A network representation of protein structures: implications for protein stability

This study views each protein structure as a network of noncovalent connections between amino acid side chains. Each amino acid in a protein structure is a node, and the strength of the noncovalent interactions between two amino acids is evaluated for edge determination. The protein structure graphs (PSGs) for 232 proteins have been constructed as a function of the cutoff of the amino acid interaction strength at a few carefully chosen values. Analysis of such PSGs constructed on the basis of edge weights has shown the following: 1), The PSGs exhibit a complex topological network behavior, which is dependent on the interaction cutoff chosen for PSG construction. 2), A transition is observed at a critical interaction cutoff, in all the proteins, as monitored by the size of the largest cluster (giant component) in the graph. Amazingly, this transition occurs within a narrow range of interaction cutoff for all the proteins, irrespective of the size or the fold topology. And 3), the amino acid preferences to be highly connected (hub frequency) have been evaluated as a function of the interaction cutoff. We observe that the aromatic residues along with arginine, histidine, and methionine act as strong hubs at high interaction cutoffs, whereas the hydrophobic leucine and isoleucine residues get added to these hubs at low interaction cutoffs, forming weak hubs. The hubs identified are found to play a role in bringing together different secondary structural elements in the tertiary structure of the proteins. They are also found to contribute to the additional stability of the thermophilic proteins when compared to their mesophilic counterparts and hence could be crucial for the folding and stability of the unique three-dimensional structure of proteins. Based on these results, we also predict a few residues in the thermophilic and mesophilic proteins that can be mutated to alter their thermal stability.     

Predicting protein-protein interaction by searching evolutionary tree automorphism space

MOTIVATION: Uncovering the protein-protein interaction network is a fundamental step in the quest to understand the molecular machinery of a cell. This motivates the search for efficient computational methods for predicting such interactions. Among the available predictors are those that are based on the co-evolution hypothesis "evolutionary trees of protein families (that are known to interact) are expected to have similar topologies". Many of these methods are limited by the fact that they can handle only a small number of protein sequences. Also, details on evolutionary tree topology are missing as they use similarity matrices in lieu of the trees. RESULTS: We introduce MORPH, a new algorithm for predicting protein interaction partners between members of two protein families that are known to interact. Our approach can also be seen as a new method for searching the best superposition of the corresponding evolutionary trees based on tree automorphism group. We discuss relevant facts related to the predictability of protein-protein interaction based on their co-evolution. When compared with related computational approaches, our method reduces the search space by approximately 3 x 10(5)-fold and at the same time increases the accuracy of predicting correct binding partners.     

Identification of domains and domain interface residues in multidomain proteins from graph spectral method

We present a novel method for the identification of structural domains and domain interface residues in proteins by graph spectral method. This method converts the three-dimensional structure of the protein into a graph by using atomic coordinates from the PDB file. Domain definitions are obtained by constructing either a protein backbone graph or a protein side-chain graph. The graph is constructed based on the interactions between amino acid residues in the three-dimensional structure of the proteins. The spectral parameters of such a graph contain information regarding the domains and subdomains in the protein structure. This is based on the fact that the interactions among amino acids are higher within a domain than across domains. This is evident in the spectra of the protein backbone and the side-chain graphs, thus differentiating the structural domains from one another. Further, residues that occur at the interface of two domains can also be easily identified from the spectra. This method is simple, elegant, and robust. Moreover, a single numeric computation yields both the domain definitions and the interface residues.     

In Silico Modeling of Human α2C-Adrenoreceptor Interaction with Filamin-2

Vascular smooth muscle α_2C-adrenoceptors (α_2C-ARs) mediate vasoconstriction of small blood vessels, especially arterioles. Studies of endogenous receptors in human arteriolar smooth muscle cells (referred to as microVSM) and transiently transfected receptors in heterologous HEK293 cells show that the α_2C-ARs are perinuclear receptors that translocate to the cell surface under cellular stress and elicit a biological response. Recent studies in microVSM unraveled a crucial role of Rap1A-Rho-ROCK-F-actin pathways in receptor translocation, and identified protein-protein interaction of α_2C-ARs with the actin binding protein filamin-2 as an essential step in the process. To better understand the molecular nature and specificity of this interaction, in this study, we constructed comparative models of human α_2C-AR and human filamin-2 proteins. Finally, we performed in silico protein-protein docking to provide a structural platform for the investigation of human α_2C-AR and filamin-2 interactions. We found that electrostatic interactions seem to play a key role in this complex formation which manifests in interactions between the C-terminal arginines of α_2C-ARs (particularly R454 and R456) and negatively charged residues from filamin-2 region between residues 1979 and 2206. Phylogenetic and sequence analysis showed that these interactions have evolved in warm-blooded animals.     

