Observation of ultracold atomic bubbles in orbital microgravity.

Substantial leaps in the understanding of quantum systems have been driven by exploring geometry, topology, dimensionality and interactions in ultracold atomic ensembles1-6. A system where atoms evolve while confined on an ellipsoidal surface represents a heretofore unexplored geometry and topology. Realizing an ultracold bubble-potentially Bose-Einstein condensed-relates to areas of interest including quantized-vortex flow constrained to a closed surface topology, collective modes and self-interference via bubble expansion7-17. Large ultracold bubbles, created by inflating smaller condensates, directly tie into Hubble-analogue expansion physics18-20. Here we report observations from the NASA Cold Atom Lab21 facility onboard the International Space Station of bubbles of ultracold atoms created using a radiofrequency-dressing protocol. We observe bubble configurations of varying size and initial temperature, and explore bubble thermodynamics, demonstrating substantial cooling associated with inflation. We achieve partial coverings of bubble traps greater than one millimetre in size with ultracold films of inferred few-micrometre thickness, and we observe the dynamics of shell structures projected into free-evolving harmonic confinement. The observations are among the first measurements made with ultracold atoms in space, using perpetual freefall to explore quantum systems that are prohibitively difficult to create on Earth. This work heralds future studies (in orbital microgravity) of the Bose-Einstein condensed bubble, the character of its excitations and the role of topology in its evolution.

Weighting schemes in metabolic graphs for identifying biochemical routes.

Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes [Formula: see text] fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks.

Network properties of decoys and CASP predicted models: a comparison with native protein structures.

Protein structure space is believed to consist of a finite set of discrete folds, unlike the protein sequence space which is astronomically large, indicating that proteins from the available sequence space are likely to adopt one of the many folds already observed. In spite of extensive sequence-structure correlation data, protein structure prediction still remains an open question with researchers having tried different approaches (experimental as well as computational). One of the challenges of protein structure prediction is to identify the native protein structures from a milieu of decoys/models. In this work, a rigorous investigation of Protein Structure Networks (PSNs) has been performed to detect native structures from decoys/models. Ninety four parameters obtained from network studies have been optimally combined with Support Vector Machines (SVM) to derive a general metric to distinguish decoys/models from the native protein structures with an accuracy of 94.11%. Recently, for the first time in the literature we had shown that PSN has the capability to distinguish native proteins from decoys. A major difference between the present work and the previous study is to explore the transition profiles at different strengths of non-covalent interactions and SVM has indeed identified this as an important parameter. Additionally, the SVM trained algorithm is also applied to the recent CASP10 predicted models. The novelty of the network approach is that it is based on general network properties of native protein structures and that a given model can be assessed independent of any reference structure. Thus, the approach presented in this paper can be valuable in validating the predicted structures. A web-server has been developed for this purpose and is freely available at .

Dynamics of the native and the ligand-bound structures of eosinophil cationic protein: network of hydrogen bonds at the catalytic site.

Eosinophil Cationic Protein (ECP) is sequentially and structurally similar to ribonuclease A (RNase A). It belongs to the RNase A family of proteins and the RNA catalysis is essential to its biological function. In the present study, we have generated the dinucleotide-bound structures of ECP by docking the dinucleotides to a number of molecular dynamics (MD) generated ECP structures. The stability of the docked enzyme-ligand complexes was ascertained by extensive MD simulations. The modes of ligand binding are explored by essential dynamics studies. The role of water molecules in the stability of the complex and in the catalysis was investigated. The active site residues form a complex network of connections with the ligand and with a water molecule. The catalytic mechanism of the RNA cleavage is examined on the basis of the active site geometry obtained by the simulations.

Conformational transitions in eosinophil cationic protein: a molecular dynamics study in aqueous environment.

Extensive molecular dynamics simulations have been performed on eosinophil cationic protein (ECP). The two structures found in the crystallographic dimer (ECPA and ECPB) have been independently simulated. A small difference in the pattern of the sidechain hydrogen bonds in the starting structure has resulted in interesting differences in the conformations accessed during the simulations. In one simulation (ECPB), a stable equilibrium conformation was obtained and in the other (ECPA), conformational transitions at the level of sidechain interactions were observed. The conformational transitions exhibit the involvement of the solvent (water) molecules with a pore-like construct in the equilibrium conformation and an opening for a large number of water molecules during the transition phase. The details of these transitions are examined in terms of intra-protein hydrogen bonds, protein-water networks and the residence times of water molecules on the polar atoms of the protein. These properties show some significant differences in the region between the N-terminal helix and the loop before the C-terminal strand as a function of different conformations accessed during the simulations. However, the stable hydrogen bonds, the protein-water networks, and the hydration patterns in most part of the protein including the active site are very much similar in both the simulations, indicating the fact that these are intrinsic properties of proteins.

