(Not) getting misled by crystal structures: part 5 – conformational heterogeneity

It’s been a while since the last installment in our “getting misled” series. One of the key issues with crystallography is that ligands are almost always modeled as binding in a single conformation. This does not necessarily reflect reality, as we discussed here. Indeed, as described here and here, subtle changes can cause ligands to dramatically change their binding modes, which could reflect the fact that the initial ligand itself had multiple binding modes, and the change simply shifts the equilibrium. In an effort to proactively seek out disparate binding conformations, Henry van den Bedem and a group of collaborators from Stanford, UCSF, Schrödinger, and Université Paris-Saclay have created a new program, which they describe in J. Med. Chem. (See here for In The Pipeline’s discussion.)

The open-source program, called qFit-ligand, starts with an existing protein-ligand structure and an electron density map. It first breaks the ligand into rigid fragments (such as rings) and rotatable bonds. Each rigid group is then allowed to move around and rotate to fit the density. Of course, this might entail the rest of the molecule moving as well to avoid bumping into the protein; up to five positions are stored for each rigid group. Combinations that best match the electron density are retained: for a ligand with three rigid groups, 15 conformations would be considered. Importantly, the entire process is automated.

The researchers validated qFit-ligand against a set of 73 reasonably high-resolution structures from the protein data bank (PDB) that had included two different binding conformations; they started with just one of the reported conformations and used their program to find the second. qFit-ligand was very effective at identifying cases where a terminal portion of the molecule had flipped or rotated, though less so for more difficult cases such as displacement of the entire ligand.

Next, the researchers turned to the D3R dataset of 145 high-quality, manually curated crystal structures, where qFit-ligand correctly identified 7 of the 10 structures with alternate conformations, and even identified an alternative conformation for a ligand that had not previously been detected.

The researchers then examined a large set of crystal structures that had been flagged as potentially dubious, and found several could be improved by including alternate conformations. Similarly, an examination of all 126 crystal structures of BRD2-4 bromodomain-ligand complexes in the PDB revealed that 12 almost certainly had previously undetected alternate binding conformations; another 24 likely did.

qFit-ligand strikes me as a powerful tool for getting beyond the static picture usually presented by crystallography. Because the program is automated, the researchers note, it should be complementary to high-throughput approaches such as PanDDa (which we described here). Of course, using qFit-ligand effectively assumes that everyone is aware of the potential for both false positives and negatives. As the researchers conclude, “communication between structural biologists, computational chemists, and medicinal chemists remains a requisite for successful, rational design.”