Crystallisation protein construct for Protein-Ligand Crystal complexes


The functional clues about various cellular processes in living organisms relies on the interaction of biological macromolecules, especially proteins with small molecules (ligands). A deeper understanding of these proteins – ligand interactions at molecular level is essential for answering the functional biological questions and key concepts required for structure based drug design.  The high-resolution structural data for protein-ligand complexes are generally obtained by either X-ray crystallography or nuclear magnetic resonance spectroscopy. These structural knowledge elucidate the molecular architecture for the protein of interest as well as convey experimental evidence of binding mode how the small molecule binds, which is of extremely beneficial during the design of lead molecules for the treatment of diseases.

The availability of the suitable crystallization construct for the protein of interest is the basis for generation of protein-ligand complex crystals for X-ray structural studies. In this blog, few of the key points focusing this area is mentioned. A recent review by (Muller, 2017) illustrates a flowchart (Fig.1) to achieve this key step.

Mohan 1

 

Fig 1: Adapted from Muller, 2017 Acta Crystallogr D Struct Biol D73, 79-92

The first step of protein construct design is of identifying a well-ordered structural domain for the protein of interest, avoiding the unstructured regions by carrying out detailed structure based sequence alignments using various bioinformatics domain prediction tools such as Pfam (Finn et al., 2016), pDomTHREADER (Lewis et al., 2013), DISOPRED (Ward et al., 2004) RONN (Yang et al., 2005) etc. An insilico-based validation of the initially designed protein domain for its ability to crystallise could also be analysed by XtalPRed-RF tool (Slabinski et al., 2007) is also a good choice.

Once the core domains are identified, the regions of disorder either between the domains or within the domains could be replaced with equivalent less flexible residue of the homologue proteins or by introduction of short flexible linkers which could also aid in the crystal formation. Few successful stories from the literature are listed here (Clifton et al., 2015, Ocasio et al., 2016, Blair et al., 2010, Muller, 2017).

It is also worth looking at the chemical modifications occurring for the protein of interest, which could also hinder the crystallisation process. For kinases production studies, several groups (Cowan-Jacob et al., 2007, Mace et al., 2013) were successful in obtaining higher yields of proteins of interest by altering the phosphorylation profiles with kinase inhibitors or removing the phosphorylation sites or phospho-mimetics.

The exploration of various affinity and solubility tags (Pina et al., 2014, Costa et al., 2014) to enhance the folding and solubility for the protein of interest to aid in protein purification could also speed up the whole crystallisation process

Not in all cases, but the presence of large flexible amino acids such as Lys, Glu, Gln on the protein surface could sometimes hinder the protein crystallisation. Surface entropy reduction (SER) provides solution to this by designing various mutant protein constructs replacing these flexible amino acids to smaller amino acids such as alanine found to be effective. On in-silico basis, SERp server (Goldschmidt et al., 2007) helps in identifying the amino acids for surface mutation to enhance the crystallisation probability.

Once the protein construct for crystallisation is designed based on the above parameters, the next step is to successfully prepare a homogenous preparation of pure protein for structural studies. There are many expression systems such as E. coli (Rosano & Ceccarelli, 2014), Yeast, Baculovirus (Unger & Peleg, 2012) or Mammalian for the production of protein of interest (Structural Genomics et al., 2008, Muller, 2017). The selection of resource efficient and suitable expression system for each protein of interest varies and it involved various experimental optimizations to be carried out.

In some cases, the addition of ligands during protein expression and/or purification (Muller, 2017) as well as thermal stability assays were also worth trying out during the production of proteins on large scale.

When the pure protein is available, the next task of generating protein-ligand crystal complexes can be achieved by soaking or co-crystallisation techniques.

The main requisite for soaking is the availability of good quality apo crystals for the protein of interest. As a general guide, 10-50 mM for fragments and 0.1 – 1mM for high molecular weight compounds could be used if Kd is not known (Muller, 2017), but these parameters varies a lot. The treatment of soaking crystals with stabilisation buffer, cross-linking with glutaraldehyde (Lusty, 1999), step-wise increase of ligand concentration during soaking, soaking incubation timings are some of the optimisations normally carried out for an efficient soaking (Hassell et al., 2007). A high-throughput automated technique for carrying out soaking experiments is also getting popular (Collins et al., 2017)

The process of co-crystallisation involves the pre-incubation of protein with the ligand of interest to make the protein-ligand complex before the crystallisation set up (Hassell et al., 2007, Muller, 2017). As a general rule of thumb, the compound concentration should be three times the Kd value (Muller, 2017). Optimisations involving varying the drop ratios (1:1,1:2, 2:1 for protein:well solution) (Ng et al., 2016), ‘dry’ co-crystallisation (Gelin et al., 2015) in which crystallisation wells are pre-coated with ligands before the crystallisation set up could speed up the entire process.

In general, the whole process of generating the protein–ligand complex crystals consists of various stages such as designing protein construct, protein production on large scale followed by crystallisation experiments with co-crystallisation and/or soaking and each stage involves trial and error methods for the better outcome.

Blog by Mohan Rajasekaran

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