Protein-water interactions in ribonuclease A and angiogenin: a molecular dynamics study.

It is known that water molecules play an important role in the biological functioning of proteins. The members of the ribonuclease A (RNase A) family of proteins, which are sequentially and structurally similar, are known to carry out the obligatory function of cleaving RNA and individually perform other diverse biological functions. Our focus is on elucidating whether the sequence and structural similarity lead to common hydration patterns, what the common hydration sites are and what the differences are. Extensive molecular dynamics simulations followed by a detailed analysis of protein-water interactions have been carried out on two members of the ribonuclease A superfamily-RNase A and angiogenin. The water residence times are analyzed and their relationship with the characteristic properties of the protein polar atoms, such as their accessible surface area and mean hydration, is studied. The capacity of the polar atoms to form hydrogen bonds with water molecules and participate in protein-water networks are investigated. The locations of such networks are identified for both proteins.

Analysis of homodimeric protein interfaces by graph-spectral methods.

The quaternary structures impart structural and functional credibility to proteins. In a multi-subunit protein, it is important to understand the factors that drive the association or dissociation of the subunits. It is a well known fact that both hydrophobic and charged interactions contribute to the stability of the protein interface. The interface residues are also known to be highly conserved. Though they are buried in the oligomer, these residues are either exposed or partially exposed in the monomer. It is felt that a systematic and objective method of identifying interface clusters and their analysis can significantly contribute to the identification of a residue or a collection of residues important for oligomerization. Recently, we have applied the techniques of graph-spectral methods to a variety of problems related to protein structure and folding. A major advantage of this methodology is that the problem is viewed from a global protein topology point of view rather than localized regions of the protein structure. In the present investigation, we have applied the methods of graph-spectral analysis to identify side chain clusters at the interface and the centers of these clusters in a set of homodimeric proteins. These clusters are analyzed in terms of properties such as amino acid composition, accessibility to solvent and conservation of residues. Interesting results such as participation of charged and aromatic residues like arginine, glutamic acid, histidine, phenylalanine and tyrosine, consistent with earlier investigations, have emerged from these analyses. Important additional information is that the residues involved are a part of a cluster(s) and that they are sequentially distant residues which have come closer to each other in the three-dimensional structure of the protein. These residues can easily be detected using our graph-spectral algorithm. This method has also been used to identify important residues ('hot spots') in dimerization and also to detect dimerization sites on the monomer. The residues predicted using the present algorithm have correlated well with the experiments indicating the efficacy of this method in predicting residues involved in dimer stability.

A molecular dynamics study based post facto free energy analysis of the binding of bovine angiogenin with UMP and CMP ligands.

Angiogenin is a protein belonging to the superfamily of RNase A. The RNase activity of this protein is essential for its angiogenic activity. Although members of the RNase A family carry out RNase activity, they differ markedly in their strength and specificity. In this paper, we address the problem of higher specificity of angiogenin towards cytosine against uracil in the first base binding position. We have carried out extensive nano-second level molecular dynamics(MD) computer simulations on the native bovine angiogenin and on the CMP and UMP complexes of this protein in aqueous medium with explicit molecular solvent. The structures thus generated were subjected to a rigorous free energy component analysis to arrive at a plausible molecular thermodynamic explanation for the substrate specificity of angiogenin.

Computer modeling and molecular dynamics simulations of ligand bound complexes of bovine angiogenin: dinucleotide topology at the active site of RNase a family proteins.

