尾静脉注射是小鼠实验最常见的给药方式之一，也是较难掌握的小鼠实验技能。我在很长一段时间内，也是一直没有掌握到实验精髓。
在早期实验条件差的时候，我们都是先把小鼠麻醉，然后用 250 mL 或是 500 mL 的烧杯扣住，把小鼠的尾巴从杯嘴漏出来，接着用 1 mL 注射器给药。在如此条件下，我们实验室历史上仍然有两三个人能取得较高的成功率，每一位一到实验都是炙手资源，可谓是实验的大救星。我本人也曾尝试努力钻研相关技巧，一度能达到尾静脉注射 C57 有近 50% 的成功率，甚至颇为自豪，可惜的是一个月后的才开始正式实验，生疏许久之后手感全无，无奈最后还是请来帮手。至此，只能放弃认命，而我提升实验室地位的尝试也就此失败。
后来到了一个新实验室，老板斥资买了一个小鼠尾静脉注射仪，摸索了一下，发现相当实用，熟练后一针成功率估计有 80%，两针基本都能打进，不经感叹“科技改变生活”。很早以前在网上听过某个老师讲一句话，大意是“科研 = IT = Ideas + Tools”，如今想来，感慨万千，不经潸然泪下。
最近换了个路由器，不过只能刷 OpenWrt 固件。我之前用的都是 Padavan，几乎可以一键配置 IPv6，加上有 IPv6 公网，非常方便我在外使用远程桌面。不过换成 OpenWrt 后，前后折腾了一个多星期，才搞定了 OpenWrt IPv6 中继。
这里稍微介绍一些 IPv6 基础知识，同时记录一下 OpenWrt 开启 IPv6 中继实现过程。
IPv6 地址的长度为 128 bits（2128），由八组 16 位字段组成，相邻字段用冒号分隔，如：
我在 2017 年或者更早，就搭建了自己的独立博客，那时主要写一些随笔，更的很少很慢。研究生和工作之后，开始转向写一些技术内容，当然了，主要是一些学习笔记，毕竟我还没有到能独立输出知识的地步。同时博客的更新频率也相对多了些。一方面，我关注的方向比较多，涉猎广泛杂乱；另一方面，我逐渐养成整理知识体系的习惯，并使用了一些 Markdown 类的管理工具，方便转成博客。
属于独立博客的时代已经过去了。我所坚持的似乎是一件很老套的事情，就如同在精心维护一片野花园，却处在偏远山中，少有人游览。但我觉得呢，那些花儿本身，就值得被栽种盛开。
我想，写博客的目的，首先是记录。“那些很渺小的东西，都是我人生的大事。”我始终保持对文字的敬畏，我们应该记录和表达一些东西。只可惜，我好像能静下来思考的时间越来越少，我好久没能写一篇完整的随笔。
其次，写博客的目的是共享。我很乐意分享我学习和总结到的一些东西，并期待在提供帮助的同时，也能收到一些反馈。从后台统计看，我很高兴一些内容似乎帮助到了某些陌生人，至少，对 AI 起了帮助。
此外，我认为比无知更可怕的是误解。我担心在传递这些内容的同时，由于我的不足也传递了谬误。所以，亲爱的陌生人，如果您有任何的疑问，请一定不要吝惜与我交流（ jiangshen@outlook.com ）。
Declaration: The article was reprinted from The Protein Preparation Process.
The preparation of a protein involves a number of steps, which are outlined below. The procedure assumes that the initial protein structure is in a PDB-format file, includes a cocrystallized ligand, and does not include explicit hydrogens. The result is refined, hydrogenated structures of the ligand and the ligand-receptor complex, suitable for use with other Schrödinger products. In many cases, not all of the steps outlined below need to be performed.
Water molecules that mediate receptor-ligand interactions (so-called "structural waters" that bridge the receptor and ligand by way of H-bonds) can be retained during target preparation. In the Glide docking experiment, these waters will be retained and treated as part of the receptor environment — for example, a ligand H-bond to a water molecule will receive an energetic reward, the exact value of which depends on interaction geometry and the surrounding environment (not unlike a ligand H-bond to a protein residue).
During target preparation, you will need to make an informed decision about which water molecules to retain in the active site and which water molecules should be deleted before the docking experiment is carried out. Among other things, deleting unnecessary water molecules allows the active site to accommodate novel ligands that wouldn't otherwise fit.
