Biomedical subjects

Ora Schueler-Furman

Publications and source records attributed to Ora Schueler-Furman.

13 recordsLinked to original sources

PubMed2007

Trans protein splicing of cyanobacterial split inteins in endogenous and exogenous combinations.

Inteins are autocatalytic protein domains that post-translationally excise from protein precursors and ligate their flanking regions with a peptide bond, in a process called protein splicing. Intein-containing DNA polymerases of cyanobacteria and nanoarchaea are naturally split into two separate genes at their intein domain. Such naturally occurring split inteins rapidly self-associate and reconstitute protein-splicing activity in trans. Here, we analyze the in vitro protein-splicing activity of three naturally split inteins from diverse cyanobacteria: Oscillatoria limnetica, Thermosynechococcus vulcanus, and Nostoc sp. PCC7120. N- and C-terminal halves of these split inteins were mixed in nine combinations, resulting in three endogenous (wild-type) and six exogenous combinations. Protein splicing was detected in all split-intein combinations, despite a 30-50% sequence variation between the homologous proteins. Splicing activity proceeded under a variety of conditions, including the presence of denaturants and reductants and high temperature, ionic strength, and viscosity. Still, in a high concentration of salt (2 M) or urea (6 M), specific combinations spliced significantly better than others. Additionally, copper ions were found to inhibit trans splicing in a reversible double-lock reaction. Our comparative analysis of naturally split inteins in endogenous and exogenous combinations demonstrates the modularity of trans protein-splicing elements and their robust activity. It suggests tight interactions between split-intein halves and conditions for modifying the specificity of intein parts. These results promote the biotechnological use of split inteins for controlled assembly of protein fragments either in vivo or in vitro and under moderate or extreme conditions.

Amino Acid Sequence↗

PubMed2006

Folding of the C-terminal bacterial binding domain in statherin upon adsorption onto hydroxyapatite crystals.

Statherin is an enamel pellicle protein that inhibits hydroxyapatite (HAP) nucleation and growth, lubricates the enamel surface, and is recognized by oral bacteria in periodontal diseases. We report here from solid-state NMR measurements that the protein's C-terminal region folds into an alpha-helix upon adsorption to HAP crystals. This region contains the binding sites for bacterial fimbriae that mediate bacterial cell adhesion to the surface of the tooth. The helical segment is shown through long-range distance measurements to fold back onto the intermediate region (residues Y16-P28) defining the global fold of the protein. Statherin, previously shown to be unstructured in solution, undergoes conformation selection on its substrate mineral surface. This surface-induced folding of statherin can be related to its functionality in inhibiting HAP crystal growth and can explain how oral pathogens selectively recognize HAP-bound statherin.

Adsorption↗

PubMed2006

Learning MHC I--peptide binding.

MOTIVATION AND RESULTS: Motivated by the ability of a simple threading approach to predict MHC I--peptide binding, we developed a new and improved structure-based model for which parameters can be estimated from additional sources of data about MHC-peptide binding. In addition to the known 3D structures of a small number of MHC-peptide complexes that were used in the original threading approach, we included three other sources of information on peptide-MHC binding: (1) MHC class I sequences; (2) known binding energies for a large number of MHC-peptide complexes; and (3) an even larger binary dataset that contains information about strong binders (epitopes) and non-binders (peptides that have a low affinity for a particular MHC molecule). Our model significantly outperforms the standard threading approach in binding energy prediction. In our approach, which we call adaptive double threading, the parameters of the threading model are learnable, and both MHC and peptide sequences can be threaded onto structures of other alleles. These two properties make our model appropriate for predicting binding for alleles for which very little data (if any) is available beyond just their sequence, including prediction for alleles for which 3D structures are not available. The ability of our model to generalize beyond the MHC types for which training data is available also separates our approach from epitope prediction methods which treat MHC alleles as symbolic types, rather than biological sequences. We used the trained binding energy predictor to study viral infections in 246 HIV patients from the West Australian cohort, and over 1000 sequences in HIV clade B from Los Alamos National Laboratory database, capturing the course of HIV evolution over the last 20 years. Finally, we illustrate short-, medium-, and long-term adaptation of HIV to the human immune system. AVAILABILITY: http://www.research.microsoft.com/~jojic/hlaBinding.html.

Algorithms↗

PubMed2006

Is GAS1 a co-receptor for the GDNF family of ligands?

