Bohrium
robot
新建

空间站广场

论文
Notebooks
比赛
课程
Apps
我的主页
我的Notebooks
我的论文库
我的足迹

我的工作空间

任务
节点
文件
数据集
镜像
项目
数据库
公开
Uni-GBSA：为高通量虚拟筛选设计的结合自由能计算自动化工具
Tutorial
中文
Virtual Screening
Uni-GBSA
Tutorial中文Virtual ScreeningUni-GBSA
bozh@dp.tech
发布于 2023-06-20
赞 4
10
AI4SCUP-CNS-BBB(v1)

Uni-GBSA：为高通量虚拟筛选设计的结合自由能计算自动化工具

M. Yang, Z. Bo, T. Xu, B. Xu, D. Wang, H. Zhang (2023). Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening, Briefings in Bioinformatics,

2023年6月17日，深势科技在Briefings in Bioinformatics上发表了一篇题为Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening的文章，发布了Uni-GBSA结合自由能自动计算套件。Uni-GBSA可以自动地完成MM/GB(PB)SA计算的拓扑准备、结构优化和结合自由能计算整个流程。在有若干化合物亲和力实验数据的蛋白体系中，Uni-GBSA可以自动进行结合自由能计算参数扫描，挑选最优的参数组合。此外，为了在虚拟筛选中快速评估大量蛋白-配体复合物的结合自由能计算，Uni-GBSA还提供了一种并行批处理模式，可以同时评估数千个分子。这项工作降低了结合自由能计算工具的使用门槛，为结合自由能计算虚拟筛选中的广泛应用提供了高效的计算工具。

June 19, 2023 – In a recent publication in the Briefings in Bioinformatics, Uni-GBSA: an open-source and web-based automatic workflow to perform MM/GB(PB)SA calculations for virtual screening, DP Technology has introduced Uni-GBSA, a user-friendly automatic workflow to perform MM/GB(PB)SA calculations. Uni-GBSA can perform topology preparation, structure optimization, binding free energy calculation and parameter scanning for MM/GB(PB)SA calculations. It also offers a batch mode that evaluates thousands of molecules against one protein target in parallel for efficient application in virtual screening. The default parameters are selected after systematic testing on the PDBBind-2011 refined dataset. In our case studies, Uni-GBSA produced a satisfactory correlation with the experimental binding affinities and outperformed AutoDock Vina in molecular enrichment.

代码
文本

**Uni-GBSA现面向用户开放免费获取！**遵从使用协议，用户可以从深势科技GitHub仓库的Uni-GBSA release页面获取Uni-GBSA的最新发行版。

Uni-GBSA is available for users to obtain for free! In compliance with the usage agreement, users can obtain the latest release of Uni-GBSA from DeepTech's GitHub repository on the Uni-GBSA release page.

通过本教程，你可以学会如何安装Uni-GBSA，使用Uni-GBSA运行结合自由能计算任务，并对其结果进行简单分析。
Through this tutorial, you can learn how to install Uni-GBSA, perform MM/GB(PB)SA calculations using Uni-GBSA, and perform a simple analysis of the results.
快速开始：点击上方的 开始连接 按钮，选择 unigbsa:0.1.4镜像及任意CPU节点配置，稍等片刻即可运行。
Quick Start: Click the Start Connection button at the top, choose the unigbsa:0.1.4 image and any CPU node configuration, and wait a moment to run.

代码
文本

1. [Installation] 下载和安装Uni-GBSA

Uni-GBSA的运行依赖于包括acpype、gmx_MMPBSA、lickit等的第三方软件，这些软件在当前的镜像中已经安装。

Uni-GBSA depends on several third-party softwares including acpype, gmx_MMPBSA, lickit, etc. These softwares have already been installed in this image.

