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.

1. Import a ligand/protein cocrystallized structure, typically from the Protein Data Bank, into Maestro.
2. Locate any waters you want to keep, then delete all others.

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.

Declaration

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.

Introduction

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.

Declaration

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.

Introduction

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.

Declaration

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.

Introduction

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.

Declaration

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.

Introduction

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.

Declaration

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.

Copying the Field-Based QSAR Exercise Files

1. 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

2. Unzip the files into your working directory.

3. Choose File → Change Working Directory in Maestro to set the working directory to where you unzipped the files, if needed.

Declaration

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.

Introduction

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.

Prepare the Protein Using the Protein Preparation Workflow

RESOURCES

Docker Desktop is available for Mac, Linux and Windows https://docs.docker.com/desktop/

Install Docker Engine on Linux https://docs.docker.com/engine/install/

View example projects that use Docker https://github.com/docker/awesome-compose/

Check out our docs for information on using Docker https://docs.docker.com/

What is discussed in this question?

During the process of research writing, we often need to cite certain references. With the aid of reference management tools, we can efficiently modify the style of citations to meet the requirements of various publishers. However, I'm puzzled about whether the in-text citation should be positioned before or after the final punctuation mark.

Before we delve into this question, there are some definitions that need to be clarified. The process of citing references generally involves two parts—in-text citations and the bibliography.

The bibliography, also called reference lists, a list of all sources used in your research, is typically placed at the end of the text, as below^[1]:

References

1. Berezin, M. Y. & Achilefu, S. Fluorescence lifetime measurements and biological imaging. Chem. Rev. 110, 2641–2684 (2010).
2. Kandori, H., Katsuta, Y., Ito, M. & Sasabe, H. Femtosecond fluorescence study of the rhodopsin chromophore in solution. J. Am. Chem. Soc. 117, 2669–2670 (1995).
3. Baba, M., Li, Y. & Matsuoka, M. Intensity interference of ultrashort pulsed fluorescence. Phys. Rev. Lett. 76, 4697–4700 (1996).
4. Muskens,O. L., Giannini, V., Sánchez-Gil, J. A. & Rivas, J. G. ómez Strong enhancement of the radiative decay rate of emitters by single plasmonic nanoantennas. Nano Lett. 7, 2871–2875 (2007). ...
5. McGlynn, J. A., Wu, N. & Schultz, K. M. Multiple particle tracking microrheological characterization: fundamentals, emerging techniques and applications. J. Appl. Phys. 127, 201101 (2020).
6. Ghosh, A., Karedla, N., Thiele, JanChristoph, Gregor, I. & Enderlein, J. örg. Fluorescence lifetime correlation spectroscopy: Basics and applications. Methods 140–141, 32–39 (2018).
7. Newville, M., Stensitzki, T., Allen, D. B. & Ingargiola, A. LMFIT: nonlinear least-square minimization and curve-fitting for Python. https://doi.org/10.5281/zenodo.11813 (2023).

Introduction

What is Anaconda Distribution?

Anaconda® Distribution is a free Python/R data science distribution that contains:

• conda - a package and environment manager for your command line interface
• Anaconda Navigator - a desktop application built on conda, with options to launch other development applications from your managed environments
• 250 automatically-installed packages that work well together out of the box
• access to the Anaconda Public Repository, with 8000 open-source data science and machine learning packages

Anaconda Distribution is free, easy to install, and offers free community support.