命令行linux版本 Linux版本命令

查看linux内核版本发行版本常用命令

1.uname查看linux内核版本最常用的命令，适用于CentOS，Fedora，Debian，Ubuntu等各种linux发行版本。

成都创新互联公司的客户来自各行各业，为了共同目标，我们在工作上密切配合，从创业型小企业到企事业单位，感谢他们对我们的要求，感谢他们从不同领域给我们带来的挑战，让我们激情的团队有机会用头脑与智慧不断的给客户带来惊喜。专业领域包括成都网站建设、成都网站设计、电商网站开发、微信营销、系统平台开发。

这里我用的服务器是CentOS7版本

uname -a

列出系统所有信息

解释如下

也可以只查询某一项信息

a.显示主机类型：

uname -m

b.显示主机名

uname -n

c.显示操作系统发行编号：

uname -r

d.显示操作系统名称：

uname -s

e.显示系统版本与时间：

uname -v

2.使用linux配置文件查看

a.查看内核版本

cat /proc/version

相比较uname而言可以看到开发者组织和gcc版本号

b.查看发行版本

cat /etc/redhat-release

3.Linux dmesg命令用于显示开机信息

kernel会将开机信息存储在ring buffer中。开机信息亦保存在/var/log目录中，名称为dmesg的文件里。

查看linux内核版本

dmesg | grep Linux

linux系统查看系统版本命令

一、查看Linux内核版本命令

方法一：cat/proc/version

用法：[root@S-CentOS home]# cat /proc/version

方法二：uname-a

用法：[root@S-CentOS home]# uname-a

二、查看Linux系统版本的命令　　

方法一：lsb_release -a

用法：[root@S-CentOS ~]# lsb_release -a

注意：使用该命令即可列出所有版本信息，这个命令适用于所有的Linux发行版，包括RedHat、SUSE、Debian等发行版。

方法二：cat /etc/redhat-release

用法：[root@S-CentOS home]# cat /etc/redhat-release

注意：该方法只适合Redhat系的Linux

方法三：cat /etc/issue

用法：[root@S-CentOS home]# cat /etc/issue

注意：该命令也适用于所有的Linux发行版

Autodock vina 和MGL tools linux版本操作（命令行操作）

蛋白质Edit——加polar H，加Kollman电荷后，Grid——Macromolecule——choose，自动保存pro1.pdbqt

配体 Ligand——Input——QuickSetup，自动保存.out.pdbqt

receptor = clusters_0001_model1.pdbqt

ligand = hypericin.out.pdbqt

center_x = 2.148 （第一个文件可以由Docking——output——Vina生成）

center_y = 3.704

center_z = -2.81

size_x = 74.55 （大于30*30*30，要增加exhaustiveness）

size_y = 74.55

size_z = 74.55

out = cluster1_hypericin_out.pdbqt

log = cluster1_hypericin_out.log

exhaustiveness=24（可以多少个CPU设置多少个，并行计算，exhaustiveness控制一个docking过程重复计算多少次，越高花时间越长，但也不要设置特别大，也没有意义，要在效率和准确度找一个平衡点）

num_modes=30 （不一定输出30个，可能只找到30构象，也有可能被energy_range限制）

energy_range=6 (kcal/mol)

（柔性残基信息：flex=side_chains.pdbqt）

vina --config config1.txt

用pymol打开cluster1_hypericin_out.pdbqt看结果

以下为详细设置和操作：

1. 首先用MGLtools准备蛋白和小分子的坐标文件（pdbqt）

蛋白质: 加氢（Add all hydrogens or just non-polar hydrogens. Merge non-polar hydrogens and their charges with their parent carbon atom）、计算电荷（Assign partial atomic charges to the ligand and the macromolecule (Gasteiger or Kollman United Atom charges))、添加原子类型、柔性残基信息

1. Edit——加polar H，merge nonpolar（自动）后，pdbqt中的原子数会改变，加H的时候，可能会重新编号

2.Edit——加Kollman United Atom charges电荷（只能加这个，还可以计算Gasteiger 电荷，其值更负），加电荷后，pdbqt中的电荷会改变

配体分子: 加氢、计算电荷、确定root（扭矩中心），选择可旋转的键 (Set up rotatable bonds in the ligand using a graphical version of AutoTors)。我直接使用的quick setup。

Tips： H的位置是任意的，仅取决于输入文件；电荷AutoDock Vina ignores the user-supplied partial charges. It has its own way of dealing with the electrostatic interactions through the hydrophobic and the hydrogen bonding terms.

保存pdbqt文件：pro1.pdbqt，pro2.pdbqt，pro3.pdbqt，lig1.pdbqt，lig2.pdbqt，lig3.pdbqt

2. 写参数文件 config.txt

eg. config.txt

receptor = clusters_0001_model1.pdbqt

ligand = hypericin.out.pdbqt

center_x = 2.148

center_y = 3.704

center_z = -2.81

size_x = 74.55 （大于30*30*30，要增加exhaustiveness）

size_y = 74.55

size_z = 74.55

out = cluster1_hypericin_out.pdbqt

log = cluster1_hypericin_out.log

exhaustiveness=15

num_modes=30 （不一定输出30个，可能只找到30构象，也有可能被energy_range限制）

energy_range=6

Details：

Input:

--receptor arg rigid part of the receptor (PDBQT)

--flex arg flexible side chains, if any (PDBQT)

--ligand arg ligand (PDBQT)

Search space (required): 搜索空间有效地限制了包括柔性侧链在内的可移动原子的位置。

（ How big should the search space be?