Quantum clustering and network analysis of MD simulation trajectories to probe the conformational ensembles of protein-ligand interactions

In this article, we present a novel application of a quantum clustering (QC) technique to objectively cluster the conformations, sampled by molecular dynamics simulations performed on different ligand bound structures of the protein. We further portray each conformational population in terms of dynamically stable network parameters which beautifully capture the ligand induced variations in the ensemble in atomistic detail. The conformational populations thus identified by the QC method and verified by network parameters are evaluated for different ligand bound states of the protein pyrrolysyl-tRNA synthetase (DhPylRS) from D. hafniense. The ligand/environment induced re-distribution of protein conformational ensembles forms the basis for understanding several important biological phenomena such as allostery and enzyme catalysis. The atomistic level characterization of each population in the conformational ensemble in terms of the re-orchestrated networks of amino acids is a challenging problem, especially when the changes are minimal at the backbone level. Here we demonstrate that the QC method is sensitive to such subtle changes and is able to cluster MD snapshots which are similar at the side-chain interaction level. Although we have applied these methods on simulation trajectories of a modest time scale (20 ns each), we emphasize that our methodology provides a general approach towards an objective clustering of large-scale MD simulation data and may be applied to probe multistate equilibria at higher time scales, and to problems related to protein folding for any protein or protein-protein/RNA/DNA complex of interest with a known structure.     

Rule-based modelling of iron homeostasis in tuberculosis

To establish itself within the host system, Mycobacterium tuberculosis (Mtb) has formulated various means of attacking the host system. One such crucial strategy is the exploitation of the iron resources of the host system. Obtaining and maintaining the required concentration of iron becomes a matter of contest between the host and the pathogen, both trying to achieve this through complex molecular networks. The extent of complexity makes it important to obtain a systems perspective of the interplay between the host and the pathogen with respect to iron homeostasis. We have reconstructed a systems model comprising 92 components and 85 protein-protein or protein-metabolite interactions, which have been captured as a set of 194 rules. Apart from the interactions, these rules also account for protein synthesis and decay, RBC circulation and bacterial production and death rates. We have used a rule-based modelling approach, Kappa, to simulate the system separately under infection and non-infection conditions. Various perturbations including knock-outs and dual perturbation were also carried out to monitor the behavioral change of important proteins and metabolites. From this, key components as well as the required controlling factors in the model that are critical for maintaining iron homeostasis were identified. The model is able to re-establish the importance of iron-dependent regulator (ideR) in Mtb and transferrin (Tf) in the host. Perturbations, where iron storage is increased, appear to enhance nutritional immunity and the analysis indicates how they can be harmful for the host. Instead, decreasing the rate of iron uptake by Tf may prove to be helpful. Simulation and perturbation studies help in identifying Tf as a possible drug target. Regulating the mycobactin (myB) concentration was also identified as a possible strategy to control bacterial growth. The simulations thus provide significant insight into iron homeostasis and also for identifying possible drug targets for tuberculosis.     

