03\u51fd\u6570（RDF）<\/h3>\n

                CPPTRAJ\u8f93\u5165：<\/p>\n

                bash<\/p>\n

                parm system.prmtop
                \ntrajin production.nc
                \n\u6c34\u5206\u5b50\u6c27\u539f\u5b50\u56f4\u7ed5\u86cb\u767d\u8d28\u7684RDF
                \nradial rdf_water.dat 0.1 10.0 :1-300 :WAT@O volume
                \n\u79bb\u5b50\u56f4\u7ed5\u914d\u4f53\u7684RDF
                \nradial rdf_ion.dat 0.1 10.0 resname LIG :Na+ <\/p>\n

                Python\u4f5c\u56fe：<\/p>\n

                Python<\/p>\n

                rdf = pd.read_csv('rdf_water.dat', delim_whitespace=True, comment='#',
                \n names=['r', 'g(r)'])
                \nfig, ax = plt.subplots(figsize=(10, 6))
                \nax.plot(rdf['r'], rdf['g(r)'], color='#1D3557', linewidth=2)
                \nax.axhline(y=1, color='gray', linestyle='–', alpha=0.5, label='Bulk density')
                \n\u6807\u6ce8\u5cf0\u4f4d
                \nfrom scipy.signal import find_peaks
                \npeaks, _ = find_peaks(rdf['g(r)'], height=1.5, distance=10)
                \nax.scatter(rdf['r'].iloc[peaks], rdf['g(r)'].iloc[peaks], color='red', s=100, zorder=5)
                \nax.set_xlabel('r (\u00c5)', fontsize=12)
                \nax.set_ylabel('g(r)', fontsize=12)
                \nax.set_title('Radial Distribution Function: Protein-Water', fontsize=14)
                \nax.legend()
                \nax.grid(True, alpha=0.3)
                \nplt.savefig('rdf_analysis.png', dpi=300) <\/p>\n

                \n

                \u5341\u4e8c\u3001\u81ea\u52a8\u5316\u5206\u6790\u6d41\u6c34\u7ebf<\/h3>\n

                \u5b8c\u6574Python\u81ea\u52a8\u5316\u811a\u672c：<\/p>\n

                Python<\/p>\n

                #!\/usr\/bin\/env python3
                \n"""
                \nAmber MD Trajectory Analysis Pipeline
                \n\u81ea\u52a8\u751f\u6210\u6807\u51c6\u5206\u6790\u56fe\u8868
                \n"""
                \nimport subprocess
                \nimport pandas as pd
                \nimport matplotlib.pyplot as plt
                \nimport seaborn as sns
                \nfrom pathlib import Path
                \nclass AmberAnalyzer:
                \ndef init(self, topology, trajectory, output_dir='analysis'):
                \nself.top = topology
                \nself.traj = trajectory
                \nself.outdir = Path(output_dir)
                \nself.outdir.mkdir(exist_ok=True)
                \ndef run_cpptraj(self, script_name, commands):
                \n """\u8fd0\u884ccpptraj\u5e76\u4fdd\u5b58\u8f93\u5165\u811a\u672c"""
                \n script_path = self.outdir \/ f"{script_name}.in"
                \n with open(script_path, 'w') as f:
                \n f.write(f"parm {self.top}\\\\n")
                \n f.write(f"trajin {self.traj}\\\\n")
                \n f.write(commands)
                \nresult = subprocess.run(['cpptraj', '-i', str(script_path)],
                \n capture_output=True, text=True)
                \nreturn result
                \ndef analyze_rmsd(self):
                \n"""RMSD\u5206\u6790"""
                \ncommands = """
                \nautoimage
                \nrmsd rmsd_protein :1-300@CA,C,N out {outdir}\/rmsd.dat time 0.1
                \n""".format(outdir=self.outdir)
                \nself.run_cpptraj('rmsd_analysis', commands)
                \nself._plot_rmsd()
                \ndef _plot_rmsd(self):
                \n"""\u7ed8\u5236RMSD\u56fe"""
                \ndata = pd.read_csv(self.outdir\/'rmsd.dat', delim_whitespace=True,
                \ncomment='#', names=['Frame', 'Time', 'RMSD'])
                \nplt.figure(figsize=(10, 6))
                \nplt.plot(data['Time'], data['RMSD'], color='#2E86AB', linewidth=1.5)
                \nplt.xlabel('Time (ns)')
                \nplt.ylabel('RMSD (\u00c5)')
                \nplt.title('Protein Backbone RMSD')
                \nplt.grid(True, alpha=0.3)
                \nplt.savefig(self.outdir\/'rmsd_plot.png', dpi=300, bbox_inches='tight')
                \nplt.close()
                \n\u4f7f\u7528\u793a\u4f8b
                \nanalyzer = AmberAnalyzer('protein.prmtop', 'production.nc')
                \nanalyzer.analyze_rmsd() <\/p>\n

