Defining and computing optimum RMSD for gapped and weighted multiple-structure alignment

Pairwise structure alignment commonly uses root mean square deviation (RMSD) to measure the structural similarity, and methods for optimizing RMSD are well established. We extend RMSD to weighted RMSD for multiple structures. By using multiplicative weights, we show that weighted RMSD for all pairs is the same as weighted RMSD to an average of the structures. Thus, using RMSD or weighted RMSD implies that the average is a consensus structure. Although we show that in general, the two tasks of finding the optimal translations and rotations for minimizing weighted RMSD cannot be separated for multiple structures like they can for pairs, an inherent difficulty and a fact ignored by previous work, we develop a near-linear iterative algorithm to converge weighted RMSD to a local minimum. 10,000 experiments of gapped alignment done on each of 23 protein families from HOMSTRAD (where each structure starts with a random translation and rotation) converge rapidly to the same minimum. Finally we propose a heuristic method to iteratively remove the effect of outliers and find well-aligned positions that determine the structural conserved region by modeling B-factors and deviations from the average positions as weights and iteratively assigning higher weights to better aligned atoms.     

Gaussian-weighted RMSD superposition of proteins: a structural comparison for flexible proteins and predicted protein structures

Many proteins contain flexible structures such as loops and hinged domains. A simple root mean square deviation (RMSD) alignment of two different conformations of the same protein can be skewed by the difference between the mobile regions. To overcome this problem, we have developed a novel method to overlay two protein conformations by their atomic coordinates using a Gaussian-weighted RMSD (wRMSD) fit. The algorithm is based on the Kabsch least-squares method and determines an optimal transformation between two molecules by calculating the minimal weighted deviation between the two coordinate sets. Unlike other techniques that choose subsets of residues to overlay, all atoms are included in the wRMSD overlay. Atoms that barely move between the two conformations will have a greater weighting than those that have a large displacement. Our superposition tool has produced successful alignments when applied to proteins for which two conformations are known. The transformation calculation is heavily weighted by the coordinates of the static region of the two conformations, highlighting the range of flexibility in the overlaid structures. Lastly, we show how wRMSD fits can be used to evaluate predicted protein structures. Comparing a predicted fold to its experimentally determined target structure is another case of comparing two protein conformations of the same sequence, and the degree of alignment directly reflects the quality of the prediction.     

RMSD analysis of structures of the bacterial protein FimH identifies five conformations of its lectin domain

FimH is a bacterial adhesin protein located at the tip of Escherichia coli fimbria that functions to adhere bacteria to host cells. Thus, FimH is a critical factor in bacterial infections such as urinary tract infections and is of interest in drug development. It is also involved in vaccine development and as a model for understanding shear-enhanced catch bond cell adhesion. To date, over 60 structures have been deposited in the Protein Data Bank showing interactions between FimH and mannose ligands, potential inhibitors, and other fimbrial proteins. In addition to providing insights about ligand recognition and fimbrial assembly, these structures provide insights into conformational changes in the two domains of FimH that are critical for its function. To gain further insights into these structural changes, we have superposed FimH's mannose binding lectin domain in all these structures and categorized the structures into five groups of lectin domain conformers using RMSD as a metric. Many structures also include the pilin domain, which anchors FimH to the fimbriae and regulates the conformation and function of the lectin domain. For these structures, we have also compared the relative orientations of the two domains. These structural analyses enhance our understanding of the conformational changes associated with FimH ligand binding and domain-domain interactions, including its catch bond behavior through allosteric action of force in bacterial adhesion.     

Variations in clique and community patterns in protein structures during allosteric communication: investigation of dynamically equilibrated structures of methionyl tRNA synthetase complexes

The allosteric concept has played a key role in understanding the biological functions of proteins. The rigidity or plasticity and the conformational population are the two important ideas invoked in explaining the allosteric effect. Although molecular insights have been gained from a large number of structures, a precise assessment of the ligand-induced conformational changes in proteins at different levels, ranging from gross topology to intricate details, remains a challenge. In this study, we have explored the conformational changes in the complexes of methionyl tRNA synthetase (MetRS) through novel network parameters such as cliques and communities, which identify the rigid regions in the protein structure networks (PSNs) constructed from the noncovalent interactions of amino acid side chains. MetRS belongs to the aminoacyl tRNA synthetase (aaRS) family that plays a crucial role in the translation of genetic code. These enzymes are modular with distinct domains from which extensive genetic, kinetic, and structural data are available, highlighting the role of interdomain communication. The network parameters evaluated here on the conformational ensembles of MetRS complexes, generated from molecular dynamics simulations, have enabled us to understand the interdomain communication in detail. Additionally, the characterization of conformational changes in terms of cliques and communities has also become possible, which had eluded conventional analyses. Furthermore, we find that most of the residues participating in cliques and communities are strikingly different from those that take part in long-range communication. The cliques and communities evaluated here for the first time on PSNs have beautifully captured the local geometries in detail within the framework of global topology. Here the allosteric effect is revealed at the residue level via identification of the important residues specific for structural rigidity and functional flexibility in MetRS. This ought to enhance our understanding of the functioning of aaRS in general.     

