Kristian Kaufmann

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 Å. More impressively, there are several cases in which Rosetta has been used to predict structures with atomic level accuracy better than 2.5 Å. 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.

Community-wide assessment of GPCR structure modelling and ligand docking

Recent breakthroughs in the determination of the crystal structures of G protein-coupled receptors (GPCRs) have provided new opportunities for structure-based drug design strategies targeting this protein family. With the aim of evaluating the current status of GPCR structure prediction and ligand docking, a community-wide, blind prediction assessment — GPCR Dock 2008 — was conducted in coordination with the publication of the crystal structure of the human adenosine A2A receptor bound to the ligand ZM241385. Twenty-nine groups submitted 206 structural models before the release of the experimental structure, which were evaluated for the accuracy of the ligand binding mode and the overall receptor model compared with the crystal structure. This analysis highlights important aspects for success and future development, such as accurate modelling of structurally divergent regions and use of additional biochemical insight such as disulphide bridges in the extracellular loops.

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 (LeuTAa) structure reported by Yamashita et al. (Nature 2005;437:215–223), PBDID 2A65. Although the overall amino acid identity between SERTs and the LeuTAa 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 LeuTAa 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 Å of residue D98. Our studies identify a number of potential contacts whose contribution to substrate binding and transport was previously unsuspected. Proteins 2009.

A Conserved Asparagine Residue in Transmembrane Segment 1 (TM1) of Serotonin Transporter Dictates Chloride-coupled Neurotransmitter Transport

Na+- and Cl−-dependent uptake of neurotransmitters via transporters of the SLC6 family, including the human serotonin transporter (SLC6A4), is critical for efficient synaptic transmission. Although residues in the human serotonin transporter involved in direct Cl− coordination of human serotonin transport have been identified, the role of Cl− in the transport mechanism remains unclear. Through a combination of mutagenesis, chemical modification, substrate and charge flux measurements, and molecular modeling studies, we reveal an unexpected role for the highly conserved transmembrane segment 1 residue Asn-101 in coupling Cl− binding to concentrative neurotransmitter uptake.

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.

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 (β/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 β-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/β protein of more than 200 amino acids, Octarellin VI shows an apparent noncooperative chemical unfolding and low solubility.

Pancreatic Polypeptide Is Recognized by Two Hydrophobic Domains of the Human Y4 Receptor Binding Pocket

Background: The Y4R is involved in regulation of food intake and gastrointestinal transport. Results: Mutagenesis studies revealed several residues displaying a significant loss of potency for hPP. Conclusion: Tops of TM2, TM6, and TM7 interact with the hY4R native agonist hPP. Significance: Characterizing the structure of the Y4R binding pocket is crucial for the development of new anti-obesity drugs. 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 Tyr2.64, Asp2.68, Asn6.55, Asn7.32, and Phe7.35 of Y4R are found to be important in receptor activation by hPP. Specifically, Tyr2.64 interacts with Tyr27 of hPP through hydrophobic contacts. Asn7.32 is affected by modifications on position Arg33 of hPP, suggesting a hydrogen bond between these two residues. Likewise, we find that Phe7.35 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.

Discovery and SAR of a novel series of GIRK1/2 and GIRK1/4 activators.

This Letter describes a novel series of GIRK activators identified through an HTS campaign. The HTS lead was a potent and efficacious dual GIRK1/2 and GIRK1/4 activator. Further chemical optimization through both iterative parallel synthesis and fragment library efforts identified dual GIRK1/2 and GIRK1/4 activators as well as the first examples of selective GIRK1/4 activators. Importantly, these compounds were inactive on GIRK2 and other non-GIRK1 containing GIRK channels, and SAR proved shallow.

Prediction of HIV-1 protease/inhibitor affinity using RosettaLigand.

Predicting HIV‐1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV‐1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV‐1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV‐1 protease inhibitor development.