Glial-cell-line-derived neurotrophic factor (GDNF) is a survival and maintenance factor for dopamine-containing neurons and motoneurons. GDNF belongs to a family of structurally related factors that includes neurturin (NRTN), artemin (ARTN) and persephin (PSPN). An initial step in the activation of signaling via the GDNF family of ligands (GFLs) is their binding to their cognate co-receptor GFR alpha. GAS1, an apparently unrelated protein, exhibits homology to GFR alpha and thus we hypothesize that GAS1 can serve as an alternative receptor for GFLs. The functional similarity between GFR alpha and GAS1 extends to their role in embryogenesis, differentiation and glia maintenance, and is substantiated by overlap in their expression profile, subcellular localization and structural details. We propose that the relative expression and localization of the two remote receptors, GFR alpha and GAS1, on the membranes of neuronal and glial cells determines whether these cells survive or undergo apoptotic death.

Amino Acid Sequence↗

PubMed2005

Identifying a common molecular mechanism for inhibition of MITF and STAT3 by PIAS3.

Protein inhibitor of activated STAT3 (PIAS3) functions in vivo as a key molecule in suppressing the transcriptional activity of both microphthalmia transcription factor (MITF) and signal transducer and activator of transcription 3 (STAT3), 2 transcription factors that play a major role in the regulation of growth and function in mast cells and melanocytes. Previously, we have demonstrated binding of PIAS3 to MITF leading to the inhibition of MITF transcriptional activity. Following cellular activation, PIAS3 is released from MITF and binds to STAT3. Now we have localized a common binding motif in PIAS3 for MITF and STAT3. This motif (PIAS82-132), which contains 50 amino acids, is sufficient for the inhibition of both MITF and STAT3. Three-dimensional protein modeling demonstrated that this motif contains 2 alpha helices. Disruption of one of the helices led to the loss of PIAS3 inhibitory activity. In addition to contributing to our understanding of the mechanisms of PIAS3 activity, these results could pave the way toward the formulation of an antioncogenic agent for the inhibition of both STAT3 and MITF.

Amino Acid Sequence↗

PubMed2005

Progress in modeling of protein structures and interactions.

The prediction of the structures and interactions of biological macromolecules at the atomic level and the design of new structures and interactions are critical tests of our understanding of the interatomic interactions that underlie molecular biology. Equally important, the capability to accurately predict and design macromolecular structures and interactions would streamline the interpretation of genome sequence information and allow the creation of macromolecules with new and useful functions. This review summarizes recent progress in modeling that suggests that we are entering an era in which high-resolution prediction and design will make increasingly important contributions to biology and medicine.

Computer Simulation↗

PubMed2005

A model of anthrax toxin lethal factor bound to protective antigen.

Anthrax toxin is made up of three proteins: the edema factor (EF), lethal factor (LF) enzymes, and the multifunctional protective antigen (PA). Proteolytically activated PA heptamerizes, binds the EF/LF enzymes, and forms a pore that allows for EF/LF passage into host cells. Using directed mutagenesis, we identified three LF-PA contact points defined by a specific disulfide crosslink and two pairs of complementary charge-reversal mutations. These contact points were consistent with the lowest energy LF-PA complex found by using Rosetta protein-protein docking. These results illustrate how biochemical and computational methods can be combined to produce reliable models of large complexes. The model shows that EF and LF bind through a highly electrostatic interface, with their flexible N-terminal region positioned at the entrance of the heptameric PA pore and thus poised to initiate translocation in an N- to C-terminal direction.

Antigens, Bacterial↗

PubMed2005

Progress in protein-protein docking: atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side-chain flexibility.

RosettaDock uses real-space Monte Carlo minimization (MCM) on both rigid-body and side-chain degrees of freedom to identify the lowest free energy docked arrangement of 2 protein structures. An improved version of the method that uses gradient-based minimization for off-rotamer side-chain optimization and includes information from unbound structures was used to create predictions for Rounds 4 and 5 of CAPRI. First, large numbers of independent MCM trajectories were carried out and the lowest free energy docked configurations identified. Second, new trajectories were started from these lowest energy structures to thoroughly sample the surrounding conformation space, and the lowest energy configurations were submitted as predictions. For all cases in which there were no significant backbone conformational changes, a small number of very low-energy configurations were identified in the first, global search and subsequently found to be close to the center of the basin of attraction in the free energy landscape in the second, local search. Following the release of the experimental coordinates, it was found that the centers of these free energy minima were remarkably close to the native structures in not only the rigid-body orientation but also the detailed conformations of the side-chains. Out of 8 targets, the lowest energy models had interface root-mean-square deviations (RMSDs) less than 1.1 A from the correct structures for 6 targets, and interface RMSDs less than 0.4 A for 3 targets. The predictions were top submissions to CAPRI for Targets 11, 12, 14, 15, and 19. The close correspondence of the lowest free energy structures found in our searches to the experimental structures suggests that our free energy function is a reasonable representation of the physical chemistry, and that the real space search with full side-chain flexibility to some extent solves the protein-protein docking problem in the absence of significant backbone conformational changes. On the other hand, the approach fails when there are significant backbone conformational changes as the steric complementarity of the 2 proteins cannot be modeled without incorporating backbone flexibility, and this is the major goal of our current work.

