Pancreatic polypeptide is recognized by two hydrophobic domains of the human Y4 receptor binding pocket.

Structural characterization of the human Y4 receptor (hY4R) interaction with human pancreatic polypeptide (hPP) is crucial, not only for understanding its biological function but also for testing treatment strategies for obesity that target this interaction. Here, the interaction of receptor mutants with pancreatic polypeptide analogs was studied through double-cycle mutagenesis. To guide mutagenesis and interpret results, a three-dimensional comparative model of the hY4R-hPP complex was constructed based on all available class A G protein-coupled receptor crystal structures and refined using experimental data. Our study reveals that residues of the hPP and the hY4R form a complex network consisting of ionic interactions, hydrophobic interactions, and hydrogen binding. Residues Tyr(2.64), Asp(2.68), Asn(6.55), Asn(7.32), and Phe(7.35) of Y4R are found to be important in receptor activation by hPP. Specifically, Tyr(2.64) interacts with Tyr(27) of hPP through hydrophobic contacts. Asn(7.32) is affected by modifications on position Arg(33) of hPP, suggesting a hydrogen bond between these two residues. Likewise, we find that Phe(7.35) is affected by modifications of hPP at positions 33 and 36, indicating interactions between these three amino acids. Taken together, we demonstrate that the top of transmembrane helix 2 (TM2) and the top of transmembrane helices 6 and 7 (TM6-TM7) form the core of the peptide binding pocket. These findings will contribute to the rational design of ligands that bind the receptor more effectively to produce an enhanced agonistic or antagonistic effect.

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions.

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with ROSETTA. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several ROSETTA protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

RosettaEPR: rotamer library for spin label structure and dynamics.

An increasingly used parameter in structural biology is the measurement of distances between spin labels bound to a protein. One limitation to these measurements is the unknown position of the spin label relative to the protein backbone. To overcome this drawback, we introduce a rotamer library of the methanethiosulfonate spin label (MTSSL) into the protein modeling program Rosetta. Spin label rotamers were derived from conformations observed in crystal structures of spin labeled T4 lysozyme and previously published molecular dynamics simulations. Rosetta's ability to accurately recover spin label conformations and EPR measured distance distributions was evaluated against 19 experimentally determined MTSSL labeled structures of T4 lysozyme and the membrane protein LeuT and 73 distance distributions from T4 lysozyme and the membrane protein MsbA. For a site in the core of T4 lysozyme, the correct spin label conformation (Χ1 and Χ2) is recovered in 99.8% of trials. In surface positions 53% of the trajectories agree with crystallized conformations in Χ1 and Χ2. This level of recovery is on par with Rosetta performance for the 20 natural amino acids. In addition, Rosetta predicts the distance between two spin labels with a mean error of 4.4 Å. The width of the experimental distance distribution, which reflects the flexibility of the two spin labels, is predicted with a mean error of 1.3 Å. RosettaEPR makes full-atom spin label modeling available to a wide scientific community in conjunction with the powerful suite of modeling methods within Rosetta.

Octarellin VI: using rosetta to design a putative artificial (ß/α)8 protein.

The computational protein design protocol Rosetta has been applied successfully to a wide variety of protein engineering problems. Here the aim was to test its ability to design de novo a protein adopting the TIM-barrel fold, whose formation requires about twice as many residues as in the largest proteins successfully designed de novo to date. The designed protein, Octarellin VI, contains 216 residues. Its amino acid composition is similar to that of natural TIM-barrel proteins. When produced and purified, it showed a far-UV circular dichroism spectrum characteristic of folded proteins, with α-helical and ß-sheet secondary structure. Its stable tertiary structure was confirmed by both tryptophan fluorescence and circular dichroism in the near UV. It proved heat stable up to 70°C. Dynamic light scattering experiments revealed a unique population of particles averaging 4 nm in diameter, in good agreement with our model. Although these data suggest the successful creation of an artificial α/ß protein of more than 200 amino acids, Octarellin VI shows an apparent noncooperative chemical unfolding and low solubility.

Using RosettaLigand for small molecule docking into comparative models.

Computational small molecule docking into comparative models of proteins is widely used to query protein function and in the development of small molecule therapeutics. We benchmark RosettaLigand docking into comparative models for nine proteins built during CASP8 that contain ligands. We supplement the study with 21 additional protein/ligand complexes to cover a wider space of chemotypes. During a full docking run in 21 of the 30 cases, RosettaLigand successfully found a native-like binding mode among the top ten scoring binding modes. From the benchmark cases we find that careful template selection based on ligand occupancy provides the best chance of success while overall sequence identity between template and target do not appear to improve results. We also find that binding energy normalized by atom number is often less than -0.4 in native-like binding modes.

Ligand-mimicking receptor variant discloses binding and activation mode of prolactin-releasing peptide.

