Development of a universal method for vibrational analysis of the terminal alkyne C≡C stretch.

The terminal alkyne C≡C stretch has a large Raman scattering cross section in the "silent" region for biomolecules. This has led to many Raman tag and probe studies using this moiety to study biomolecular systems. A computational investigation of these systems is vital to aid in the interpretation of these results. In this work, we develop a method for computing terminal alkyne vibrational frequencies and isotropic transition polarizabilities that can easily and accurately be applied to any terminal alkyne molecule. We apply the discrete variable representation method to a localized version of the C≡C stretch normal mode. The errors of (1) vibrational localization to the terminal alkyne moiety, (2) anharmonic normal mode isolation, and (3) discretization of the Born-Oppenheimer potential energy surface are quantified and found to be generally small and cancel each other. This results in a method with low error compared to other anharmonic vibrational methods like second-order vibrational perturbation theory and to experiments. Several density functionals are tested using the method, and TPSS-D3, an inexpensive nonempirical density functional with dispersion corrections, is found to perform surprisingly well. Diffuse basis functions are found to be important for the accuracy of computed frequencies. Finally, the computation of vibrational properties like isotropic transition polarizabilities and the universality of the localized normal mode for terminal alkynes are demonstrated.

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-like 2 (Nrf2). Guided by X-ray crystallography, NMR, modeling, and machine learning, we show that the full 20 nM binding affinity of Nrf2 for KEAP1 can be recapitulated in a cyclic 7-mer peptide, c[(D)-ß-homoAla-DPETGE]. This compound was identified from the Nrf2-derived linear peptide GDEETGE (KD = 4.3 µM) solely by optimizing the conformation of the cyclic compound, without changing any KEAP1 interacting residue. X-ray crystal structures were determined for each linear and cyclic peptide variant bound to KEAP1. Despite large variations in affinity, no obvious differences in the conformation of the peptide binding residues or in the interactions they made with KEAP1 were observed. However, analysis of the X-ray structures by machine learning showed that locations of strain in the bound ligand could be identified through patterns of subangstrom distortions from the geometry observed for unstrained linear peptides. We show that optimizing the cyclic peptide affinity was driven partly through conformational preorganization associated with a proline substitution at position 78 and with the geometry of the noninteracting residue Asp77 and partly by decreasing strain in the ETGE motif itself. This approach may have utility in dissecting the trade-off between conformational preorganization and strain in other ligand-receptor systems. We also identify a pair of conserved hydrophobic residues flanking the core DxETGE motif which play a conformational role in facilitating the high-affinity binding of Nrf2 to KEAP1.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2).

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.

Probing the origin of structural stability of single and double stapled p53 peptide analogs bound to MDM2.

The stabilization of secondary structure is believed to play an important role in the peptide-protein binding interaction. In this study, the α-helical conformation and structural stability of single and double stapled all-hydrocarbon cross-linked p53 peptides when bound and unbound to MDM2 are investigated. We determined the effects of the peptide sequence, the stereochemistry of the cross-linker, the conformation of the double bond in the alkene bridge, and the length of the bridge, to the relative stability of the α-helix structure. The binding affinity calculations by WaterMap provided over one hundred hydration sites in the MDM2 binding pocket where water density is greater than twice that of the bulk, and the relative value of free energy released by displacing these hydration sites. In agreement with the experimental data, potentials of mean force obtained by weighted histogram analysis methods indicated the order of peptides from lowest to highest binding affinity. Our study provides a comprehensive rationalization of the relationship between peptide stapling strategy, the secondary structural stability, and the binding affinity of p53/MDM2 complex. We hope our efforts can help to further the development of a new generation p53/MDM2 inhibitors that can reactivate the function of p53 as tumor suppressor gene.

The evolution of catalytic function in the HIV-1 protease.

The evolution of species is a complex phenomenon based on the optimization of a multidimensional function referred to as fitness. At the level of biomolecular evolution, the fitness function can be reduced to include physiochemical properties relevant to the biological function of a particular molecule. In this work, questions involving the physical-chemical mechanisms underlying the evolution of HIV-1 protease are addressed through molecular simulation and subsequent analysis of thermodynamic properties related to the activity of the enzyme. Specifically, the impact of 40 single amino acid mutations on the binding affinity toward the matrix/capsid (MA/CA) substrate and corresponding transition state intermediate has been characterized using a molecular mechanics Poisson-Boltzmann surface area approach. We demonstrate that this approach is capable of extracting statistically significant information relevant to experimentally determined catalytic activity. Further, no correlation was observed between the effect of mutations on substrate and transition state binding, suggesting independent evolutionary pathways toward optimizing substrate specificity and catalytic activity. In addition, a detailed analysis of calculated binding affinity data suggests that ground-state destabilization (reduced binding affinity for the substrate) could be a contributing factor in the evolutionary optimization of HIV-1 protease. A numerical model is developed to demonstrate that ground-state destabilization is a valid mechanism for activity optimization given the high concentrations of substrate experienced by the functional enzyme in vivo.