When X-ray Crystallography Doesn't Give the Full Structure of a Protein: How Are the Remaining Residues Determined?

When working with protein structures, especially those determined by X-ray crystallography, it's not uncommon to encounter incomplete or missing residues. This can be due to various reasons, such as the flexibility of certain regions of the protein or low resolution. In this article, we will explore how these missing residues are typically determined and the computational methods used to complete the structure.

Introduction to Missing Residues in Protein Structures

X-ray crystallography is a powerful technique for determining the three-dimensional structure of proteins. However, there are cases where portions of the protein do not form well-ordered crystal lattice, leading to missing parts in the final model. Commonly, flexible loops, which are inherently ordered but fluctuating, are particularly challenging to resolve. This can result in gaps in the crystallographic electron density map, making it necessary to employ computational techniques to bridge these gaps.

Dealing with Missing Loops

For shorter flexible loops, typically less than 10 residues, computational methods such as structural modeling using software like MODELLER can provide a useful starting point. MODELLER uses homology modeling to predict the missing parts of the protein based on known structures in the Protein Data Bank (PDB).

However, it is crucial to recognize that flexible regions are often dynamic in nature. While a single conformation predicted by MODELLER may be a reasonable approximation, it is often a starting point for more detailed analysis rather than a definitive solution. For well-characterized flexible regions, performing a molecular dynamics (MD) simulation can provide insight into the most probable conformations the protein adopts under physiological conditions.

Handling Missing Hydrogens

Hydrogen atoms, while not as problematic as missing backbones, are frequently not resolved in X-ray crystallography due to their small size and low electron density. To address this, computational tools can automatically position hydrogens based on predefined bond lengths and angles. This process is typically followed by an energy minimization step to refine the hydrogen positions and ensure they fit within the overall protein structure.

For cases where only a few atoms are missing, using another experimental method to fill in these gaps may be overkill. In these instances, computational approaches are often sufficient to provide a complete and accurate model.

Conclusion and Further Reading

In summary, when X-ray crystallography does not provide a complete protein structure, computational methods are a valuable tool in completing the model. This includes structural modeling for missing loops and hydrogen placement from known bond parameters. Understanding these techniques is essential for researchers working in structural biology and computational modeling.

For a deeper dive into the subject, you may want to explore the following resources:

Homology Modeling: BMC Structural Biology Molecular Dynamics Simulations: Frontiers in Computational Biology and Medicine X-ray Crystallography: Barnard College Tutorials

By leveraging these resources and computational tools, researchers can effectively determine and model the complete structure of proteins, even in challenging cases where X-ray crystallography alone is insufficient.