如果不在此镜像中或者不在Bohrium平台，可以按照以下方式通过Conda安装Uni-GBSA以及第三方安装包。

If you want to install Uni-GBSA on a different image or not on the Bohrium web platform, you can install Uni-GBSA and its third-party dependencies using Conda.

conda create -n gbsa -c conda-forge acpype openmpi mpi4py gromacs
conda activate gbsa
pip install unigbsa gmx_MMPBSA>=1.5.6 lickit
代码
文本
[2]
# Install Uni-GBSA with pip
!pip install unigbsa==0.1.5
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting unigbsa==0.1.5
  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/dc/12/45ae03f4a22f483a69c4dea91bf7522a375ca7aca9367745f83a0db92c94/unigbsa-0.1.5-py3-none-any.whl (419 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 419.4/419.4 kB 565.2 kB/s eta 0:00:00a 0:00:01
Requirement already satisfied: acpype in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from unigbsa==0.1.5) (2022.6.6)
Requirement already satisfied: gmx-MMPBSA>=1.5.6 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from unigbsa==0.1.5) (1.6.1)
Requirement already satisfied: lickit in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from unigbsa==0.1.5) (0.1.0)
Requirement already satisfied: tqdm in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from unigbsa==0.1.5) (4.65.0)
Requirement already satisfied: pandas>=1.2.2 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (2.0.2)
Requirement already satisfied: seaborn<0.12 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (0.11.2)
Requirement already satisfied: mpi4py>=3.1.3 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (3.1.4)
Requirement already satisfied: scipy>=1.6.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (1.10.1)
Requirement already satisfied: matplotlib>=3.5.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (3.7.1)
Requirement already satisfied: contourpy>=1.0.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (1.1.0)
Requirement already satisfied: cycler>=0.10 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (4.40.0)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (1.4.4)
Requirement already satisfied: numpy>=1.20 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (1.25.0)
Requirement already satisfied: packaging>=20.0 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (23.1)
Requirement already satisfied: pillow>=6.2.0 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (9.5.0)
Requirement already satisfied: pyparsing>=2.3.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (3.1.0)
Requirement already satisfied: python-dateutil>=2.7 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from pandas>=1.2.2->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (2023.3)
Requirement already satisfied: tzdata>=2022.1 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from pandas>=1.2.2->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (2023.3)
Requirement already satisfied: six>=1.5 in /opt/mamba/envs/gbsa/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib>=3.5.1->gmx-MMPBSA>=1.5.6->unigbsa==0.1.5) (1.16.0)
Installing collected packages: unigbsa
  Attempting uninstall: unigbsa
    Found existing installation: unigbsa 0.1.4
    Uninstalling unigbsa-0.1.4:
      Successfully uninstalled unigbsa-0.1.4
Successfully installed unigbsa-0.1.5
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
代码
文本
[1]
import os
代码
文本

Uni-GBSA还包含很多命令行： unigbsa-pipeline, unigbsa-scan, unigbsa-traj, unigbsa-pbc, unigbsa-buildtop, unigbsa-buildsys, unigbsa-md。你可以使用下面的单元格来探索它们。

Uni-GBSA contains many command lines: unigbsa-pipeline, unigbsa-scan, unigbsa-traj, unigbsa-pbc, unigbsa-buildtop, unigbsa-buildsys, unigbsa-md. You can explore them using the cell below.

代码
文本
[26]
#os.system('unigbsa-pipeline -h')
#os.system('unigbsa-scan -h')
#os.system('unigbsa-traj -h')
#os.system('unigbsa-pbc -h')
#os.system('unigbsa-buildtop -h')
#os.system('unigbsa-buildsys -h')
#os.system('unigbsa-md -h')
代码
文本

2. [Run PBSA/GBSA calculations using Uni-GBSA] 使用Uni-GBSA进行结合自由能计算

其中，unigbsa-pipeline是将所有的单个工具自动化的核心命令行。用户只需要提供蛋白质和配体的输入文件，unigbsa-pipeline可以自动进行拓扑准备、结构优化到结合自由能计算。

unigbsa-pipeline is a simple, automatic pipeline to perform MM/GB(PB)SA calculations. You only need to provide a protein file (in the PDB format) and ligand files (in the MOL or SDF format). This function will perform an energy minimization then calculate the PBSA/GBSA values for the each input ligand.

