Computational modeling of GPCRs: What are the challenges?

GPCRs are extremely important proteins both for pure and applied science research, but they are also very difficult to crystallize and hence structural information on them has been sparse. Naturally in such a case, computational modeling can be expected to be of great value of providing insight into GPCR structure and function. However, even though progress has been impressive, such modeling still has to overcome many challenges. A recent review lists some of them.

Firstly, in the absence of crystal structure, homology modeling wherein a sequence for an unknown structure is 'threaded' through that of a known one is well-established as a valuable technique. However the technique is tricky. First and foremost one has to get the right sequence alignment between the target and the template. As the article notes, recent studies have suggested that using multiple structures for alignment instead of a single one provides better results. Particularly noteworthy is this detailed study. Once a homology model has been obtained, it must be meticulously examined, both for internal consistency (bad contacts, incorrect hydrogen bonding interactions etc.) and for its agreement with experiment. Data from cross-linking studies and mutagenesis can be used to achieve this. A recent promising development has been termed 'ligand-supported homology modeling'. In this process, topographical protein-ligand interaction data from mutagenesis and other studies is used to limit the number of homology models. Such data-driven homology modeling is becoming increasingly popular.

Once a good homology model has been obtained, many things can be done with it. Molecular dynamics (MD) simulations provide a very valuable avenue for exploring protein motion and be used to detect structural features not obvious in static models. A recent MD simulation of the beta-adrenergic receptor helped to resolve discrepancies between biochemical and structural observations. MD simulations can be used to investigate protein dynamics and to refine the models. Several challenges present themselves during this procedure. Firstly, while helices in GPCRs can be well-modeled, loops (of which there are six- three intracellular and three extracellular) are much harder to model because of their higher flexibility and because they are often ill-resolved in crystal structures. Unfortunately, it's these loops which are important ligand-interacting elements, so getting them right is key. Recently developed algorithms for loop-refinement based on either first-principles energy minimization or by statistical modeling based on a database of known loop conformations have been used in getting loops right. Also, state-of-the-art long MD simulations spanning several microseconds can be used to model large-scale structural changes in GPCRs.

There are still immense challenges still to be overcome in understanding GPCRs. One of the biggest concerns the cycling between several inactive and active states (and not just one active and one inactive state) that present often conflicting features that can be subject to varying interpretation. For instance, for class A GPCRs (which is the largest class), it has been well-established that activated states involve the breakage of the "ionic lock", a salt bridge between arginines and glutamates on transmembrane helices 6 and 3. Breaking this lock allows TM6 to shift away from TM3 and towards TM5, a hallmark of GPCR activation. Yet the MD study on the beta2 cited above indicated that even an inactive state may feature breakage of this lock.

In the GPCR jungle, strange shape-shifting creatures appear and clutch gems of insight in their palms. It is only fitting that we throw the kitchen sink at them to unravel their secrets, and computational techniques can only be a valuable arrow in this quiver.

Yarnitzky T, Levit A, & Niv MY (2010). Homology modeling of G-protein-coupled receptors with X-ray structures on the rise. Current opinion in drug discovery & development, 13 (3), 317-25 PMID: 20443165