The prolactin-releasing peptide receptor and its bioactive RF-amide peptide (PrRP20) have been investigated to explore the ligand binding mode of peptide G-protein-coupled receptors (GPCRs). By receptor mutagenesis, we identified the conserved aspartate in the upper transmembrane helix 6 (Asp(6.59)) of the receptor as the first position that directly interacts with arginine 19 of the ligand (Arg(19)). Replacement of Asp(6.59) with Arg(19) of PrRP20 led to D6.59R, which turned out to be a constitutively active receptor mutant (CAM). This suggests that the mutated residue at the top of transmembrane helix 6 mimics Arg(19) by interacting with additional binding partners in the receptor. Next, we generated an initial comparative model of this CAM because no ligand docking was required, and we selected the next set of receptor mutants to find the engaged partners of the binding pocket. In an iterative process, we identified two acidic residues and two hydrophobic residues that form the peptide ligand binding pocket. As all residues are localized on top or in the upper part of the transmembrane domains, we clearly can show that the extracellular surface of the receptor is sufficient for full signal transduction for prolactin-releasing peptide, rather than a deep, membrane-embedded binding pocket. This contributes to the knowledge of the binding of peptide ligands to GPCRs and might facilitate the development of GPCR ligands, but it also provides new targeting of CAMs involved in hereditary diseases.

Computational design of an endo-1,4-beta-xylanase ligand binding site.

The field of computational protein design has experienced important recent success. However, the de novo computational design of high-affinity protein-ligand interfaces is still largely an open challenge. Using the Rosetta program, we attempted the in silico design of a high-affinity protein interface to a small peptide ligand. We chose the thermophilic endo-1,4-ß-xylanase from Nonomuraea flexuosa as the protein scaffold on which to perform our designs. Over the course of the study, 12 proteins derived from this scaffold were produced and assayed for binding to the target ligand. Unfortunately, none of the designed proteins displayed evidence of high-affinity binding. Structural characterization of four designed proteins revealed that although the predicted structure of the protein model was highly accurate, this structural accuracy did not translate into accurate prediction of binding affinity. Crystallographic analyses indicate that the lack of binding affinity is possibly due to unaccounted for protein dynamics in the 'thumb' region of our design scaffold intrinsic to the family 11 ß-xylanase fold. Further computational analysis revealed two specific, single amino acid substitutions responsible for an observed change in backbone conformation, and decreased dynamic stability of the catalytic cleft. These findings offer new insight into the dynamic and structural determinants of the ß-xylanase proteins.

Design and directed evolution of a dideoxy purine nucleoside phosphorylase.

Purine nucleoside phosphorylase (PNP) catalyzes the synthesis and phosphorolysis of purine nucleosides, interconverting nucleosides with their corresponding purine base and ribose-1-phosphate. While PNP plays significant roles in human and pathogen physiology, we are interested in developing PNP as a catalyst for the formation of nucleoside analog drugs of clinical relevance. Towards this aim, we describe the engineering of human PNP to accept 2',3'-dideoxyinosine (ddI, Videx((R))) as a substrate for phosphorolysis using a combination of site-directed mutagenesis and directed evolution. In human PNP, we identified a single amino acid, Tyr-88, as a likely modulator of ribose selectivity. RosettaLigand was employed to calculate binding energies for substrate and substrate analog transition state complexes for single mutants of PNP where Tyr-88 was replaced with another amino acid. In parallel, these mutants were generated by site-directed mutagenesis, expressed and purified. A tyrosine to phenylalanine mutant (Y88F) was predicted by Rosetta to improve PNP catalytic activity with respect to ddI. Kinetic characterization of this mutant determined a 9-fold improvement in k(cat) and greater than 2-fold reduction in K(M). Subsequently, we used directed evolution to select for improved variants of PNP-Y88F in Escherichia coli cell extracts resulting in an additional 3-fold improvement over the progenitor strain. The engineered PNP may form the basis for catalysts and pathways for the biosynthesis of ddI.

Practically useful: what the Rosetta protein modeling suite can do for you.

The objective of this review is to enable researchers to use the software package Rosetta for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with Rosetta. For each of these six tasks, we provide a tutorial that illustrates a basic Rosetta protocol. The Rosetta method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 A. In addition to de novo structure prediction, Rosetta also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. Rosetta has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, Rosetta has been used to solve the X-ray crystallographic phase problem.

Structural determinants of species-selective substrate recognition in human and Drosophila serotonin transporters revealed through computational docking studies.

To identify potential determinants of substrate selectivity in serotonin (5-HT) transporters (SERT), models of human and Drosophila serotonin transporters (hSERT, dSERT) were built based on the leucine transporter (LeuT(Aa)) structure reported by Yamashita et al. (Nature 2005;437:215-223), PBDID 2A65. Although the overall amino acid identity between SERTs and the LeuT(Aa) is only 17%, it increases to above 50% in the first shell of the putative 5-HT binding site, allowing de novo computational docking of tryptamine derivatives in atomic detail. Comparison of hSERT and dSERT complexed with substrates pinpoints likely structural determinants for substrate binding. Forgoing the use of experimental transport and binding data of tryptamine derivatives for construction of these models enables us to critically assess and validate their predictive power: A single 5-HT binding mode was identified that retains the amine placement observed in the LeuT(Aa) structure, matches site-directed mutagenesis and substituted cysteine accessibility method (SCAM) data, complies with support vector machine derived relations activity relations, and predicts computational binding energies for 5-HT analogs with a significant correlation coefficient (R = 0.72). This binding mode places 5-HT deep in the binding pocket of the SERT with the 5-position near residue hSERT A169/dSERT D164 in transmembrane helix 3, the indole nitrogen next to residue Y176/Y171, and the ethylamine tail under residues F335/F327 and S336/S328 within 4 A of residue D98. Our studies identify a number of potential contacts whose contribution to substrate binding and transport was previously unsuspected.