Dual regulatory switch confers tighter control on HtrA2 proteolytic activity.

High-temperature requirement protease A2 (HtrA2), a multitasking serine protease that is involved in critical biological functions and pathogenicity, such as apoptosis and cancer, is a potent therapeutic target. It is established that the C-terminal post-synaptic density protein, Drosophila disc large tumor suppressor, zonula occludens-1 protein (PDZ) domain of HtrA2 plays pivotal role in allosteric modulation, substrate binding and activation, as commonly reported in other members of this family. Interestingly, HtrA2 exhibits an additional level of functional modulation through its unique N-terminus, as is evident from 'inhibitor of apoptosis proteins' binding and cleavage. This phenomenon emphasizes multiple activation mechanisms, which so far remain elusive. Using conformational dynamics, binding kinetics and enzymology studies, we addressed this complex behavior with respect to defining its global mode of regulation and activity. Our findings distinctly demonstrate a novel N-terminal ligand-mediated triggering of an allosteric switch essential for transforming HtrA2 to a proteolytically competent state in a PDZ-independent yet synergistic activation process. Dynamic analyses suggested that it occurs through a series of coordinated structural reorganizations at distal regulatory loops (L3, LD, L1), leading to a population shift towards the relaxed conformer. This precise synergistic coordination among different domains might be physiologically relevant to enable tighter control upon HtrA2 activation for fostering its diverse cellular functions. Understanding this complex rheostatic dual switch mechanism offers an opportunity for targeting various disease conditions with tailored site-specific effector molecules.

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

Epik: a software program for pK( a ) prediction and protonation state generation for drug-like molecules.

Epik is a computer program for predicting pK(a) values for drug-like molecules. Epik can use this capability in combination with technology for tautomerization to adjust the protonation state of small drug-like molecules to automatically generate one or more of the most probable forms for use in further molecular modeling studies. Many medicinal chemicals can exchange protons with their environment, resulting in various ionization and tautomeric states, collectively known as protonation states. The protonation state of a drug can affect its solubility and membrane permeability. In modeling, the protonation state of a ligand will also affect which conformations are predicted for the molecule, as well as predictions for binding modes and ligand affinities based upon protein-ligand interactions. Despite the importance of the protonation state, many databases of candidate molecules used in drug development do not store reliable information on the most probable protonation states. Epik is sufficiently rapid and accurate to process large databases of drug-like molecules to provide this information. Several new technologies are employed. Extensions to the well-established Hammett and Taft approaches are used for pK(a) prediction, namely, mesomer standardization, charge cancellation, and charge spreading to make the predicted results reflect the nature of the molecule itself rather just for the particular Lewis structure used on input. In addition, a new iterative technology for generating, ranking and culling the generated protonation states is employed.