One way of making these informed decisions is by consulting publications that describe the active site. There are also computational tools that can help in deciding which water molecules to retain. One such computational method is to align different PDB structures of the same target, color the structures by entry number in the Workspace, and look for highly conserved water molecules. The idea here is that highly conserved water molecules are important for binding.
It is known that in some targets, a structural water can be replaced by a ligand with a functional group that forms the same H-bonds to the receptor that the water molecule did. If you suspect this may be the case for the prepared target, you may choose to retain or displace the water molecule depending on the chemotype of the ligands being docked. Such instances can be treated by preparing two versions of the target - one that retains the water and one that removes it. A single ligand library can then be docked against both target models in a single experiment using our Virtual Screening Workflow interface, which automatically sorts and filters the results.
Note that the Glide SP and XP scoring functions both include terms that are designed to account for solvation of the active site. Thus, water molecules do not need to be added to the active site in order to obtain an estimate of desolvation effects. For example, the energetics of desolvation account for the extra reward term that is incurred by hydrophobic ligand groups that are fully enclosed by hydrophobic receptor residues. Glide XP further accounts for the energetics of desolvation by placing so-called "virtual waters" in the active site to estimate water displacement and ligand-solvent interactions.
These waters are identified by the oxygen atom, and usually do not have hydrogens attached. Generally, all waters (except those coordinated to metals) are deleted, but waters that bridge between the ligand and the protein are sometimes retained. If waters are kept, hydrogens are added to them in the preparation process.
Refer to https://www.schrodinger.com/kb/31.
This note is based on the article, “Enumeration Tools for Library Design”1, and created with the Schrödinger Software Release 2023-4.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
In this tutorial, you will learn how to use various enumeration tools in Maestro to design libraries for the lead optimization stage of a CDK2 inhibitor drug discovery project. In addition to building libraries, you will learn some workflows for library curation and enrichment.
This note is based on the article, “Rapid Screening of Chemical Libraries with GPU Shape”1, and created with the Schrödinger Software Release 2023-4.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
In this tutorial, you will learn how to perform rapid shape-based screening of a chemical library with Shape GPU. We will use information from nearly 70 CDK2 small-molecule inhibitors to evaluate a library of compounds provided by DUD-E for their propensity to bind CDK2 (http://dude.docking.org/). We will then run a screen on GPU using Shape GPU, and perform enrichment calculations using the true actives in the dataset as provided by DUD-E.
This note is based on the article, “Structure-Based Virtual Screening Using Phase”1, and created with the Schrödinger Software Release 2023-4.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
This tutorial demonstrates the creation, validation, and application of pharmacophore hypotheses to recognize common protein-ligand interactions and use them in virtual screening. You will learn how to create a pharmacophore hypothesis using a protein-ligand complex, how to modify a pharmacophore hypothesis to bias by experimental observables and to screen against the hypothesis to identify Leukotriene-A4 hydrolase inhibitors.
This note is based on the article, “Ligand-Based Virtual Screening Using Phase”1, and created with the Schrödinger Software Release 2023-4.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
This tutorial demonstrates the creation of pharmacophore hypotheses from both congeneric and diverse ligands sets. You will learn how to create a pharmacophore hypothesis from a congeneric set of ligands with known experimental binding affinity. Additionally, you will learn how to create a Phase Database from a set of ligands, and use it to both prepare and filter a ligand library for future ligand screens or docking. Lastly, you will screen a Phase Database against a set of hypotheses, generate a pharmacophore hypothesis from a diverse ligand set, and visualize the binding modes.
This tutorial is based on the Schrödinger Product Documentation, “Field-based QSAR”1, and created with the Schrödinger Software Release 2023-4.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
Use the following link to download the zip archive that contains the tutorial files: https://content.schrodinger.com/quick_start_guide/current/field_qsar.zip
Unzip the files into your working directory.
Choose File → Change Working Directory in Maestro to set the working directory to where you unzipped the files, if needed.
This note contains only minimal annotations to the original text, along with corrections to formatting errors. It is intended for educational and communicative purposes only, and all rights remain with the original author.
Glide is a Schrödinger module that performs ligand-receptor docking reliably. To run a Glide virtual screen, you need a grid file and a ligand file. The grid file is typically generated from a prepared protein (using Protein Preparation Workflow and Receptor Grid Generation), and the ligand file is processed by LigPrep.