                \n

                \u5341\u4e09\u3001\u5de5\u5177\u9009\u578b\u603b\u8868<\/h3>\n\n\u5206\u6790\u7c7b\u578b\u63a8\u8350\u5de5\u5177\u8f93\u51fa\u683c\u5f0f\u53ef\u89c6\u5316\u5e93<\/tr>\n\n\n\n\n\n\n\n
                RMSD\/RMSF<\/td>\nCPPTRAJ<\/td>\n.dat<\/td>\nMatplotlib\/Seaborn<\/td>\n<\/tr>\n
                \u6c22\u952e\u5206\u6790<\/td>\nCPPTRAJ<\/td>\n.dat, .gnu<\/td>\nMatplotlib\u70ed\u56fe<\/td>\n<\/tr>\n
                PCA<\/td>\nCPPTRAJ<\/td>\n.dat<\/td>\nScipy+Matplotlib<\/td>\n<\/tr>\n
                \u80fd\u91cf\u5206\u6790<\/td>\nprocess_mdout.perl<\/td>\n.dat<\/td>\nPandas+Matplotlib<\/td>\n<\/tr>\n
                3D\u53ef\u89c6\u5316<\/td>\nVMD\/PyMOL<\/td>\n.png, .tga<\/td>\n\u5185\u7f6e\u6e32\u67d3\u5668<\/td>\n<\/tr>\n
                RDF\/\u7a7a\u95f4\u5206\u5e03<\/td>\nCPPTRAJ<\/td>\n.dat<\/td>\nMatplotlib<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                \u8fd9\u5957\u5206\u6790\u6d41\u7a0b\u6db5\u76d6\u4e86Amber\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u540e\u5904\u7406\u7684\u7edd\u5927\u591a\u6570\u9700\u6c42，\u5efa\u8bae\u6839\u636e\u5177\u4f53\u7814\u7a76\u95ee\u9898\u9009\u62e9\u5408\u9002\u7684\u5206\u6790\u7ec4\u5408\u3002\u5bf9\u4e8e\u5927\u89c4\u6a21\u8f68\u8ff9，\u5efa\u8bae\u4f7f\u7528pytraj（Python\u63a5\u53e3）\u6216\u5e76\u884c\u5316\u7684CPPTRAJ\u4ee5\u63d0\u9ad8\u6548\u7387<\/p>\n

                \u53c2\u8003\u8d44\u6599<\/h3>\n
                  \n
                • Amber \u5b98\u65b9\u624b\u518c（Amber24）：https:\/\/ambermd.org\/doc12\/Amber24.pdf<\/li>\n
                • cpptraj \u6559\u7a0b：Tutorial C1<\/li>\n
                • Matplotlib \u5b98\u7f51：https:\/\/matplotlib.org<\/li>\n
                • MDAnalysis \u7528\u6237\u6307\u5357：Redirecting to https:\/\/userguide.mdanalysis.org\/stable\/index.html<\/li>\n
                • pytraj \u6587\u6863：pytraj: data analysis package for MD simulation data<\/li>\n<\/ul>\n
                  \n

                  \u5173\u952e\u5b57<\/h3>\n

                  AMBER cpptraj RMSD RMSF \u6c22\u952e\u5206\u6790 \u4e3b\u6210\u5206\u5206\u6790 \u5f84\u5411\u5206\u5e03\u51fd\u6570 \u8f68\u8ff9\u4f5c\u56fe<\/p>\n

                  \u7cfb\u5217\u5bfc\u822a：\u4e13\u680f\u5168\u96c6\u00a0\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Amber<\/p>\n

