This year’s CCP4 study weekend focused on providing an overview of the process and pipelines available, to take crystallographic diffraction data from spot intensities right through to structure. Therefore sessions included; processing diffraction data, phasing through molecular replacement and experimental techniques, automated model building and refinement. As well as updates to CCP4 and where is crystallography going to take us in the future?
Surrounding the meeting there was also a session for Macromolecular (MX) crystallography users of Diamond Light Source (DLS), which gave an update on the beamlines, and scientific software, as well as examples of how fragment screening at DLS has been used. The VMXi (Versatile Macromolecular X-tallography in-situ) beamline is being developed to image crystals that are forming in situ crystallisation plates. This should allow for crystallography to be optimized, as crystallization conditions can be screened, and data collected on experiments as they crystallise, especially helpful in cases where crystallisation has routinely led to non-diffracting crystals. VXMm is a micro/nanofocus MX beamline, which is in development, with a focus to get crystallographic from very small crystals (~300nm to 10 micron diameters, with a bias to the smaller size), thereby allowing crystallography of targets that have previously been hard to get sufficient crystals. Other updates included how technology developed for fast solid state data collection on x-ray free electron lasers (XFEL) can be used on synchrotron beamlines.
A slightly more in-depth discussion of two tools presented that were developed for use alongside and within CCP4, which might be of interest more broadly:
ConKit: A python interface for contact prediction tools
Contact prediction for proteins, at its simplest, involves estimating which residues within a certain certain spatial proximity of each other, given the sequence of the protein, or proteins (for complexes and interfaces). Two major types of contact prediction exist:
- Evolutionary Coupling
- Take a series of sequence homologues, and identifying co-evolved residues from multiple sequence alignment of the protein family. These co-evolved residues are hypothesized to share a functional dependence. Discussed previously on BLOPIG: Predicted protein contacts: is it the solution to (de novo) protein structure prediction?
- Supervised machine learning
- Using ab initio structure prediction tools, without sequence homologues, to predict which contacts exist, but with a much lower accuracy than evolutionary coupling.
ConKit is a python interface (API) for contact prediction tools, consisting of three major modules:
- Core: A module for constructing hierarchies, thereby storing necessary data such as sequences in a parsable format.
- Providing common functionality through functions that for example declare a contact as a false positive.
- Application: Python wrappers for common contact prediction and sequence alignment applications
- I/O: I/O interface for file reading, writing and conversions.
Contact prediction can be used in the crystallographic structure determination field, during unconventional molecular replacement, using a tool such as AMPLE. Molecular replacement is a computational strategy to solve the phase problem. In the typical case, by using homologous structures to determine an estimate a model of the protein, which best fits the experimental diffraction intensities, and thus estimate the phase. AMPLE utilises ab initio modeling (using Rosetta) to generate a model for the protein, contact prediction can provide input to this ab initio modeling, thereby making it more feasible to generate an appropriate structure, from which to solve the phase problem. Contact prediction can also be used to analyse known and unknown structures, to identify potential functional sites.
ACEDRG: Generating Crystallographic Restraints for Ligands
Small molecule ligands are present in many crystallographic structures, especially in drug development campaigns. Proteins are formed (almost exclusively) from a sequence containing a selection of 20 amino acids, this means there are well known restraints (for example: bond lengths, bond angles, torsion angles and rotamer position) for model building or refinement of amino acids. As ligands can be built from a much wider selection of chemical moieties, they have not previously been restrained as well during MX refinement. Ligands found in PDB depositions can be used as models for the model building/ refinement of ligands in new structures, however there are a limited number of ligands available (~23,000). Furthermore, the resolution of the ligands is limited to the resolution of the macro-molecular structure from which they are extracted.
ACEDRG utilises the crystallorgraphy open database (COD), a library of (>300,000) small molecules usually with atomic resolution data (often at least 0.84 Angstrom), to generate a dictionary of restraints to be used in refining the ligand. To create these restraints ACEDRG utilises the RDkit chemoinformatics package, generating a detailed descriptor of each atom of the ligands in COD. The descriptor utilises properties of each atom including the element name, number of bonds, environment of nearest neighbours, third degree neighbours that are aromatic ring systems. The descriptor, is stored alongside the electron density values from the COD. When a ACEDRG query is generated, for each atom in the ligand, the atom type is compared to those for which a COD structure is available, the nearest match is then used to generate a series of restraints for the atom.
ACEDRG can take a molecular description (SMILES, SDF MOL, SYBYL MOL2) of your ligand, and generate appropriate restraints for refinement, (atom types, bond lengths and angles, torsion angles, planes and chirality centers) as a mmCIF file. These restraints can be generated for a number of different probable conformations for the ligand, such that it can be refined in these alternate conformations, then the refinement program can use local scoring criteria to select the ligand conformation that best fits the observed electron density. ACEDRG can accessed through the CCP4i2 interface, and as a command line interface.
Hopefully a useful insight to some of the tools presented at the CCP4 Study weekend. For anyone looking for further information on the CCP4 Study weekend: Agenda, Recording of Sessions, Proceedings from previous years.