As small as possible, but not smaller. The smaller the search space, the easier it is for the docking algorithm to explore it. On the other hand, it will not explore ligand and flexible side chain atom positions outside the search space. You should probably avoid search spaces bigger than 30 x 30 x 30 Angstrom, unless you also increase "--exhaustiveness" .）

--center_x arg X coordinate of the center

--center_y arg Y coordinate of the center

--center_z arg Z coordinate of the center

--size_x arg size in the X dimension (Angstroms)

--size_y arg size in the Y dimension (Angstroms)

--size_z arg size in the Z dimension (Angstroms)

Output (optional):

--out arg output models (PDBQT), the default is chosen based on

the ligand file name

--log arg optionally, write log file

Misc (optional):

--cpu arg the number of CPUs to use (the default is to try to detect the number of CPUs or, failing that, use 1)

--seed arg explicit random seed

--exhaustiveness arg (=8) exhaustiveness of the global search (roughly proportional to time): 1+ //使用默认的(或任何给定的)穷尽性设置，用于搜索的时间已经根据原子的数量、flexibility等自发变化。通常情况下，花费额外的时间搜索来降低找不到评分函数的全局最小值的概率是没有意义的，这个概率远远低于该最小值远离本机构象的概率。然而，如果你觉得在exhaustiveness和时间之间的自动平衡是不够的，你可以提高exhaustiveness的数值。这将线性地增加时间，并降低不找到最小值的概率。

--num_modes arg (=9) maximum number of binding modes to generate //改成30

--energy_range arg (=3) maximum energy difference between the best binding //改成 8

mode and the worst one displayed (kcal/mol)

Configuration file (optional):

--config arg the above options can be put here

Information (optional):

--help display usage summary

--help_advanced display usage summary with advanced options

--version display program version

Output：

1. Energy

The predicted binding affinity is in kcal/mol.

2. RMSD

RMSD values are calculated relative to the best mode and use only movable heavy atoms. Two variants of RMSD metrics are provided, rmsd/lb (RMSD lower bound) and rmsd/ub (RMSD upper bound), differing in how the atoms are matched in the distance calculation:

rmsd/ub matches each atom in one conformation with itself in the other conformation, ignoring any symmetry

rmsd' matches each atom in one conformation with the closest atom of the same element type in the other conformation (rmsd' can not be used directly, because it is not symmetric)

rmsd/lb is defined as follows: rmsd/lb(c1, c2) = max(rmsd'(c1, c2), rmsd'(c2, c1))

3. Hydrogen positions

Vina uses a united-atom scoring function. As in AutoDock, polar hydrogens are needed in the input structures to correctly type heavy atoms as hydrogen bond donors. However, in Vina, the degrees of freedom that only move hydrogens, such as the hydroxyl group torsions, are degenerate. Therefore, in the output, some hydrogen atoms can be expected to be positioned randomly (but consistent with the covalent structure). For a united-atom treatment, this is essentially a cosmetic issue.

4. Separate models 用vina_split分割成多个pdbqt

All predicted binding modes, including the positions of the flexible side chains are placed into one multimodel PDBQT file specified by the "out" parameter or chosen by default, based on the ligand file name. If needed, this file can be split into individual models using a separate program called "vina_split" , included in the distribution.

注意：vina_split 的Windows版本要用cmd来实现，找到vina_split所在的目录，运行vina_split --input **.pdbqt

1. Why am I seeing a warning about the search space volume being over 27000 Angstrom^3?

This is probably because you intended to specify the search space sizes in "grid points" (0.375 Angstrom), as in AutoDock 4. The AutoDock Vina search space sizes are given in Angstroms instead. If you really intended to use an unusually large search space, you can ignore this warning, but note that the search algorithm's job may be harder. You may need to increase the value of the exhaustiveness to make up for it. This will lead to longer run time.

2. The bound conformation looks reasonable, except for the hydrogens. Why?

AutoDock Vina actually uses a united-atom scoring function, i.e. one that involves only the heavy atoms. Therefore, the positions of the hydrogens in the output are arbitrary. The hydrogens in the input file are used to decide which atoms can be hydrogen bond donors or acceptors though, so the correct protonation of the input structures is still important.

