A restraint molecular dynamics and simulated annealing approach for protein homology modeling utilizing mean angles

Bi-stability of the master gene regulatory network of the common dendritic precursor cell: Implications for cell differentiation

In cell lineage commitment decisions, a gene regulatory network (GRN) consisting of a limited number of transcription factors forms the regulatory pivot. Myeloid lineage dendritic cells or DCs are specialized cells having the antigen-presenting ability and are of immense importance in immune surveillance. In this report, we analyze the GRN that governs the lineage commitment of Common DC Progenitor (CDP) cells to conventional dendritic cells (cDC) and plasmacytoid dendritic cells (pDC). We have analyzed the quantitative behavior of the master regulatory circuit of CDP that governs the lineage commitment. Simulations showed that the GRN displays a bi-stable behavior within a range of parameter values. Several transcription factors, PU.1, IRF8, Flt3, and Stat3, whose concentrations vary significantly in the two steady states, appear to be the key players. We hypothesize that the two stable steady states are precursors of cDC and pDC, and the variation of concentration of these key transcription factors in the two states may be responsible for early events in lineage commitment.

Structural transitions in full-length human prion protein detected by xenon as probe and spin labeling of the N-terminal domain

Fatal neurodegenerative disorders termed transmissible spongiform encephalopathies (TSEs) are associated with the accumulation of fibrils of misfolded prion protein PrP. The noble gas xenon accommodates into four transiently enlarged hydrophobic cavities located in the well-folded core of human PrP(23–230) as detected by [¹H, ¹⁵N]-HSQC spectroscopy. In thermal equilibrium a fifth xenon binding site is formed transiently by amino acids A120 to L125 of the presumably disordered N-terminal domain and by amino acids K185 to T193 of the well-folded domain. Xenon bound PrP was modelled by restraint molecular dynamics. The individual microscopic and macroscopic dissociation constants could be derived by fitting the data to a model including a dynamic opening and closing of the cavities. As observed earlier by high pressure NMR spectroscopy xenon binding influences also other amino acids all over the N-terminal domain including residues of the AGAAAAGA motif indicating a structural coupling between the N-terminal domain and the core domain. This is in agreement with spin labelling experiments at positions 93 or 107 that show a transient interaction between the N-terminus and the start of helix 2 and the end of helix 3 of the core domain similar to that observed earlier by Zn²⁺-binding to the octarepeat motif.

Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) for Conformational Space Search of Peptide and Miniprotein

Protein and peptide structure predictions are of paramount importance for understanding their functions, as well as the interactions with other molecules. However, the use of molecular simulation techniques to directly predict the peptide structure from the primary amino acid sequence is always hindered by the rough topology of the conformational space and the limited simulation time scale. We developed here a new strategy, named Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) to identify the native states of a peptide and miniprotein. A cluster of near native structures could be obtained by using the MSA-MD method, which turned out to be significantly more efficient in reaching the native structure compared to continuous MD and conventional SA-MD simulation.

Human cytomegalovirus major immediate early 1 protein targets host chromosomes by docking to the acidic pocket on the nucleosome surface

The 72-kDa immediate early 1 (IE1) protein encoded by human cytomegalovirus (hCMV) is a nuclearly localized promiscuous regulator of viral and cellular transcription. IE1 has long been known to associate with host mitotic chromatin, yet the mechanisms underlying this interaction have not been specified. In this study, we identify the cellular chromosome receptor for IE1. We demonstrate that the viral protein targets human nucleosomes by directly binding to core histones in a nucleic acid-independent manner. IE1 exhibits two separable histone-interacting regions with differential binding specificities for H2A-H2B and H3-H4. The H2A-H2B binding region was mapped to an evolutionarily conserved 10-amino-acid motif within the chromatin-tethering domain (CTD) of IE1. Results from experimental approaches combined with molecular modeling indicate that the IE1 CTD adopts a β-hairpin structure, docking with the acidic pocket formed by H2A-H2B on the nucleosome surface. IE1 binds to the acidic pocket in a way similar to that of the latency-associated nuclear antigen (LANA) of the Kaposi's sarcoma-associated herpesvirus. Consequently, the IE1 and LANA CTDs compete for binding to nucleosome cores and chromatin. Our work elucidates in detail how a key viral regulator is anchored to human chromosomes and identifies the nucleosomal acidic pocket as a joint target of proteins from distantly related viruses. Based on the striking similarities between the IE1 and LANA CTDs and the fact that nucleosome targeting by IE1 is dispensable for productive replication even in "clinical" strains of hCMV, we speculate that the two viral proteins may serve analogous functions during latency of their respective viruses.

