Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

A major uncertainty in binding free energy estimates for protein−ligand complexes by methods such as MM-PB(GB)SA or docking scores results from neglecting or approximating changes in the configurational entropies (ΔS config. ) of the solutes. In MM/PB(GB)SA-type calculations, ΔS config. has usually been estimated in the rigid rotor, harmonic oscillator approximation. Here, we present the development of a computationally efficient method (termed BEERT) to approximate ΔS config. in terms of the reduction in translational and rotational freedom of the ligand upon protein−ligand binding (ΔS R/T ), starting from the flexible molecule approach. We test the method successfully in binding affinity computations in connection with MM-PBSA effective energies describing changes in gas-phase interactions and solvation free energies. Compared to related work by Ruvinsky and co-workers, clustering bound ligand poses based on interactions with the protein rather than structural similarity of the poses, and an appropriate averaging over single entropies associated with an individual well of the energy landscape of the protein−ligand complex, were found to be crucial. Employing three data sets of protein−ligand complexes of pharmacologically relevant targets for validation, with up to 20, in part related ligands per data set, spanning binding free energies over a range of ≤7 kcal mol −1 , reliable and predictive linear models to estimate binding affinities are obtained in all three cases (R 2 = 0.54−0.72, p < 0.001, root mean squared error S = 0.78−1.44 kcal mol −1 ; q 2 = 0.34−0.67, p < 0.05, root mean squared error s PRESS = 1.07−1.36 kcal mol −1 ). These models are markedly improved compared to considering MM-PBSA effective energies alone, scoring functions, and combinations with ΔS config. estimates based on the number of rotatable bonds, rigid rotor, harmonic oscillator approximation, or interaction entropy method. As a limitation, our method currently requires a target-specific training data set to identify appropriate scaling coefficients for the MM-PBSA effective energies and BEERT ΔS R/T . Still, our results suggest that the approach is a valuable, computationally more efficient complement to existing rigorous methods for estimating changes in binding free energy across structurally (weakly) related series of ligands binding to one target.

Novel peptidic dual agonists of the glucagon-like peptide 1 (GLP-1) and glucagon receptor are reported to have enhanced efficacy over pure GLP-1 receptor agonists with regard to treatment of obesity and diabetes. We describe novel exendin-4 based dual agonists designed with an activity ratio favoring the GLP-1 versus the glucagon receptor. As result of an iterative optimization procedure that included molecular modeling, structural biological studies (X-ray, NMR), peptide design and synthesis, experimental activity, and solubility profiling, a candidate molecule was identified. Novel SAR points are reported that allowed us to fine-tune the desired receptor activity ratio and increased solubility in the presence of antimicrobial preservatives, findings that can be of general applicability for any peptide discovery project. The peptide was evaluated in chronic in vivo studies in obese diabetic monkeys as translational model for the human situation and demonstrated favorable blood glucose and body weight lowering effects.

The periplasmic polysulfide-sulfur transferase (Sud) protein encoded by Wolinella succinogenes is involved in oxidative phosphorylation with polysulfide-sulfur as a terminal electron acceptor. The polysulfide-sulfur is covalently bound to the catalytic Cys residue of the Sud protein and transferred to the active site of the membranous polysulfide reductase. The solution structure of the homodimeric Sud protein has been determined using heteronuclear multidimensional NMR techniques. The structure is based on NOE-derived distance restraints, backbone hydrogen bonds, and torsion angle restraints as well as residual dipolar coupling restraints for a refinement of the relative orientation of the monomer units. The monomer structure consists of a five-stranded parallel beta-sheet enclosing a hydrophobic core, a two-stranded antiparallel beta-sheet, and six alpha-helices. The dimer fold is stabilized by hydrophobic residues and ion pairs found in the contact area between the two monomers. Similar to rhodanese enzymes, Sud catalyzes the transfer of the polysulfide-sulfur to the artificial acceptor cyanide. Despite their similar functions and active sites, the amino acid sequences and structures of these proteins are quite different.