Algorithms↗

PubMed2005

Improved side-chain modeling for protein-protein docking.

Success in high-resolution protein-protein docking requires accurate modeling of side-chain conformations at the interface. Most current methods either leave side chains fixed in the conformations observed in the unbound protein structures or allow the side chains to sample a set of discrete rotamer conformations. Here we describe a rapid and efficient method for sampling off-rotamer side-chain conformations by torsion space minimization during protein-protein docking starting from discrete rotamer libraries supplemented with side-chain conformations taken from the unbound structures, and show that the new method improves side-chain modeling and increases the energetic discrimination between good and bad models. Analysis of the distribution of side-chain interaction energies within and between the two protein partners shows that the new method leads to more native-like distributions of interaction energies and that the neglect of side-chain entropy produces a small but measurable increase in the number of residues whose interaction energy cannot compensate for the entropic cost of side-chain freezing at the interface. The power of the method is highlighted by a number of predictions of unprecedented accuracy in the recent CAPRI (Critical Assessment of PRedicted Interactions) blind test of protein-protein docking methods.

Models, Molecular↗

PubMed2003

Conserved residue clustering and protein structure prediction.

Protein residues that are critical for structure and function are expected to be conserved throughout evolution. Here, we investigate the extent to which these conserved residues are clustered in three-dimensional protein structures. In 92% of the proteins in a data set of 79 proteins, the most conserved positions in multiple sequence alignments are significantly more clustered than randomly selected sets of positions. The comparison to random subsets is not necessarily appropriate, however, because the signal could be the result of differences in the amino acid composition of sets of conserved residues compared to random subsets (hydrophobic residues tend to be close together in the protein core), or differences in sequence separation of the residues in the different sets. In order to overcome these limits, we compare the degree of clustering of the conserved positions on the native structure and on alternative conformations generated by the de novo structure prediction method Rosetta. For 65% of the 79 proteins, the conserved residues are significantly more clustered in the native structure than in the alternative conformations, indicating that the clustering of conserved residues in protein structures goes beyond that expected purely from sequence locality and composition effects. The differences in the spatial distribution of conserved residues can be utilized in de novo protein structure prediction: We find that for 79% of the proteins, selection of the Rosetta generated conformations with the greatest clustering of the conserved residues significantly enriches the fraction of close-to-native structures.

Acyl Coenzyme A↗

PubMed2003

Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations.

Protein-protein docking algorithms provide a means to elucidate structural details for presently unknown complexes. Here, we present and evaluate a new method to predict protein-protein complexes from the coordinates of the unbound monomer components. The method employs a low-resolution, rigid-body, Monte Carlo search followed by simultaneous optimization of backbone displacement and side-chain conformations using Monte Carlo minimization. Up to 10(5) independent simulations are carried out, and the resulting "decoys" are ranked using an energy function dominated by van der Waals interactions, an implicit solvation model, and an orientation-dependent hydrogen bonding potential. Top-ranking decoys are clustered to select the final predictions. Small-perturbation studies reveal the formation of binding funnels in 42 of 54 cases using coordinates derived from the bound complexes and in 32 of 54 cases using independently determined coordinates of one or both monomers. Experimental binding affinities correlate with the calculated score function and explain the predictive success or failure of many targets. Global searches using one or both unbound components predict at least 25% of the native residue-residue contacts in 28 of the 32 cases where binding funnels exist. The results suggest that the method may soon be useful for generating models of biologically important complexes from the structures of the isolated components, but they also highlight the challenges that must be met to achieve consistent and accurate prediction of protein-protein interactions.

Algorithms↗

PubMed2003

Protein-protein docking predictions for the CAPRI experiment.

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low-resolution rigid body Monte Carlo search followed by high-resolution refinement with side-chain conformational changes and rigid body minimization. Decoys (approximately 10(6) per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue-residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 A RMSD and included 48 of 65 correct residue-residue contacts. Target 7 was predicted at 5.3 A RMSD with 22 of 37 correct residue-residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 A although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified.

Algorithms↗

PubMed2003

Rosetta predictions in CASP5: successes, failures, and prospects for complete automation.

We describe predictions of the structures of CASP5 targets using Rosetta. The Rosetta fragment insertion protocol was used to generate models for entire target domains without detectable sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a structural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large proteins that were incorrectly parsed into domains and for topologically complex (high contact order) proteins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human-assisted predictions for most of the targets, suggesting that automated genomic-scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human-assisted predictions were significantly closer to the native structure, we identify the steps that remain to be automated.

Algorithms↗