Determining the DNA stability parameters for the breathing dynamics of heterogeneous DNA by stochastic optimization

We suggest that the thermodynamic stability parameters (nearest neighbor stacking and hydrogen bonding free energies) of double-stranded DNA molecules can be inferred reliably from time series of the size fluctuations (breathing) of local denaturation zones (bubbles). On the basis of the reconstructed bubble size distribution, this is achieved through stochastic optimization of the free energies in terms of simulated annealing. In particular, it is shown that even noisy time series allow the identification of the stability parameters at remarkable accuracy. This method will be useful to obtain the DNA stacking and hydrogen bonding free energies from single bubble breathing assays rather than equilibrium data.

Automated protein NMR structure determination in solution

The main drawback of protein NMR spectroscopy today is still the extensive amount of time required for solving a single structure. The main bottleneck in this respect is the manual evaluation of the experimental spectra. A clear solution to this challenge is the development of automated methods for this purpose. At the current stage of development, this goal has been almost or in a few cases fully reached for favorable cases such as well-behaved, stably folding smaller proteins below the 25 kDa range. For larger and/or more difficult molecules, the input of a human expert is still required. However, even here, automated routines will substantially speed up the structure determination process. In this report, we will summarize recent developments in this field and especially emphasize practical aspects important for a successful automated protein structure determination in solution. An important aspect closely related to structure determination is structure validation. Therefore, we devote a section to automated approaches for this topic.

Protein structure calculation with data imputation: the use of substitute restraints

The amount of experimental restraints e.g., NOEs is often too small for calculating high quality three-dimensional structures by restrained molecular dynamics. Considering this as a typical missing value problem we propose here a model based data imputation technique that should lead to an improved estimation of the correct structure. The novel automated method implemented in AUREMOL makes a more efficient use of the experimental information to obtain NMR structures with higher accuracy. It creates a large set of substitute restraints that are used either alone or together with the experimental restraints. The new approach was successfully tested on three examples: firstly, the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the mutant HPr (H15A) from Staphylococcus aureus, and a X-ray structure of human ubiquitin. In all three examples, the quality of the resulting final bundles was improved considerably by the use of additional substitute restraints, as assessed quantitatively by the calculation of RMSD values to the "true" structure and NMR R-factors directly calculated from the original NOESY spectra or the published diffraction data.

A novel method for predicting and using distance constraints of high accuracy for refining protein structure prediction

The principal bottleneck in protein structure prediction is the refinement of models from lower accuracies to the resolution observed by experiment. We developed a novel constraints-based refinement method that identifies a high number of accurate input constraints from initial models and rebuilds them using restrained torsion angle dynamics (rTAD). We previously created a Bayesian statistics-based residue-specific all-atom probability discriminatory function (RAPDF) to discriminate native-like models by measuring the probability of accuracy for atom type distances within a given model. Here, we exploit RAPDF to score (i.e., filter) constraints from initial predictions that may or may not be close to a native-like state, obtain consensus of top scoring constraints amongst five initial models, and compile sets with no redundant residue pair constraints. We find that this method consistently produces a large and highly accurate set of distance constraints from which to build refinement models. We further optimize the balance between accuracy and coverage of constraints by producing multiple structure sets using different constraint distance cutoffs, and note that the cutoff governs spatially near versus distant effects in model generation. This complete procedure of deriving distance constraints for rTAD simulations improves the quality of initial predictions significantly in all cases evaluated by us. Our procedure represents a significant step in solving the protein structure prediction and refinement problem, by enabling the use of consensus constraints, RAPDF, and rTAD for protein structure modeling and refinement.