代码
文本
[27]
os.system('unigbsa-pipeline -h')
usage: unigbsa-pipeline [-h] -i RECEPTOR [-l LIGAND [LIGAND ...]] [-c CONFIG]
                        [-d LIGDIR] [-f PBSAFILE] [-o OUTFILE] [-nt THREAD]
                        [--decomp] [--verbose] [-v]

GBSA Calculation. Version: 0.1.4

options:
  -h, --help            show this help message and exit
  -i RECEPTOR           Input protein file with pdb format.
  -l LIGAND [LIGAND ...]
                        Ligand files to calculate binding energy.
  -c CONFIG             Configue file, default:
                        /opt/mamba/envs/gbsa/lib/python3.10/site-
                        packages/unigbsa/data/default.ini
  -d LIGDIR             Floder contains many ligand files. file format: .mol
                        or .sdf
  -f PBSAFILE           gmx_MMPBSA input file. default=None
  -o OUTFILE            Output file.
  -nt THREAD            Set number of thread to run this program.
  --decomp              Decompose the free energy. default:False
  --verbose             Keep all the files.
  -v, --version         show program's version number and exit

0
代码
文本
[28]
# Download test example
# 下载测试数据

!git clone https://github.com/dptech-corp/Uni-GBSA.git
fatal: destination path 'Uni-GBSA' already exists and is not an empty directory.
代码
文本

2.1 [MM/GB(PB)SA calculation of one ligand aginst a receptor] 一个蛋白与一个配体的结合自由能计算

代码
文本
[29]
# Get a ligand SDF file and a protein PDB file
# 获取配体SDF文件与蛋白PDB文件

ligand = 'Uni-GBSA/example/1ceb/1ceb_ligand.sdf' # 配体SDF文件
protein = 'Uni-GBSA/example/1ceb/1ceb_protein.pdb' # 蛋白PDB文件
代码
文本
[30]
# Perform an automatic GBSA calculation on the test example
# 进行一个结合自由能计算

os.system(f'unigbsa-pipeline -i {protein} -l {ligand} -o BindingEnergy.csv')
06/20/2023 07:10:46 AM - INFO - Build protein topology.
06/20/2023 07:10:47 AM - INFO - Build ligand topology: 1ceb_ligand
06/20/2023 07:10:49 AM - INFO - Running energy minimization: 1ceb_ligand
06/20/2023 07:10:51 AM - INFO - Run the MMPB(GB)SA.
06/20/2023 07:10:59 AM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -20.0715  

0
代码
文本

2.2 [MM/GB(PB)SA calculations of multiple ligands aginst a receptor] 一个蛋白与多个配体的结合自由能计算

代码
文本
[49]
# Get relevent files
# 获取相关文件

protein_file = 'Uni-GBSA/example/scan/protein.pdb' # 一个蛋白PDB文件
ligand_files = 'Uni-GBSA/example/scan/*.sdf' # 多个配体SDF文件
代码
文本
[50]
# Perform an automatic GBSA calculation on the test example
# 进行一个结合自由能计算

os.system(f'unigbsa-pipeline -i {protein_file} -l {ligand_files} -o BindingEnergy.csv')
06/20/2023 07:18:15 AM - INFO - Build protein topology.
  0%|          | 0/4 [00:00<?, ?it/s]06/20/2023 07:18:24 AM - INFO - Run the MMPB(GB)SA.
06/20/2023 07:18:25 AM - INFO - Run the MMPB(GB)SA.
06/20/2023 07:18:25 AM - INFO - Run the MMPB(GB)SA.
06/20/2023 07:18:26 AM - INFO - Run the MMPB(GB)SA.
06/20/2023 07:18:43 AM - INFO - Clean the results.
06/20/2023 07:18:43 AM - INFO - Clean the results.
 25%|██▌       | 1/4 [00:26<01:20, 26.79s/it]06/20/2023 07:18:44 AM - INFO - Clean the results.
06/20/2023 07:18:45 AM - INFO - Clean the results.
100%|██████████| 4/4 [00:28<00:00,  7.12s/it]
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.0858  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.4789  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -46.1509  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -49.0785  