We have undertaken the modeling of substrate-bound structures of angiogenin. In our recent study, we modeled the dinucleotide ligand binding to human angiogenin. In the present study, the substrates CpG, UpG, and CpA were docked onto bovine angiogenin. This was achieved by overcoming the problem of an obstruction to the B1 site by the C-terminus and identifying residues that bind to the second base. The modeled complexes retain biochemically important interactions. The docked models were subjected to 1 ns of molecular dynamics, and structures from the simulation were refined by using simulated annealing. Our models explained the enzyme's specificity for both B1 and B2 bases as observed experimentally. The nature of binding of the dinucleotide substrate was compared with that of the mononucleotide product. The models of these complexes were also compared with those obtained earlier with human angiogenin. On the basis of the simulations and annealed structures, we came up with a consensus topology of dinucleotide ligands that binds to human and bovine angiogenins. This dinucleotide conformation can serve as a starting model for ligand-bound complex structures for RNase A family of proteins. We demonstrated this capability by generating the complex structure of CpA bound to eosinophil-derived neurotoxin (EDN) by fitting the consensus topology of CpA to the crystal structure of native EDN.

Deducing hydration sites of a protein from molecular dynamics simulations.

Invariant water molecules that are of structural or functional importance to proteins are detected from their presence in the same location in different crystal structures of the same protein or closely related proteins. In this study we have investigated the location of invariant water molecules from MD simulations of ribonuclease A, HIV1-protease and Hen egg white lysozyme. Snapshots of MD trajectories represent the structure of a dynamic protein molecule in a solvated environment as opposed to the static picture provided by crystallography. The MD results are compared to an analysis on crystal structures. A good correlation is observed between the two methods with more than half the hydration sites identified as invariant from crystal structures featuring as invariant in the MD simulations which include most of the functionally or structurally important residues. It is also seen that the propensities of occupying the various hydration sites on a protein for structures obtained from MD and crystallographic studies are different. In general MD simulations can be used to predict invariant hydration sites when there is a paucity of crystallographic data or to complement crystallographic results.

Stabilizing interactions in the dimer interface of alpha-subunit in Escherichia coli RNA polymerase: a graph spectral and point mutation study.

The formation of alpha(2) dimer in Escherichia coli core RNA polymerase (RNAP) is thought to be the first step toward the assembly of the functional enzyme. A large number of evidences indicate that the alpha-subunit dimerizes through its N-terminal domain (NTD). The crystal structures of the alpha-subunit NTD and that of a homologous Thermus aquaticus core RNAP are known. To identify the stabilizing interactions in the dimer interface of the alpha-NTD of E. coli RNAP, we identified side-chain clusters by using the crystal structure coordinates of E. coli alpha-NTD. A graph spectral algorithm was used to identify side-chain clusters. This algorithm considers the global nonbonded side-chain interactions of the residues for the clustering procedure and is unique in identifying residues that make the largest number of interactions among the residues that form clusters in a very quantitative way. By using this algorithm, a nine-residue cluster consisting of polar and hydrophobic residues was identified in the subunit interface adjacent to the hydrophobic core. The residues forming the cluster are relatively rigid regions of the interface, as measured by the thermal factors of the residues. Most of the cluster residues in the E. coli enzyme were topologically and sequentially conserved in the T. aquaticus RNAP crystal structure. Residues 35F and 46I were predicted to be important in the stability of the alpha-dimer interface, with 35F forming the center of the cluster. The predictions were tested by isolating single-point mutants alpha-F35A and alpha-I46S on the dimer interface, which were found to disrupt dimerization. Thus, the identified cluster at the edge of the dimer interface seems to be a vital component in stabilizing the alpha-NTD.

Short-strong hydrogen bonds and a low barrier transition state for the proton transfer reaction in RNase A catalysis: a quantum chemical study.

There is growing evidence that some enzymes catalyze reactions through the formation of short-strong hydrogen bonds as first suggested by Gerlt and Gassman. Support comes from several experimental and quantum chemical studies that include correlation energies on model systems. In the present study, the process of proton transfer between hydroxyl and imidazole groups, a model of the crucial step in the hydrolysis of RNA by the enzymes of the RNase A family, is investigated at the quantum mechanical level of density functional theory and perturbation theory at the MP2 level. The model focuses on the nature of the formation of a complex between the important residues of the protein and the hydroxyl group of the substrate. We have also investigated different configurations of the ground state that are important in the proton transfer reaction. The nature of bonding between the catalytic unit of the enzyme and the substrate in the model is investigated by Bader's atoms in molecule theory. The contributions of solvation and vibrational energies corresponding to the reactant, the transition state and the product configurations are also evaluated. Furthermore, the effect of protein environment is investigated by considering the catalytic unit surrounded by complete proteins--RNase A and Angiogenin. The results, in general, indicate the formation of a short-strong hydrogen bond and the formation of a low barrier transition state for the proton transfer model of the enzyme.