3. What does "exhaustiveness" really control, under the hood? （exhaustiveness为the number of runs，并行，可以设为cpu数，可以充分利用）

In the current implementation, the docking calculation consists of a number of independent runs, starting from random conformations. Each of these runs consists of a number of sequential steps. Each step involves a random perturbation of the conformation followed by a local optimization (using the Broyden-Fletcher-Goldfarb-Shanno algorithm ) and a selection in which the step is either accepted or not. Each local optimization involves many evaluations of the scoring function as well as its derivatives in the position-orientation-torsions coordinates. The number of evaluations in a local optimization is guided by convergence and other criteria. The number of steps in a run is determined heuristically, depending on the size and flexibility of the ligand and the flexible side chains. However, the number of runs is set by the exhaustiveness parameter. Since the individual runs are executed in parallel, where appropriate, exhaustiveness also limits the parallelism. Unlike in AutoDock 4, in AutoDock Vina, each run can produce several results: promising intermediate results are remembered. These are merged, refined, clustered and sorted automatically to produce the final result.

4. Why do I not get the correct bound conformation?

It can be any of a number of things:

If you are coming from AutoDock 4, a very common mistake is to specify the search space in "points" (0.375 Angstrom), instead of Angstroms.

Your ligand or receptor might not have been correctly protonated. （初始结构没有优化好）

Bad luck (the search algorithm could have found the correct conformation with good probability, but was simply unlucky). Try again with a different seed.

The minimum of the scoring function correponds to the correct conformation, but the search algorithm has trouble finding it. In this case, higher exhaustiveness or smaller search space should help. （搜索算法没有找到最优结构，可以增大exhaustiveness或减小search space）

The minimum of the scoring function simply is not where the correct conformation is. Trying over and over again will not help, but may occasionally give the right answer if two wrongs (inexact search and scoring) make a right. Docking is an approximate approach.

Related to the above, the culprit may also be the quality of the X-ray or NMR receptor structure.

If you are not doing redocking, i.e. using the correct induced fit shape of the receptor, perhaps the induced fit effects are large enough to affect the outcome of the docking experiment.（换受体结构）

The rings can only be rigid during docking. Perhaps they have the wrong conformation, affecting the outcome.

You are using a 2D (flat) ligand as input.

The actual bound conformation of the ligand may occasionally be different from what the X-ray or NMR structure shows.

运维必懂！怎么查看Linux内核版本

1. 前言

内核是操作系统的核心组件。它管理系统的资源，是计算机硬件和软件之间的桥梁。

您可能因多种原因需要确切知道GNU / Linux操作系统上运行的内核版本。也许您正在调试与硬件相关的问题，或者了解影响旧内核版本的新安全漏洞，并且您想知道您的内核是否易受攻击。无论是什么原因，从命令行确定Linux内核版本都非常容易。

在本教程中，我们将向您展示几种不同的方法，以找出系统上运行的Linux内核版本。

怎么查看Linux内核版本

2. 使用`uname`命令查看Linux内核版本

uname命令显示多个系统信息，包括Linux内核体系结构，名称版本和发行版。

要找出系统上正在运行的Linux内核版本，请输入以下命令：

输出结果：

Linux 3.10.0-957.12.2.el7.x86_64 x86_64

3 - 内核版本.

10 - 主修订版本.

0-957 - 次要修订版本.

12 - 补丁版本.

使用uname命令查看Linux内核版本

3. 使用`hostnamectl`命令查看内核版本

hostnamectl实用程序是systemd的一部分，用于查询和更改系统主机名。它还显示Linux发行版和内核版本：

Static hostname: CentOS7.linuxrumen.com

Icon name: computer-vm

Chassis: vm

Machine ID: 20c27040135a4d46b2d3d07180f37303

Boot ID: 2f495af0684e4adfb34f0366f2567460

Virtualization: vmware

Operating System: CentOS Linux 7 (Core)

CPE OS Name: cpe:/o:centos:centos:7

Kernel: Linux 3.10.0-957.12.2.el7.x86_64

Architecture: x86-64

使用以下命令只显示当前内核版本：

使用hostnamectl命令查看内核版本

4. 通过查看/proc/version文件确认内核版本

/proc目录包含虚拟文件，其中包含有关系统内存，CPU内核，已安装文件系统等的信息。有关正在运行的内核的信息存储在/proc/version虚拟文件中。

结合cat查看文件内容：

输出结果如下：

Linux version 3.10.0-957.12.2.el7.x86_64 (mockbuild@kbuilder.bsys.centos.org) (gcc version 4.8.5 20150623 (Red Hat 4.8.5-36) (GCC) ) #1 SMP Tue May 14 21:24:32 UTC 2019

使用hostnamectl命令查看内核版本

5.结论

我们已经向您展示了如何从命令行查找在您的系统上运行的Linux内核的版本。这些命令适用于所有流行的Linux发行版，包括Debian，Red Hat，Ubuntu，Arch Linux，Fedora，CentOS，Kali Linux，OpenSUSE，Linux Mint等。

Linux命令行版本的怎么使用

关于 Linux 系统的命令行，其实使用起来一点也不困难。只需要你首先记住、并熟悉使用Linux 的基本命令（例如：ls、cat、more、chown、chmod，等等），然后慢慢再对 Linux 命令进行扩展（例如：ping、iptables，等等），这样日积月累，你对 Linux 系统的命令行使用就越来越熟悉了。因为根据我进行Linux系统管理的经验，其实有很多时候在做系统管理上，还是命令行方式要比图形界面方式功能更加强大、灵活。

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