Field of Science

Computational modeling of GPCRs: not too bad
GPCRs constitute one of the most important family of proteins in our body, both for their innate importance in signal transduction and neurotransmission, and as important targets for drugs. Many of the important drugs on the market today target GPCRs. And yet there is an unusual gap between knowledge and application when it comes to this important family. That's because only two crystal structures of GPCRs are known. And one of them was derived last year, so there's been a real dearth of structural information about GPCRs for a long time.

We do know something about many GPCRs, however. We know that they are 7-TM receptor-spanning proteins. And the two structures we do know about shed valuable insight on GPCR function. One is rhodopsin which has been around for a while. Then there was big news last year about the second important GPCR whose structure was determined- the ß-2 adrenergic receptor.

Given the paucity of structural information and the availability of two structures, a logical question is whether computational modeling can teach us something new about GPCRs whose structure is unknown. To this end, Stefano Costanzi at the NIH did a nice set of experiments which he published in J. Med. Chem. He attempted to build a homology model of the adrenergic receptor based on the sequence and structure of rhodopsin. Since we now have a crystal structure of the adrenergic GPCR, we have something concrete to compare modeled structures and ligand orientations to.

Costanzi was particularly interested in knowing how a small molecule-carazolol- binds to the modeled GPCR. This is important both from a structural and functional drug-discovery point of view. His results indicate that we can do pretty well. In essence, he built two models of the receptor, one of them de novo. While the models were similar to rhodopsin in the conserved regions, the important differences were with respect to a loop that flaps on top of the protein. In one model the loop was buried inside the binding pocket, and in the other one it was open. Docking of carazolol into the buriled-loop model using the Glide program from Schrodinger gave a binding pose in which the ligand was, not surprisingly, buried deeper into the cavity compared to the crystal structure. This was naturally the effect of the loop blocking part of the pocket. The other model in which the loop was not buried gave much better results. Curiously, the ligand was buried a little deep in the pocket even in this model, even though it was much less buried compared to the previous one. It still misaligned considerably with the experimental pose. Inspection revealed that there was a Phe in the pocket which was anti in the model but +gauche in the crystal structure. Since the corresponding residue in rhodopsin was Ala, there was no way this unusual conformation could have been predicted ab initio. Fixing the conformation of this residue to +gauche suddenly gave excellent alignment with the ligand orientation in the crystal structure.

An instructive piece of work that shows that homology modeling and docking of ligands into GPCRs of unknown structure can be fruitful. However, it also indicates caveats like the Phe conformation which are hard to account for de novo. However, since structures of members in this important family of proteins are unavailable anyway, even some predictive ability might be welcome in this area.

Costanzi, S. (2008). On the Applicability of GPCR Homology Models to Computer-Aided Drug Discovery: A Comparison between In Silico and Crystal Structures of the ß2-Adrenergic Receptor. Journal of Medicinal Chemistry DOI: 10.1021/jm800044k


  1. Nice Post!
    Good to see what you can achieve with a well constructed model.

  2. have a nice site by the way. Pretty good write-ups.

  3. The better computational modeling becomes, the better the drugs we are likely to have. However, you have to know the actual target of the drug in a living organism to model the interaction.

    Drugs designed for one target (or which can be shown to bind to a given target < if it was discovered before computers got so fast > ) may have additional quite unrelated effects in a living system. For an example see the current chemiotics post on the Skeptical Chymist.


  4. There are some new interesting similarity-searching methods which can sometimes look for compounds "similar" to known inhibitors for which the target is not known. These ligand-based drug design approaches are slowly becoming important in their own right. One of the powerful new methods is ROCS, which compares structures by volume overlap and assigns similarity scores to them.

  5. I'm glad you blogged this, because there needs to be increased analysis and thought in this very complex area - see: How our cellular receptors function on the molecular level


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