                  \u66f4\u591a\u4e13\u680f：<\/p>\n\n\u86cb\u767d \/ \u591a\u80bd\u5206\u5b50\u6a21\u62df \/ \u52a8\u529b\u5b66\u5206\u5b50\u5bf9\u63a5 \/ CADD \/ \u5de5\u5177\u5176\u4ed6<\/tr>\n\n\n\n\n\n\n\n
                  \u5f00\u6e90\u86cb\u767d\u7ed3\u6784\u63a8\u7406\u9884\u6d4b<\/td>\n\u5206\u5b50\u6a21\u62df\u57fa\u7840<\/td>\nUCSF DOCK\u7cfb\u5217<\/td>\nagent\u667a\u80fd\u4f53\u7cfb\u5217<\/td>\n<\/tr>\n
                  \u5f00\u6e90\u86cb\u767d\u751f\u6210\u65b9\u6cd5\u5b9e\u8df5<\/td>\n\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Amber<\/td>\nrDock\u7cfb\u5217<\/td>\n\u5316\u5b66\u5927\u6a21\u578b\u4ecb\u7ecd（2025）<\/td>\n<\/tr>\n
                  \u86cb\u767d\u836f\u7269\u8bbe\u8ba1-\u539f\u7406\u4e0e\u6848\u4f8b\u5256\u6790<\/td>\n\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Gromacs<\/td>\nLeDock\u7cfb\u5217<\/td>\n\u6211\u80e1\u5e08\u5144\u8bf4\u836f<\/td>\n<\/tr>\n
                  \u5f00\u6e90\u591a\u80bd\u8bbe\u8ba1\u6a21\u578b\u548c\u65b9\u6cd5\u5b9e\u8df5<\/td>\n\u7d50\u5408\u81ea\u7531\u80fd<\/td>\nCADD\u4e2d\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/td>\nsiRNA\u836f\u7269\u8bbe\u8ba1\u6a21\u578b<\/td>\n<\/tr>\n
                  \u5f00\u6e90\u591a\u80bd\u6027\u8d28\u9884\u6d4b<\/td>\n\u9ad8\u6548\u8ba1\u7b97\u57fa\u672c\u914d\u7f6e<\/td>\n\u5c0f\u5206\u5b50\u836f\u7269\u8bbe\u8ba1-\u539f\u7406\u4e0e\u6848\u4f8b\u5256\u6790<\/td>\nASO\u836f\u7269\u8bbe\u8ba1\u6a21\u578b<\/td>\n<\/tr>\n
                  \u591a\u80bd\u836f\u7269\u8bbe\u8ba1-\u539f\u7406\u4e0e\u6848\u4f8b\u5256\u6790<\/td>\n\u4f5c\u7528\u4e8eDNA\/RNA\u7684\u836f\u7269\u8bbe\u8ba1\u5b9e\u8df5<\/td>\n\u5f00\u6e90\u5c0f\u5206\u5b50\u751f\u6210\u548c\u8bbe\u8ba1\u5b9e\u8df5<\/td>\n\u5f00\u6e90\u836f\u4ee3\u52a8\u529b\u5b66\u6a21\u62df\u8f6f\u4ef6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

                  \u672c\u6587\u6536\u5f55\u4e8e\u4e13\u680f\u00a0\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Amber\u00a0\u2014\u2014 \u4e13\u680f\u7cfb\u7edf\u8986\u76d6Amber \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u5168\u6d41\u7a0b，\u70b9\u51fb\u8ba2\u9605\u53ef\u8ddf\u8e2a\u540e\u7eed\u66f4\u65b0\u3002
                  \n\u6458\u8981：\u672c\u6587\u7cfb\u7edf\u68b3\u7406 Amber \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u8f68\u8ff9\u7684\u5e38\u7528\u5206\u6790\u5de5\u5177\u4e0e\u5b8c\u6574\u6d41\u7a0b，\u6db5\u76d6 cpptraj\/ptraj\/VMD \u7684\u9009\u578b\u5bf9\u6bd4，\u4ee5\u53ca RMSD\u3001RMSF\u3001\u56de\u65cb\u534a\u5f84\u3001\u6c22\u952e\u3001\u8ddd\u79bb\/\u4e8c\u9762\u89d2\u3001PCA \u516d\u7c7b\u5e38\u89c1\u5206\u6790\u7684 cpptraj \u8f93\u5165\u6587\u4ef6\u6a21\u677f\u3002\u6587\u7ae0\u8fd8\u7ed9\u51fa\u4ece\u6570\u636e\u5bfc\u51fa\u5230\u8bba\u6587\u7ea7\u4f5c\u56fe\u7684\u56db\u79cd\u65b9\u5f0f（M<\/p>\n","protected":false},"author":2,"featured_media":106989,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[8684,12153,12154,12155,12156],"topic":[],"class_list":["post-106990","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-amber","tag-cpptraj","tag-rmsd","tag-rmsf","tag-12156"],"yoast_head":"\nAmber\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df16: Amber\u8f68\u8ff9\u5206\u6790\u4e0e\u4f5c\u56fe-1 - 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