论文用分子对接 Methods 模板

Markers rarely grade your conda environment — they grade whether Methods are reproducible and Results are honest. Use the templates below for BSc/MSc chapters and lab reports. Replace bracketed fields with values from your run_manifest.json or Dock PDF. Then run or re-export from AutoDock Vina online if you still need figures.

Methods template (English — rigid-receptor Vina)

Molecular docking was performed with AutoDock Vina using a rigid-receptor protocol. The receptor structure [PDB ID] was obtained from the RCSB Protein Data Bank. Waters and non-essential heteroatoms were removed; [cofactor policy]. Ligands were prepared from SMILES/SDF with protonation at pH [X.X]. The search box was defined from [co-crystal ligand centroid / residue anchors / predicted pocket] with dimensions [Å × Å × Å] centered at [x, y, z]. Vina was run with exhaustiveness [8], num_modes [9], and energy_range [3] kcal/mol. The top poses were retained for analysis. Protein–ligand interactions were annotated with PLIP; pose quality was checked with [PoseBusters / visual inspection]. Affinities are reported as Vina scores (kcal/mol) and interpreted only as relative ranks under identical settings.

Methods template (when you used Dock cloud)

Docking jobs were submitted through the Dock webserver (moleculardocking.online), which prepares receptor and ligand PDBQT files with Meeko and executes AutoDock Vina in the cloud. Job metadata (engine versions, box coordinates, protonation pH, and Vina settings) were recorded in run_manifest.json and mirrored in the PDF report. Optional co-crystal redocking was [enabled/disabled] as a sanity check before analog ranking.

Results paragraph skeleton

Redocking of the co-crystal ligand yielded an affinity of [−X.X] kcal/mol with heavy-atom RMSD [Y.Y] Å relative to the crystallographic pose. Among [N] analogs, affinities ranged from [−A.A] to [−B.B] kcal/mol. The top-ranked compound formed [H-bonds / hydrophobic contacts] with residues [list]. Compounds within 0.5 kcal/mol were treated as rank ties and compared by interaction patterns rather than score alone.

Discussion / limitations (required for a defensible thesis)

• State that Vina scores are not experimental Kd/IC50.
• Note rigid receptor — no induced fit.
• Cite box definition and any apo/AlphaFold assumptions (holo vs apo guide).
• Never claim a universal “good score”; link ranking logic to vina score cutoffs.

Publication-style export checklist

Deliverable	Why markers want it	Where on Dock
Affinity table (CSV / PDF)	Ranked compounds for Results	PDF + ZIP
2D interaction figure	H-bonds / contacts discussion	PLIP figures in ZIP
3D binding pose	Show ligand in pocket	Complex PDB + optional PyMOL PNG
run_manifest.json	Reproducible Methods numbers	ZIP root
Redock RMSD (if holo)	Workflow sanity check	redock_report.json / PDF

Figure captions you can adapt

• Figure X. Predicted binding pose of [ligand] in [PDB ID] from AutoDock Vina. Key residues labeled; interactions from PLIP.
• Table X. Vina affinities (kcal/mol) for [series] under identical rigid-receptor settings (exhaustiveness 8).

Chinese Methods stub (国内课程常用)

采用 AutoDock Vina 进行刚性受体分子对接。受体结构取自 PDB（[ID]），去除水分子及非必要异质原子；配体由 SMILES/SDF 在 pH [X.X] 下质子化准备。对接盒子由 [共晶配体/残基/预测口袋] 定义，尺寸为 [Å]。Vina 参数：exhaustiveness=[8]，num_modes=[9]，energy_range=[3] kcal/mol。结果以 Vina 亲和力（kcal/mol）相对排序，并结合相互作用与构象质量分析；该评分不能等同于实验结合常数。

Generate the package, then paste

1. Open Dock → PDB + SMILES → Review setup (0 credits) → Run docking.
2. Download ZIP + PDF; copy numbers from run_manifest.json into the Methods template.
3. If prep fails locally, use PDB to PDBQT or read common errors.

Also useful: step-by-step tutorial · interpret results · class screening guide.