0
代码
文本

[MM/GB(PB)SA Parameter Scan (Optional)] MM/GB(PB)SA参数扫描 （可选）

如果用户有实验结合数据，Uni-GBSA可以在进行MM/GB(PB)SA计算之前进行自动参数优化。在这个步骤中，用户需要提供一个小型的蛋白质-配体复合物的数据集，它们相应的实验结合数据和用户希望扫描的参数。unigbsa-scan会使用用户选择的不同参数进行一系列的MM/GB(PB)SA计算，随后通过计算计算的结合自由能和实验数据之间的相关性进行排序，最后返回一组优化参数。这些参数可用于后续的MM/GB(PB)SA计算，用于更大的数据集。

If the user has experimental binding data, Uni-GBSA can perform an automatic parameter optimization prior to production MM/GB(PB)SA calculation. In this step, the user needs to provide a small dataset of protein–ligand complexes, their corresponding experimental binding data and the parameters the user wishes to scan. A series of MM/GB(PB)SA calculations are performed using different sets of parameters chosen by users. The correlation between binding free energies calculated by the MM/GB(PB)SA approach and the experimental data for each set of parameters are automatically calculated and ranked. Finally, a set of optimized parameters for this system are returned, which can be used in the subsequent MM/GB(PB)SA calculations for the larger dataset.

注意：我们建议您选用32核128GB的机器运行以下代码。

Note: We recommend that you use the 32 cores 128 GB machine for testing this section.

代码
文本
[3]
os.system('unigbsa-scan -h')
usage: unigbsa-scan [-h] [-i RECEPTOR] [-pd PROTDIR] [-l LIGAND [LIGAND ...]]
                    [-ld LIGDIR] -e E -c PARASFILE [-o OUTDIR] [-nt THREAD]
                    [--verbose]

GBSA Calculation.

options:
  -h, --help            show this help message and exit
  -i RECEPTOR           Input protein file with pdb format.
  -pd PROTDIR           Floder contains many protein files. file format: .pdb
  -l LIGAND [LIGAND ...]
                        Ligand files to calculate binding energy.
  -ld LIGDIR            Floder contains many ligand files. file format: .mol
                        or .sdf
  -e E                  Experiment data file.
  -c PARASFILE          Parameters to scan
  -o OUTDIR             Output directory.
  -nt THREAD            Set number of thread to run this program.
  --verbose             Keep all the files.

0
代码
文本
[3]
# Get relevent files
# 获取相关文件

protein_file = 'Uni-GBSA/example/scan/protein.pdb' # 一个蛋白PDB文件
ligand_files = 'Uni-GBSA/example/scan/*.sdf' # 多个配体SDF文件
experimental_data = 'Uni-GBSA/example/scan/ligands.csv' # 配体的实验结合数据
parameters_to_scan = 'Uni-GBSA/example/scan/scan.json' # 需要扫描的参数
代码
文本
[4]
# Take a look at the parameters to scan
# 看一看扫描参数

import json
with open(parameters_to_scan) as f:
param = json.load(f)
param
{'simulation': {'mode': 'em',
  'boxtype': 'triclinic',
  'boxsize': 0.9,
  'conc': 0.15,
  'nsteps': 500000,
  'nframe': 100,
  'eqsteps': 50000,
  'proteinforcefield': 'amber03',
  'ligandforcefield': 'gaff',
  'maxsol': 0,
  'ligandCharge': 'bcc'},
 'GBSA': {'sys_name': 'GBSA',
  'modes': ['gb-1', 'gb-2', 'gb-5', 'pb-1', 'pb-2'],
  'indi': [2.0, 4.0, 3.0],
  'exdi': '80.0',
  'nonpolarsurfConst': '0.0',
  'surften': '0.0072'}}
代码
文本
[6]
# Perform parameter scan
# 进行参数扫描

