Metal-Mediated Affinity and Orientation Specificity in a Computationally Designed Protein Homodimer

Computationally designing protein-protein interactions with high affinity and desired orientation is a challenging task. Incorporating metal-binding sites at the target interface may be one approach for increasing affinity and specifying the binding mode, thereby improving robustness of designed interactions for use as tools in basic research as well as in applications from biotechnology to medicine. Here we describe a Rosetta-based approach for the rational design of a protein monomer to form a zinc-mediated, symmetric homodimer. Our metal interface design, named MID1 (NESG target ID OR37), forms a tight dimer in the presence of zinc (MID1-zinc) with a dissociation constant <30 nM. Without zinc the dissociation constant is 4 μM. The crystal structure of MID1-zinc shows good overall agreement with the computational model, but only three out of four designed histidines coordinate zinc. However, a histidine-to-glutamate point mutation resulted in four-coordination of zinc, and the resulting metal binding site and dimer orientation closely matches the computational model (Cα RMSD = 1.4 Å)

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

Protein design tests our understanding of protein stability and structure. Successful design methods should allow the exploration of sequence space not found in nature. However, when redesigning naturally occurring protein structures most fixed backbone design algorithms return amino acid sequences that share strong sequence identity with wild-type sequences, especially in the protein core. This behavior places a restriction on functional space that can be explored and is not consistent with observations from nature, where sequences of low identity have similar structures. Here, we allow backbone flexibility during design to mutate every position in the core (38 residues) of a four-helix bundle protein. Only small perturbations to the backbone, 1-2 Å, were needed to entirely mutate the core. The redesigned protein, DRNN, is exceptionally stable (melting point > 140 °C). An NMR and X-ray crystal structure show that the side chains and backbone were accurately modeled (all-atom RMSD = 1.3 Å)

Computational de novo design of a four-helix bundle protein - DND-4HB

The de novo design of proteins is a rigorous test of our understanding of the key determinants of protein structure. The helix bundle is an interesting de novo design model system due to the diverse topologies that can be generated from a few simple α-helices. Previously, non-computational studies demonstrated that connecting amphipathic helices together with short loops can sometimes generate helix bundle proteins, regardless of the bundle's exact sequence. However using such methods, the precise positions of helices and side-chains cannot be predetermined. Since protein function depends on exact positioning of residues, we examined if sequence design tools in the program Rosetta could be used to design a four-helix bundle with a predetermined structure. Helix position was specified using a folding procedure that constrained the design model to a defined topology, and iterative rounds of rotamer-based sequence design and backbone refinement were used to identify a low energy sequence for characterization. The designed protein, DND_4HB, unfolds cooperatively (Tm >90°C) and a NMR solution structure shows that it adopts the target helical bundle topology. Helices 2, 3 and 4 agree very closely with the design model (backbone RMSD = 1.11 Å) and >90% of the core side-chain χ1 and χ2 angles are correctly predicted. Helix 1 lies in the target groove against the other helices, but is displaced 3 Å along the bundle axis. This result highlights the potential of computational design to create bundles with atomic-level precision, but also points at remaining challenges for achieving specific positioning between amphipathic helices

Don't Stop Thinking About Leptoquarks: Constructing New Models

We discuss the general framework for the construction of new models containing a single, fermion number zero scalar leptoquark of mass $\simeq 200-220$ GeV which can both satisfy the D0/CDF search constraints as well as low energy data, and can lead to both neutral and charged current-like final states at HERA. The class of models of this kind necessarily contain new vector-like fermions with masses at the TeV scale which mix with those of the Standard Model after symmetry breaking. In this paper we classify all models of this type and examine their phenomenological implications as well as their potential embedding into SUSY and non-SUSY GUT scenarios. The general coupling parameter space allowed by low energy as well as collider data for these models is described and requires no fine-tuning of the parameters.Comment: Modified text, added table, and updated reference

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Reversed halo sign in pneumocystis pneumonia: a case report

<p>Abstract</p> <p>Background</p> <p>The reversed halo sign may sometimes be seen in patients with cryptogenic organizing pneumonia, but is rarely associated with other diseases.</p> <p>Case presentation</p> <p>We present a case study of a 32-year-old male patient with acquired immunodeficiency syndrome, who had previously been treated with chemotherapy for non-Hodgkin's lymphoma. A chest X-ray showed bilateral patchy infiltrates. High-resolution computed tomography revealed the reversed halo sign in both upper lobes. The patient was diagnosed with pneumocystis pneumonia, which was successfully treated with sulfamethoxazole trimethoprim; the reversed halo sign disappeared, leaving cystic lesions. Cases such as this one are rare, but show that the reversed halo sign may occur in patients who do not have cryptogenic organizing pneumonia.</p> <p>Conclusion</p> <p>Physicians can avoid making an incorrect diagnosis and prescribing the wrong treatment by carefully evaluating all clinical criteria rather than assuming that the reversed halo sign only occurs with cryptogenic organizing pneumonia.</p

Orientation-dependent backbone-only residue pair scoring functions for fixed backbone protein design

<p>Abstract</p> <p>Background</p> <p>Empirical scoring functions have proven useful in protein structure modeling. Most such scoring functions depend on protein side chain conformations. However, backbone-only scoring functions do not require computationally intensive structure optimization and so are well suited to protein design, which requires fast score evaluation. Furthermore, scoring functions that account for the distinctive relative position and orientation preferences of residue pairs are expected to be more accurate than those that depend only on the separation distance.</p> <p>Results</p> <p>Residue pair scoring functions for fixed backbone protein design were derived using only backbone geometry. Unlike previous studies that used spherical harmonics to fit 2D angular distributions, Gaussian Mixture Models were used to fit the full 3D (position only) and 6D (position and orientation) distributions of residue pairs. The performance of the 1D (residue separation only), 3D, and 6D scoring functions were compared by their ability to identify correct threading solutions for a non-redundant benchmark set of protein backbone structures. The threading accuracy was found to steadily increase with increasing dimension, with the 6D scoring function achieving the highest accuracy. Furthermore, the 3D and 6D scoring functions were shown to outperform side chain-dependent empirical potentials from three other studies. Next, two computational methods that take advantage of the speed and pairwise form of these new backbone-only scoring functions were investigated. The first is a procedure that exploits available sequence data by averaging scores over threading solutions for homologs. This was evaluated by applying it to the challenging problem of identifying interacting transmembrane alpha-helices and found to further improve prediction accuracy. The second is a protein design method for determining the optimal sequence for a backbone structure by applying Belief Propagation optimization using the 6D scoring functions. The sensitivity of this method to backbone structure perturbations was compared with that of fixed-backbone all-atom modeling by determining the similarities between optimal sequences for two different backbone structures within the same protein family. The results showed that the design method using 6D scoring functions was more robust to small variations in backbone structure than the all-atom design method.</p> <p>Conclusions</p> <p>Backbone-only residue pair scoring functions that account for all six relative degrees of freedom are the most accurate and including the scores of homologs further improves the accuracy in threading applications. The 6D scoring function outperformed several side chain-dependent potentials while avoiding time-consuming and error prone side chain structure prediction. These scoring functions are particularly useful as an initial filter in protein design problems before applying all-atom modeling.</p