Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations

Abstract

Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and composition. Such a phenomenon is not surprising when one considers that binding is a dynamic process with populations in equilibrium and that the shape of the binding site is strongly influenced by the molecular partner. It derives implicitly from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in solution is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross-)drug resistance. A similar effect is observed in protein superfamilies, in which different sequences with similar topologies display similar large-scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, that is, the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding molecules can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse molecules. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.

Fig. 1.

The concept described in this mini-review. Around the native state, there is a range of conformational isomers, with low barriers separating between them. These conformational isomers largely reflect large-scale motions, an outcome of hinge-bending type movements. The geometry of the binding site reflects this variability. Depending on the library of ligands present in solution and on the conditions, the protein may selectively bind to these, still at the same binding site. The protein illustrated here is the tissue factor (Banner et al. 1996). The tissue factor is in complex with blood coagulation factor VIIa with the binding sites located in the hinge region (three residues on one domain, i.e., R131, L133, and F140, and six residues on another domain, i.e., T17, K20, I22, E24, E56, and D58). This figure is a simplification of the motion observed in the molecular dynamics simulations (also see Fig. 4 ▶). (A) The energy landscape of this protein, with the minima corresponding to different hinge-bending tissue factor conformers. The conformers dynamically interconvert in solution. Arrows are drawn connecting minima in the funnel and different respective hinge-bent conformers. (B) Each such conformer is bound to a different ligand. The different ligands present in solution are drawn at the top of the figure. On mixing with the tissue conformers, the ligands selectively choose different tissue factor conformers, shifting the equilibrium in favor of these conformers.

Fig. 1.

The concept described in this mini-review. Around the native state, there is a range of conformational isomers, with low barriers separating between them. These conformational isomers largely reflect large-scale motions, an outcome of hinge-bending type movements. The geometry of the binding site reflects this variability. Depending on the library of ligands present in solution and on the conditions, the protein may selectively bind to these, still at the same binding site. The protein illustrated here is the tissue factor (Banner et al. 1996). The tissue factor is in complex with blood coagulation factor VIIa with the binding sites located in the hinge region (three residues on one domain, i.e., R131, L133, and F140, and six residues on another domain, i.e., T17, K20, I22, E24, E56, and D58). This figure is a simplification of the motion observed in the molecular dynamics simulations (also see Fig. 4 ▶). (A) The energy landscape of this protein, with the minima corresponding to different hinge-bending tissue factor conformers. The conformers dynamically interconvert in solution. Arrows are drawn connecting minima in the funnel and different respective hinge-bent conformers. (B) Each such conformer is bound to a different ligand. The different ligands present in solution are drawn at the top of the figure. On mixing with the tissue conformers, the ligands selectively choose different tissue factor conformers, shifting the equilibrium in favor of these conformers.

Fig. 2.

The hinge bending of tissue factor, using an efficient, entirely automated, hinge-bending flexible structural comparison algorithm (Shatsky et al. 2000;Shatsky 2001). (A) A rigid superposition of the tissue factor 1a21 (chain A) and growth hormone binding protein 1hwg (chain C). According to SCOP (Murzin et al. 1995), both proteins belong to the fibronectin type III protein family. The proteins have two domains, each composed of two β-sheets. The sequence similarity between the proteins is minor. The coordinates are taken from the Protein Data Bank (Bernstein et al. 1977). (B) Flexible structural superposition. Four hinges are observed. These are labeled on the figure. Each rigid fragment is depicted in a different color. The superposition has been performed by FlexProt. The rigid fragment pairs and the flexible hinge region are detected simultaneously. No predefinition of the hinge sites is required. The program can be accessed at http://bioinfo3d.math.tau.ac.il/FlexProt.

Fig. 3.

Superposition of the inactive unphosphorylated state (NtrC^r) and the phosphorylated active state (P-NtrC^r). NtrC^r is the regulatory domain of NtrC, the nitrogen regulatory protein C, a signaling protein, acting as a molecular switch. The molecules have been superimposed using FlexProt, an automated, hinge-bending, flexible structural comparison algorithm (Shatsky et al. 2000;Shatsky 2001). (A) Comparison of the unphosphorylated state (green ribbon; Protein Data Bank [PDB] code, 1DC7; Bernstein et al. 1977) and the phosphorylated state (red ribbon; PDB code, 1DC8). (B). Comparison of the unphosphorylated state (green ribbon; PDB code, 1DC7) and another phosphorylated state (red ribbon; PDB code, 1NTR). The flexible structural comparison program can be accessed at http://bioinfo3d.math.tau.ac.il/FlexProt.

Fig. 4.

A schematic diagram illustrating how taking account of populations may aid in flexible docking schemes. The protein illustrated is the tissue factor. Tissue factor is in complex with blood coagulation factor VIIa with the binding sites located in the hinge region (three residues on one domain, i.e., R131, L133, and F140, six residues on another domain, i.e., T17, K20, I22, E24, E56, and D58; Banner et al. 1996). (A) An ensemble of molecules is generated via molecular dynamics simulations. (B) The molecular ensemble is superimposed, generating some clusters. (C) Representatives are chosen for each large-enough cluster (the highlighted residues are the three residues on the first domain, and the six residues on the second domain noted above). (D) Ligands are docked into the representatives. See also Fig. 1 ▶.