A computer model to dynamically simulate protein folding: studies with crambin

The current work describes a simplified representation of protein structure with uses in the simulation of protein folding. The model assumes that a protein can be represented by a freely rotating rigid chain with a single atom approximating the effect of each side chain. Potentials describing the attraction or repulsion between different types of amino acids are determined directly from the distribution of amino acids in the database of known protein structures. The optimization technique of simulated annealing has been used to dynamically sample the conformations available to this simple model, allowing the protein to evolve from an extended, random coil into a compact globular structure. Many characteristics expected of true proteins, such as the sequence-dependent formation of secondary structure, the partitioning of hydrophobic residues, and specific disulfide pairing, are reproduced by the simulation, suggesting the model may accurately simulate the folding process.     

CARON – Average RMSD of NMR structure ensembles

The NMR protein structures are often deposited in the Protein Data Bank as ensembles of models that agree with the experimental restraints. Information about stereochemical variability and the molecular flexibility can be obtained by systematic comparison of all models. Here we describe CARON, a software that allows the computation of the root-mean-square-distances between equivalent atoms and residues in all models and introduces these values into the occupancy and the B-factor fields of PDB-formatted files. This tool allows the user to both get a quantitative estimation of the conformational homogeneity of the models and to exploit this information in common computer graphics programs.     

Deriving protein dynamical properties from weighted protein contact number

It has recently been shown that in proteins the atomic mean-square displacement (or B-factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B-factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high-throughput computational tool to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean-square displacement and the weighted protein contact-number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross-correlation of atomic motion (the B-factor is essentially the auto-correlation of atomic motion). For a nonhomologous dataset comprising 972 high-resolution X-ray protein structures (resolution <2.0 A and sequence identity <25%), the mean correlation coefficient between the X-ray and computed B-factors based on the weighted protein contact-number model is 0.61, which is better than those of the original contact-number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact-number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure-dynamics relationship.     

Superimposition of potent non-peptide AT1 receptor antagonists with angiotensin II

Summary The model of angiotensin II (ANG II) developed in our laboratory using a combination of NMR, fluorescence data and molecular graphics [Matsoukas, J.M. et al., J. Biol. Chem., 269 (1994) 5303] served as a template for a systematic superimposition of potent AT₁ receptor antagonists with ANG II. The key amino acids in this model, tyrosine, phenylalanine and histidine, form a charge-relay system. The studied ANG II AT₁ receptor antagonists were found to accommodate this relay system. The proposed model offers a motivation to synthetic chemists to develop ANG II antagonists that differ from the losartan prototype structure but possess an enhanced biological profile.     

Toward a hypothesis-free understanding of how phosphorylation dynamically impacts protein turnover

The turnover measurement of proteins and proteoforms has been largely facilitated by workflows coupling metabolic labeling with mass spectrometry (MS), including dynamic stable isotope labeling by amino acids in cell culture (dynamic SILAC) or pulsed SILAC (pSILAC). Very recent studies including ours have integrated themeasurement of post-translational modifications (PTMs) at the proteome level (i.e., phosphoproteomics) with pSILAC experiments in steady state systems, exploring the link between PTMs and turnover at the proteome-scale. An open question in the field is how to exactly interpret these complex datasets in a biological perspective. Here, we present a novel pSILAC phosphoproteomic dataset which was obtained during a dynamic process of cell starvation using data-independent acquisition MS (DIA-MS). To provide an unbiased “hypothesis-free” analysis framework, we developed a strategy to interrogate how phosphorylation dynamically impacts protein turnover across the time series data. With this strategy, we discovered a complex relationship between phosphorylation and protein turnover that was previously underexplored. Our results further revealed a link between phosphorylation stoichiometry with the turnover of phosphorylated peptidoforms. Moreover, our results suggested that phosphoproteomic turnover diversity cannot directly explain the abundance regulation of phosphorylation during cell starvation, underscoring the importance of future studies addressing PTM site-resolved protein turnover.     

Nutrient-responsive O-GlcNAcylation dynamically modulates the secretion of glycan-binding protein galectin 3

Endomembrane glycosylation and cytoplasmic O-GlcNAcylation each play essential roles in nutrient sensing, and characteristic changes in glycan patterns have been described in disease states such as diabetes and cancer. These changes in glycosylation have important functional roles and can drive disease progression. However, little is known about the molecular mechanisms underlying how these signals are integrated and transduced into biological effects. Galectins are proteins that bind glycans and that are secreted by a poorly characterized nonclassical secretory mechanism. Once outside the cell, galectins bind to the terminal galactose residues of cell surface glycans and modulate numerous extracellular functions, such as clathrin-independent endocytosis (CIE). Originating in the cytoplasm, galectins are predicted substrates for O-GlcNAc addition and removal; and as we have shown, galectin 3 is a substrate for O-GlcNAc transferase. In this study, we also show that galectin 3 secretion is sensitive to changes in O-GlcNAc levels. We determined using immunoprecipitation and Western blotting that there is a significant difference in O-GlcNAcylation status between cytoplasmic and secreted galectin 3. We observed dramatic alterations in galectin 3 secretion in response to nutrient conditions, which were dependent on dynamic O-GlcNAcylation. Importantly, we showed that these O-GlcNAc-driven alterations in galectin 3 secretion also facilitated changes in CIE. These results indicate that dynamic O-GlcNAcylation of galectin 3 plays a role in modulating its secretion and can tune its function in transducing nutrient-sensing information coded in cell surface glycosylation into biological effects.     

