Category Archives: X-ray Crystallography

Fragment Based Drug Discovery with Crystallographic Fragment Screening at XChem and Beyond

Disclaimer: I’m a current PhD student working on PanDDA 2 for Frank von Delft and Charlotte Deane, and sponsored by Global Phasing, and some of this is my opinion – if it isn’t obvious in one of the references I probably said it so take it with a pinch of salt

Fragment Based Drug Discovery

Principle

Fragment based drugs discovery (FBDD) is a technique for finding lead compounds for medicinal chemistry. In FBDD a protein target of interest is identified for inhibition and a small library, typically of a few hundred compounds, is screened against it. Though these typically bind weakly, they can be used as a starting point for chemical elaboration towards something more lead-like. This approach is primarily contrasted with high throughput screening (HTS), in which an enormous number of larger, more complex molecules are screened in order to find ones which bind. The key idea is recognizing that the molecules in these HTS libraries can typically be broken down into a much smaller number of common substructures, fragments, so screening these ought to be more informative: between them they describe more of the “chemical space” which interacts with the protein. Since it first appeared about 25 years ago, FBDD has delivered four drugs for clinical use and over 40 molecules to clinical trials.

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Model validation in Crystallographic Fragment Screening

Fragment based drug discovery is a powerful technique for finding lead compounds for medicinal chemistry. Crystallographic fragment screening is particularly useful because it informs one not just about whether a fragment binds, but has the advantage of providing information on how it binds. This information allows for rational elaboration and merging of fragments.

However, this comes with a unique challenge: the confidence in the experimental readout, if and how a fragment binds, is tied to the quality of the crystallographic model that can be built. This intimately links crystallographic fragment screening to the general statistical idea of a “model”, and the statistical ideas of goodness of fit and overfitting.

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Real Space Correlation Coefficient

Introduction

In crystalography we are often faced with the question of how well a part of our model fits the data. Now crystalography has well developed probability models for the reflection amplitudes given then entire fitted model, but these do not provide a metric for “how much of the ligand is inside the blob”. This is because the reflection based models are inherently global.

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GEMMI: A Python Cookbook

General MacroMocelecular I/O, or GEMMI, is a C++ 11 header only library for low level crystalographic .

Because its header only it is certainly the easiest to access and use low level crystalographic C++ library, however GEMMI comes with python binding via Pybind11, making it arguably the easiest low level crystalographic library to access and use in python as well!

What follows is a cookbook of useful Python code that uses GEMMI to accomplish macromolecular crystalographic tasks.

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Coronavirus

A zoonosis is an infectious disease that has jumped from a non-human animal to humans.

A painting by David S. Goodsell showing coronavirus in pink and purple. Secreted mucus (greenish threads) and antibodies (yellow/orange Y-shapes), and several small immune systems proteins (orange) from the lungs’ respiratory cells surround it. © 2020, David S. Goodsell.

The coronavirus disease 2019 (COVID-19) is one such zoonosis, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS coronavirus 2, SARS-CoV-2, or 2019-nCoV). This is very similar to the SARS virus that emerged in 2003. Its recent emergence has resulted in a WHO-declared public health emergency of international concern.

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