A general method for the unbiased improvement of solution NMR structures by the use of related X-ray data, the AUREMOL-ISIC algorithm

Background

Rapid and accurate three-dimensional structure determination of biological macromolecules is mandatory to keep up with the vast progress made in the identification of primary sequence information. During the last few years the amount of data deposited in the protein data bank has substantially increased providing additional information for novel structure determination projects. The key question is how to combine the available database information with the experimental data of the current project ensuring that only relevant information is used and a correct structural bias is produced. For this purpose a novel fully automated algorithm based on Bayesian reasoning has been developed. It allows the combination of structural information from different sources in a consistent way to obtain high quality structures with a limited set of experimental data. The new ISIC (Intelligent Structural Information Combination) algorithm is part of the larger AUREMOL software package.

Results

Our new approach was successfully tested on the improvement of the solution NMR structures of the Ras-binding domain of Byr2 from Schizosaccharomyces pombe, the Ras-binding domain of RalGDS from human calculated from a limited set of NMR data, and the immunoglobulin binding domain from protein G from Streptococcus by their corresponding X-ray structures. In all test cases clearly improved structures were obtained. The largest danger in using data from other sources is a possible bias towards the added structure. In the worst case instead of a refined target structure the structure from the additional source is essentially reproduced. We could clearly show that the ISIC algorithm treats these difficulties properly.

Conclusion

In summary, we present a novel fully automated method to combine strongly coupled knowledge from different sources. The combination with validation tools such as the calculation of NMR R-factors strengthens the impact of the method considerably since the improvement of the structures can be assessed quantitatively. The ISIC method can be applied to a large number of similar problems where the quality of the obtained three-dimensional structures is limited by the available experimental data like the improvement of large NMR structures calculated from sparse experimental data or the refinement of low resolution X-ray structures. Also structures may be refined using other available structural information such as homology models.

Simple and versatile restraints for the accurate modeling of α-helical coiled-coil structures of multiple strandedness, orientation and composition

We present a minimalist approach for the modeling of the three-dimensional structure of multistranded alpha-helical coiled coils. The approach is based on empirical principles introduced by F. H. C. Crick (F. H. C. Crick, Acta Crystallogr, 1953, Vol. 6, pp. 689-697). Crick hypothesized that keeping the distance between the residues at the interacting interface of alpha-helices constant would lead to supercoiling or the formation of a coiled coil through the knobs-into-holes mode of packing. We have implemented the latter hypothesis in a simulating annealing protocol in the simple form of interhelical distance restraints (two per heptad) between Calpha at the interfacial positions and. To demonstrate the authenticity of Crick's hypothesis and the precision and accuracy of our approach, we have modeled the crystal structures of six synthetic coiled coils in dimeric, trimeric, and tetrameric states. The mean root mean square deviations (RMSDs) between the backbone atoms of the ensemble of structures calculated and those of the corresponding geometric averages is always below 0.76 A, indicating that our protocol has an excellent degree of convergence and precision. The RMSDs between the backbone atoms of each of the six geometric average structures and the backbone of the corresponding crystal structures all range between 0.43 and 0.95 A, indicative of excellent accuracy and proving the authenticity of Crick's hypothesis. Moreover, without specifying any dihedral angles, we found that in 81% of the occurrences, the most populated conformer of the side chains at positions and in the ensembles calculated were identical to those observed in the crystal structures. This shows that our simple approach, which is the simplest reported so far, can generate accurate results for the backbone and side chains. Finally, as a test case for a wider application of our approach in the field of structural proteomics, we describe the successful modeling of the overall structure of SNARE and the organization of its interfacial ionic layer known to play an important functional role.