Clusters in alpha/beta barrel proteins: implications for protein structure, function, and folding: a graph theoretical approach.

The alpha/beta barrel fold is adopted by most enzymes performing a variety of catalytic reactions, but with very low sequence similarity. In order to understand the stabilizing interactions important in maintaining the alpha/beta barrel fold, we have identified residue clusters in a dataset of 36 alpha/beta barrel proteins that have less than 10% sequence identity within themselves. A graph theoretical algorithm is used to identify backbone clusters. This approach uses the global information of the nonbonded interaction in the alpha/beta barrel fold for the clustering procedure. The nonbonded interactions are represented mathematically in the form of an adjacency matrix. On diagonalizing the adjacency matrix, clusters and cluster centers are obtained from the highest eigenvalue and its corresponding vector components. Residue clusters are identified in the strand regions forming the beta barrel and are topologically conserved in all 36 proteins studied. The residues forming the cluster in each of the alpha/beta protein are also conserved among the sequences belonging to the same family. The cluster centers are found to occur in the middle of the strands or in the C-terminal of the strands. In most cases, the residues forming the clusters are part of the active site or are located close to the active site. The folding nucleus of the alpha/beta fold is predicted based on hydrophobicity index evaluation of residues and identification of cluster centers. The predicted nucleation sites are found to occur mostly in the middle of the strands. Proteins 2001;43:103-112.

Computer modeling of human angiogenin-dinucleotide substrate interaction.

Structures of substrate bound human angiogenin complexes have been obtained for the first time by computer modeling. The dinucleotides CpA and UpA have been docked onto human angiogenin using a systematic grid search procedure in torsion and Eulerian angle space. The docking was guided throughout by the similarity of angiogenin-substrate interactions with interactions of RNase A and its substrate. The models were subjected to 1 nanosecond of molecular dynamics to access their stability. Structures extracted from MD simulations were refined by simulated annealing. Stable hydrogen bonds that bridged protein and ligand residues during the MD simulations were taken as restraints for simulated annealing. Our analysis on the MD structures and annealed models explains the substrate specificity of human angiogenin and is in agreement with experimental results. This study also predicts the B2 binding site residues of angiogenin, for which no experimental information is available so far. In the case of one of the substrates, CpA, we have also identified the presence of a water molecule that invariantly bridges the B2 base with the protein. We have compared our results to the RNase A-substrate complex and highlight the similarities and differences.

Logos for amino-acid preferences in different backbone packing density regions of protein structural classes.

A protein sequence can be classified into one of four structural classes, namely alpha, beta, alpha + beta and alpha/beta, based on its amino-acid composition. The present study aims at understanding why a particular sequence with a given amino-acid composition should fold into a specific structural class. In order to answer this question, each amino acid in the protein sequence was classified to a particular neighbor density based on the number of spatial residues surrounding it within a distance of 6.5 A. Each of the four structural classes showed a unique preference of amino acids in each of the neighbor densities. Residues which show a high compositional bias in a structural class are also found to occur in high neighbor densities. This high compositional bias towards specific residues in the four different structural classes of proteins appears to be caused by structural and functional requirements. The distribution of amino acids in different neighbor densities is graphically presented in a novel logo form which incorporates several features such as composition, the frequency of occurrence and color code for amino acids. The spatial neighbors of the residues in different neighbor densities and their secondary structural location are also represented in the form of logos. This representation helped in the identification of specific details of the whole data which may otherwise have gone unnoticed. It is suggested that the data presented in this study may be useful in knowledge-based structure modelling and de novo protein design.

Backbone cluster identification in proteins by a graph theoretical method.

A graph theoretical algorithm has been developed to identify backbone clusters of residues in proteins. The identified clusters show protein sites with the highest degree of interactions. An adjacency matrix is constructed from the non-bonded connectivity information in proteins. The diagonalization of such a matrix yields eigenvalues and eigenvectors, which contain the information on clusters. In graph theory, distinct clusters can be obtained from the second lowest eigenvector components of the matrix. However, in an interconnected graph, all the points appear as one single cluster. We have developed a method of identifying highly interacting centers (clusters) in proteins by truncating the vector components of high eigenvalues. This paper presents in detail the method adopted for identifying backbone clusters and the application of the algorithm to families of proteins like RNase-A and globin. The objective of this study was to show the efficiency of the algorithm as well as to detect conserved or similar backbone packing regions in a particular protein family. Three clusters in topologically similar regions in the case of the RNase-A family and three clusters around the porphyrin ring in the globin family were observed. The predicted clusters are consistent with the features of the family of proteins such as the topology and packing density. The method can be applied to problems such as identification of domains and recognition of structural similarities in proteins.