Ranking predicted protein structures with support vector regression

Protein structure prediction is an important problem of both intellectual and practical interest. Most protein structure prediction approaches generate multiple candidate models first, and then use a scoring function to select the best model among these candidates. In this work, we develop a scoring function using support vector regression (SVR). Both consensus-based features and features from individual structures are extracted from a training data set containing native protein structures and predicted structural models submitted to CASP5 and CASP6. The SVR learns a scoring function that is a linear combination of these features. We test this scoring function on two data sets. First, when used to rank server models submitted to CASP7, the SVR score selects predictions that are comparable to the best performing server in CASP7, Zhang-Server, and significantly better than all the other servers. Even if the SVR score is not allowed to select Zhang-Server models, the SVR score still selects predictions that are significantly better than all the other servers. In addition, the SVR is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE), and QA634 (Pcons). Second, this work aims to improve the ability of the Robetta server to select best models, and hence we evaluate the performance of the SVR score on ranking the Robetta server template-based models for the CASP7 targets. The SVR selects significantly better models than the Robetta K*Sync consensus alignment score.     

The permeability of the endoplasmic reticulum is dynamically coupled to protein synthesis

Proteins synthesized by the rough endoplasmic reticulum (RER) co-translationally cross the membrane through the pore of a ribosome-bound translocon (RBT) complex. Although this pore is also permeable to small molecules, it is generally thought that barriers to their permeation prevent the cyclical process of protein translation from affecting the permeability of the RER. We tested this hypothesis by culturing Chinese hamster ovary-S cells with inhibitors of protein translation that affect the occupancy of RBTs by nascent proteins and then permeabilizing the plasma membrane and measuring the permeability of the RER to a small molecule, 4-methyl-umbelliferyl-alpha-d-glucopyranoside (4-MalphaG). The premature or normal release of nascent proteins by puromycin or pactamycin, respectively, increased the permeability of the RER to 4-MalphaG by 20-30%. In contrast, inhibition of elongation and the release of nascent proteins by cycloheximide did not increase the permeability, but it prevented the increase in permeability by pactamycin. We conclude that the permeability of the RER is coupled to protein translation by a simple gating mechanism whereby a nascent protein blocks the pore of a RBT during translation, but after release of the nascent protein the pore is permeable to small molecules as long as an empty ribosome remains bound to the translocon.     

Interaction of 14-3-3 protein with regulator of G protein signaling 7 is dynamically regulated by tumor necrosis factor-alpha

Regulators of G protein signaling (RGS) constitute a family of proteins with a conserved RGS domain of approximately 120 amino acids that accelerate the intrinsic GTP hydrolysis of activated Galpha(i) and Galpha(q) subunits. The phosphorylation-dependent interaction of 14-3-3 proteins with a subset of RGS proteins inhibits their GTPase-accelerating activity in vitro. The inhibitory interaction between 14-3-3 and RGS7 requires phosphorylation of serine 434 of RGS7. We now show that phosphorylation of serine 434 is dynamically regulated by TNF-alpha. Cellular stimulation by TNF-alpha transiently decreased the phosphorylation of serine 434 of RGS7, abrogating the inhibitory interaction with 14-3-3. We examined the effect of 14-3-3 on RGS-mediated deactivation kinetics of G protein-coupled inwardly rectifying K(+) channels (GIRKs) in Xenopus oocytes. 14-3-3 inhibited the function of wild-type RGS7, but not that of either RSG7(P436R) or RGS4, two proteins that do not bind 14-3-3. Our findings are the first evidence that extracellular signals can modulate the activity of RGS proteins by regulating their interaction with 14-3-3.     

Tripeptide analysis of protein structures

Background  

An efficient building block for protein structure prediction can be tripeptides. 8000 different tripeptides from a dataset of 1220 high resolution (≤ 2.0°A) structures from the Protein Data Bank (PDB) have been looked at, to determine which are structurally rigid and non-rigid. This data has been statistically analyzed, discussed and summarized. The entire data can be utilized for the building of protein structures.     

Knowledge-based prediction of protein structures

We propose a knowledge-based approach to the prediction of protein structures in cases where there is no sequence-homology to proteins with known spatial structure. Using methods from Artificial Intelligence we attempt to take into account long-range interactions within the prediction process. This allows not only the assignment of secondary but also of supersecondary structure elements. In particular, the patterns used as conditions of prediction rules are generated by learning methods from information contained in the Protein Data Base. Patterns on higher levels of the protein structure hierarchy are used as constraints to reduce the combinatorial search space. These patterns may also be used to search for specified structure motifs by interactive retrieval.