os.system(f'unigbsa-scan -i {protein_file} -l {ligand_files} -e {experimental_data} -c {parameters_to_scan} -o scan-demo -nt 32')
06/20/2023 15:26:24 PM - INFO - load scan paras.
06/20/2023 15:26:24 PM - INFO - Building protein and ligand topology.
06/20/2023 15:26:24 PM - INFO - Build ligand topology: 1a
06/20/2023 15:26:24 PM - INFO - Build ligand topology: 1b
06/20/2023 15:26:24 PM - INFO - Build ligand topology: 3a
06/20/2023 15:26:24 PM - INFO - Build ligand topology: 3b
06/20/2023 15:26:34 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_modes_gb-1
06/20/2023 15:26:34 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:34 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:34 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:34 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:54 PM - INFO - Clean the results.
06/20/2023 15:26:54 PM - INFO - Clean the results.
06/20/2023 15:26:54 PM - INFO - Clean the results.
06/20/2023 15:26:54 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -44.4101  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -47.0945  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -49.9980  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.8871  
06/20/2023 15:26:55 PM - INFO - GBSA results: 0.766238667269586 0.5871216952190713
06/20/2023 15:26:55 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_modes_gb-2
06/20/2023 15:26:56 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:56 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:56 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:26:56 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:15 PM - INFO - Clean the results.
06/20/2023 15:27:15 PM - INFO - Clean the results.
06/20/2023 15:27:16 PM - INFO - Clean the results.
06/20/2023 15:27:16 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -46.1508  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.4590  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -49.0786  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.0858  
06/20/2023 15:27:17 PM - INFO - GBSA results: 0.7538697768291684 0.5683196404164602
06/20/2023 15:27:17 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_modes_gb-5
06/20/2023 15:27:17 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:17 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:17 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:17 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:37 PM - INFO - Clean the results.
06/20/2023 15:27:37 PM - INFO - Clean the results.
06/20/2023 15:27:37 PM - INFO - Clean the results.
06/20/2023 15:27:38 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -46.7154  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.9583  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.6054  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -49.6686  
06/20/2023 15:27:39 PM - INFO - GBSA results: 0.7538205777663287 0.5682454634639617
06/20/2023 15:27:39 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_modes_pb-1
06/20/2023 15:27:39 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:39 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:39 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:27:39 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:28:34 PM - INFO - Clean the results.
06/20/2023 15:28:34 PM - INFO - Clean the results.
06/20/2023 15:28:35 PM - INFO - Clean the results.
06/20/2023 15:28:36 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -24.7204  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -22.0142  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -20.1450  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -21.5136  
06/20/2023 15:28:37 PM - INFO - GBSA results: 0.459006650471149 0.21068710517674358
06/20/2023 15:28:37 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_modes_pb-2
06/20/2023 15:28:37 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:28:37 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:28:37 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:28:37 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:29:32 PM - INFO - Clean the results.
06/20/2023 15:29:33 PM - INFO - Clean the results.
06/20/2023 15:29:39 PM - INFO - Clean the results.
06/20/2023 15:29:39 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -21.5190  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -22.0195  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -24.7273  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      pb              -20.1523  
06/20/2023 15:29:40 PM - INFO - GBSA results: 0.45904917771713166 0.21072614756277472
06/20/2023 15:29:40 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_indi_2.0
06/20/2023 15:29:40 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:29:40 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:29:40 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:29:40 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:29:59 PM - INFO - Clean the results.
06/20/2023 15:30:00 PM - INFO - Clean the results.
06/20/2023 15:30:00 PM - INFO - Clean the results.
06/20/2023 15:30:01 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -45.9443  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.0410  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -40.5207  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -40.6367  
06/20/2023 15:30:01 PM - INFO - GBSA results: 0.7025347235707834 0.493555037822677
06/20/2023 15:30:01 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_indi_4.0
06/20/2023 15:30:02 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:02 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:02 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:02 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:21 PM - INFO - Clean the results.
06/20/2023 15:30:21 PM - INFO - Clean the results.
06/20/2023 15:30:21 PM - INFO - Clean the results.
06/20/2023 15:30:22 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -46.1508  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.0858  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -49.0786  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -43.4590  
06/20/2023 15:30:24 PM - INFO - GBSA results: 0.7538697768291684 0.5683196404164602
06/20/2023 15:30:24 PM - INFO - GBSA calculation: amber03_gaff_bcc em GBSA_indi_3.0
06/20/2023 15:30:24 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:24 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:24 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:24 PM - INFO - Run the MMPB(GB)SA.
06/20/2023 15:30:44 PM - INFO - Clean the results.
06/20/2023 15:30:45 PM - INFO - Clean the results.
06/20/2023 15:30:45 PM - INFO - Clean the results.
06/20/2023 15:30:45 PM - INFO - Clean the results.
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -42.2695  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -42.4796  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -45.1143  
================================================================================
Results: Energy.csv Dec.csv
Frames    mode    detal_G(kcal/mole)
     1      gb              -48.0338  
================================================================================
The best para name is: GBSA_modes_gb-1
The best para R2 is: 0.5871
The best para file is: /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes_gb-1/paras.json
================================================================================
06/20/2023 15:30:46 PM - INFO - GBSA results: 0.7382766224544969 0.5450523712628197
06/20/2023 15:30:46 PM - INFO - Write output: paras_performance.csv

