We have generated a highly selective cathepsin B probe and several less specific reagents for the study of cathepsin biology. The reagents have several advantages over commonly used fluorogenic substrates, allowing inhibitor targets to be identified in a pool of total cellular enzymes. We have used the probes to show that cathepsin activity is regulated in tumor tissues and during differentiation of placental-derived cytotrophoblasts to invasive cells required for establishing blood circulation in a developing embryo.

In this paper we describe the first all-atom aqueous-phase MD simulations of human carbonic anhydrase II in three protonation states relevant to the rate-limiting intramolecular proton-transfer step. In particular, we have examined the zinc−water form of the enzyme (CHOH), the zinc−hydroxide form of the enzyme with a doubly protonated His-64 (COHH, the putative intramolecular proton-transfer proton-accepting residue), and the native zinc−hydroxide form (COH) of the enzyme (i.e., with an unprotonated His-64). From these MD simulations (up to ∼1 ns in length) we have studied in detail the dynamics of these three systems. Overall the dynamics of the three systems do not vary significantly (e.g., the active site region is rigid, the number of long-lived hydrogen bonds is constant, etc.) with the exception of COHH. In this case the residues that line the entrance to the active site cavity (near the location of His-64) undergo significantly higher fluctuations than in the CHOH and COH cases. It is postulated that this facilitates solvent and buffer exchange around His-64, thereby facilitating the intermolecular proton-transfer step. We also find that the motion of His-64 is limited in all three cases to occupying the “in” orientation (∼7 Å from the zinc ion, while the so-called “out” conformer is further away), which suggests that fluctuations of this residue between the in and out conformers have a limited influence on the intramolecular proton transfer. However, due to the limited time scales of our simulations, this needs to be examined in more detail. Importantly, though, we find that His-64 acts as a “gate-keeper” between the inner active site region (characterized by localized water molecules) and the outer (bulk) region, which is characterized by relatively freely diffusing water molecules. This function of His-64 has not been realized previously. In the inner active site we have identified relatively long-lived water bridges between the zinc-bound water or hydroxide and the imidazole or imidazolium side chain of His-64. The lengths of these bridges vary between two and six water molecules, and the preferred bridge depends on the protonation of the active site. We estimate that the probability of water bridge formation is low (at most ∼1.5 kcal/mol) and that water bridge formation is not the rate-limiting step in the proton-transfer process (transfer from zinc-bound water to an active site water is rate-limiting).

Classifying proteins into functionally distinct families based only on primary sequence information remains a difficult task. We describe here a method to generate a large data set of small molecule affinity fingerprints for a group of closely related enzymes, the papain family of cysteine proteases. Binding data was generated for a library of inhibitors based on the ability of each compound to block active-site labeling of the target proteases by a covalent activity based probe (ABP). Clustering algorithms were used to automatically classify a reference group of proteases into subfamilies based on their small molecule affinity fingerprints. This approach was also used to identify cysteine protease targets modified by the ABP in complex proteomes by direct comparison of target affinity fingerprints with those of the reference library of proteases. Finally, experimental data were used to guide the development of a computational method that predicts small molecule inhibitors based on reported crystal structures. This method could ultimately be used with large enzyme families to aid in the design of selective inhibitors of targets based on limited structural/function information.

Matrix metalloproteinases (MMPs) represent a potentially important class of therapeutic targets for the treatment of diseases such as cancer. Selective inhibition of MMPs will be required given the high sequence identity across the family and the discovery that individual MMPs also regulate the natural angiogenesis inhibitor angiostatin. In this study, we have used computational methods to model the selectivity for six thiadiazole urea inhibitors with stromelysin-1 and gelatinase-A, two homologous MMPs that have been implicated in breast cancer. From continuum Generalized Born molecular dynamics (GB-MD) and MM-GBSA analysis, we estimated ligand free energies of binding using 200 snapshots obtained from a short 40 ps simulation of the relevant protein-ligand complex. The MM-GBSA free energies, computed from the continuum GB-MD trajectories, show strong correlation with the experimental affinities (r(2) = 0.74); prior studies have employed explicit water MD simulations. Including estimates for changes in solute entropy in the binding calculations slightly diminishes the overall correlation with experiment (r2 = 0.71). Notably, in every case, the simulation results correctly predict that a given ligand will bind selectively to stromelysin-1 over gelatinase-A which is gratifying given the high degree of structural homology between the two proteins. The increased selectivity for stromelysin-1 appears to be driven by (1) increased favorable van der Waals interactions, (2) increased favorable Coulombic interactions, and (3) decreased unfavorable total electrostatic energies (Coulombic plus desolvation).