Aromatic clusters: a determinant of thermal stability of thermophilic proteins.

A number of factors have been elucidated as responsible for the thermal stability of thermophilic proteins. However, the contribution of aromatic interactions to thermal stability has not been systematically studied. In the present investigation we used a graph spectral method to identify aromatic clusters in a dataset of 24 protein families for which the crystal structures of both the thermophilic and their mesophilic homologues are known. Our analysis shows a presence of additional aromatic clusters or enlarged aromatic networks in 17 different thermophilic protein families, which are absent in the corresponding mesophilic homologue. The additional aromatic clusters identified in the thermophiles are smaller in size and are largely found on the protein surface. The aromatic clusters are found to be relatively rigid regions of the surface and often the additional aromatic cluster is located close to the active site of the thermophilic enzyme. The residues in the additional aromatic clusters are preferably mutated to Leu, Ser or Ile in the mesophilic homologue. An analysis of the packing geometry of the pairwise aromatic interaction in the additional aromatic clusters shows a preference for a T-shaped orthogonal packing geometry. The present study also provides new insights for protein engineers to design thermostable and thermophilic proteins.

Identification of side-chain clusters in protein structures by a graph spectral method.

This paper presents a novel method to detect side-chain clusters in protein three-dimensional structures using a graph spectral approach. Protein side-chain interactions are represented by a labeled graph in which the nodes of the graph represent the Cbeta atoms and the edges represent the distance between the Cbeta atoms. The distance information and the non-bonded connectivity of the residues are represented in the form of a matrix called the Laplacian matrix. The constructed matrix is diagonalized and clustering information is obtained from the vector components associated with the second lowest eigenvalue and cluster centers are obtained from the vector components associated with the top eigenvalues. The method uses global information for clustering and a single numeric computation is required to detect clusters of interest. The approach has been adopted here to detect a variety of side-chain clusters and identify the residue which makes the largest number of interactions among the residues forming the cluster (cluster centers). Detecting such clusters and cluster centers are important from a protein structure and folding point of view. The crucial residues which are important in the folding pathway as determined by PhiF values (which is a measure of the effect of a mutation on the stability of the transition state of folding) as obtained from protein engineering methods, can be identified from the vector components corresponding to the top eigenvalues. Expanded clusters are detected near the active and binding site of the protein, supporting the nucleation condensation hypothesis for folding. The method is also shown to detect domains in protein structures and conserved side-chain clusters in topologically similar proteins.

Comparison of the dynamics of bovine and human angiogenin: a molecular dynamics study.

Molecular dynamics simulations have been carried out for 1 ns on human and bovine angiogenin systems in an effort to compare and contrast their dynamics. An analysis of their dynamics is done by examining the rms deviations, following hydrogen-bonding interactions and looking at the role of water in and around the protein. The C-terminus of bovine angiogenin moves appreciably during dynamics suggesting a better structure for ligand binding. However, we do not find any evidence of a conformation where the glutamate residue that obstructs the active site takes on a different conformation. We observe a differential hydrogen-bonding pattern in the active site regions of bovine and human angiogenins, which could have a bearing on the different catalytic activities of the proteins. We also propose that the differential binding of the monoclonal antibody toward the two proteins might be due sequential and not conformational differences. Water molecules might play an important functional role in both proteins given their subtle functional differences. A simple computation on the molecular dynamics data has been carried out to identify locations in and around the protein that are invariably occupied by water. The locations of nearly half the waters we have identified from the simulation as being invariant in bovine angiogenin occupy similar locations in the bovine angiogenin crystal structure. The positions of the waters identified in human angiogenin differ considerably from that of bovine angiogenin.

Structure-based design of model proteins.

A structure-based, sequence-design procedure is proposed in which one considers a set of decoy structures that compete significantly with the target structure in being low energy conformations. The decoy structures are chosen to have strong overlaps in contacts with the putative native state. The procedure allows the design of sequences with large and small stability gaps in a random-bond heteropolymer model in both two and three dimensions by an appropriate assignment of the contact energies to both the native and nonnative contacts. The design procedure is also successfully applied to the two-dimensional HP model.