0
代码
文本
[8]
import pandas as pd
代码
文本
[11]
# Take a look at the parameter scan result
# 看一看参数扫描的结果

df_param = pd.read_csv('scan-demo/paras_performance.csv')
代码
文本
[13]
display(df_param)
name R R2 parasjson
0 GBSA_modes_gb-1 0.766239 0.587122 /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes...
1 GBSA_modes_gb-2 0.753870 0.568320 /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes...
2 GBSA_indi_4.0 0.753870 0.568320 /data/scan-demo/amber03_gaff_bcc/em/GBSA_indi_...
3 GBSA_modes_gb-5 0.753821 0.568245 /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes...
4 GBSA_indi_3.0 0.738277 0.545052 /data/scan-demo/amber03_gaff_bcc/em/GBSA_indi_...
5 GBSA_indi_2.0 0.702535 0.493555 /data/scan-demo/amber03_gaff_bcc/em/GBSA_indi_...
6 GBSA_modes_pb-2 0.459049 0.210726 /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes...
7 GBSA_modes_pb-1 0.459007 0.210687 /data/scan-demo/amber03_gaff_bcc/em/GBSA_modes...
代码
文本

从这个表格中，我们发现gb-1模式(所有其他参数都是默认值）是这个给定数据集的优化参数集。

From this table, we find that gb-1 mode, with all other parameters being the default, is the optimized parameter set for this given dataset.

代码
文本
[ ]

代码
文本
Tutorial
中文
Virtual Screening
Uni-GBSA
Tutorial中文Virtual ScreeningUni-GBSA
已赞4
本文被以下合集收录
App related
Charmy Niu
更新于 2024-01-17
10 篇3 人关注
CADD
9c5545
更新于 2024-04-03
7 篇0 人关注
推荐阅读
公开
Bohrium 帮助文档｜ABACUS
Bohrium 帮助文档LBG UtilityABACUS
Bohrium 帮助文档LBG UtilityABACUS
Bohrium
发布于 2023-10-13
4 转存文件1 评论
公开
CALYPSO_SaaS 快速上手
CALYPSO中文Tutorial工作流
CALYPSO中文Tutorial工作流
小锡兵
发布于 2023-08-01
1 赞28 转存文件