A Hydrogen Bonded Chain in Bactereorhodopsin by Computer Modelling Approach

Abstract

The seven α-helical segments of Bacteriorhodopsin (BR) passing through the membrane are investigated for a continuous Hydrogen Bonded Chain (HBC). The study is carried out by computer modelling approach. It is assumed that the seven helices are placed as (AGFEDCB), which has been accepted as the best model by several groups. Helices A, D, E and G are considered to be present in right handed α-helical conformation. The inter-orientation of these helices are represented by Eulerian angles α, β and γ. For the helices B, C and F which contain Proline in the middle, several conformational possibilities were considered. In these cases apart from the Eulerian angles α, β and γ, the dihedral angles φ_{p_1} and ψ_{p_1} of the residues that are succeeded by Proline residue in the helical regions were also used in fixing the position of the helices with respect to each other. All these parameters were varied to fit with the top, middle and bottom distances reported by electron diffraction studies. Good fit was obtained for all right handed α-helical conformations and also for helices B, C and F with a left handed turn at the residue preceeding proline. Hence two structures were analysed for continuous HBC, Structure I which contained all the seven helices in right handed α-helical conformation and Structure II, which had the helices A, D, E and G in right handed conformation and the helices B, C and F in right handed α-helical conformation with a left handed turn at the residue preceeding proline. All possible staggered conformations were considered for the side chains and the inter atomic distances were analysed for Hydrogen bonds. It was possible to obtain a continuous chain in both the structures which includes most of the residues found to be important by the experiments. However Lys-216 has to be considered in two different conformations to connect the cytoplasmic side with the extra cellular side. The overall height spanned by HBC is about 25Å. The chains obtained by both the structures I and II are analysed in terms of the conformational parameters. It has also been possible to place the retinal in the region as predicted by the experiments. The Tryptophan residues which affect the spectral characterestics can be aligned on either side of the retinal.     

Corrosion and Alloy Engineering in Rational Design of High Current Density Electrodes for Efficient Water Splitting

Alongside rare‐earth metals, Ni, Fe, Co, Cu are some of the critical materials that will be in huge demand thanks to growth in clean‐energy sector. Herein scrap stainless steel wires (SSW) from worn‐out tires are employed as a support material for catalyst integration in the hydrogen evolution reaction (HER). In addition, SSW by corrosion engineering is exercised as an in situ formed freestanding robust electrode for the oxygen evolution reaction (OER). By superficial corrosion of SSW, inherent active species are unmasked in the form of Ni/FeOOH nanocrystallites displaying efficient water oxidation by reaching 500 mA cm^?2 at low overpotential (η₅₀₀) of 287 mV in 1 m KOH. Similarly, cathode scrap SSW with active (alloy) coatings of MoNi₄ catalyzes the HER at η_‐200 = 77 mV, with a low activation energy (E_a = 16.338 kJ mol^?1) and high durability of 150 h. Promisingly, when used in industrial conditions, 5 m KOH, 343 K, these electrodes demonstrate abnormal activity by yielding high anodic and cathodic current density of 1000 mA cm^?2 at η = 233 mV and η = 161 mV, respectively. This work may inspire researchers to explore and reutilize high‐demand metals from scrap for addressing critical material shortfalls in clean‐energy technologies.     

Structure networks of E. coli glutaminyl-tRNA synthetase: effects of ligand binding

It is well known that proteins undergo backbone as well as side chain conformational changes upon ligand binding, which is not necessarily confined to the active site. Both the local and the global conformational changes brought out by ligand-binding have been extensively studied earlier. However, the global changes have been reported mainly at the protein backbone level. Here we present a method that explicitly takes into account the side chain interactions, yet providing a global view of the ligand-induced conformational changes. This is achieved through the analysis of Protein Structure Networks (PSN), constructed from the noncovalent side chain interactions in the protein. Here, E. coli Glutaminyl-tRNA synthetase (GlnRS) in the ligand-free and different ligand-bound states is used as a case study to assess the effect of binding of tRNA, ATP, and the amino acid Gln to GlnRS. The PSNs are constructed on the basis of the strength of noncovalent interactions existing between the side chains of amino acids. The parameters like the size of the largest cluster, edge to node ratio, and the total number of hubs are used to quantitatively assess the structure network changes. These network parameters have effectively captured the ligand-induced structural changes at a global structure network level. Hubs, the highly connected amino acids, are also identified from these networks. Specifically, we are able to characterize different types of hubs based on the comparison of structure networks of the GlnRS system. The differences in the structure networks in both the presence and the absence of the ligands are reflected in these hubs. For instance, the characterization of hubs that are present in both the ligand-free and all the ligand-bound GlnRS (the invariant hubs) might implicate their role in structural integrity. On the other hand, identification of hubs unique to a particular ligand-bound structure (the exclusive hubs) not only highlights the structural differences mediated by ligand-binding at the structure network level, but also highlights significance of these amino acids hubs in binding to the ligand and catalyzing the biochemical function. Further, the hubs identified from this study could be ideal targets for mutational studies to ascertain the ligand-induced structure-function relationships in E. coli GlnRS. The formalism used in this study is simple and can be applied to other protein-ligands in general to understand the allosteric changes mediated by the binding of ligands.