Assessing significance of connectivity and conservation in protein interaction networks.

Comparative analyses of cellular interaction networks enable understanding of the cell's modular organization through identification of functional modules and complexes. These techniques often rely on topological features such as connectedness and density, based on the premise that functionally related proteins are likely to interact densely and that these interactions follow similar evolutionary trajectories. Significant recent work has focused on efficient algorithms for identification of such functional modules and their conservation. In spite of algorithmic advances, development of a comprehensive infrastructure for interaction databases is in relative infancy compared to corresponding sequence analysis tools. One critical, and as yet unresolved aspect of this infrastructure is a measure of the statistical significance of a match, or a dense subcomponent. In the absence of analytical measures, conventional methods rely on computationally expensive simulations based on ad-hoc models for quantifying significance. In this paper, we present techniques for analytically quantifying statistical significance of dense components in reference model graphs. We consider two reference models--a G(n, p) model in which each pair of nodes in a graph has an identical likelihood, p, of sharing an edge, and a two-level G(n, p) model, which accounts for high-degree hub nodes generally observed in interaction networks. Experiments performed on a rich collection of protein interaction (PPI) networks show that the proposed model provides a reliable means of evaluating statistical significance of dense patterns in these networks. We also adapt existing state-of-the-art network clustering algorithms by using our statistical significance measure as an optimization criterion. Comparison of the resulting module identification algorithm, SIDES, with existing methods shows that SIDES outperforms existing algorithms in terms of sensitivity and specificity of identified clusters with respect to available GO annotations.     

基于序列特征网络的蛋白质结构类型研究

利用复杂网络的方法来探索序列特征因素对蛋白质结构的影响。由于蛋白质的序列对结构具有重要且复杂的影响,因此将蛋白质的结构以及序列特征之间的关系模拟成一个复杂系统,通过利用互相关系数、标准化互信息和传递熵等方法来建立以序列特征为节点的加权网络,进而利用网络中心性的方法来分析不同蛋白质结构类型对应加权网络的中心性分布的差异,探索不同结构类型蛋白质的序列特征差异。发现不同的蛋白质结构类型对应的序列特征网络既有共性又有差异,文章将针对每一种结构类型的网络中心性分布,以及不同结构类型之间的共性与差异进行详细地讨论。研究结果对蛋白质序列与结构之间关系的研究,特别是结构分类研究具有重要的意义。     

Water structural changes involved in the activation process of photoactive yellow protein

Fourier transform infrared (FTIR) spectroscopy was applied to the blue-light photoreceptor photoactive yellow protein (PYP) to investigate water structural changes possibly involved in the photocycle of PYP. Photointermediates were stabilized at low temperature, and difference IR spectra were obtained between intermediate states and the original state of PYP (pG). Water structural changes were never observed in the >3570 cm(-)(1) region for the intermediates stabilized at 77-250 K, such as the red-shifted pR and blue-shifted pB intermediates. In contrast, a negative band was observed at 3658 cm(-)(1) in the pB minus pG spectrum at 295 K, which shifts to 3648 cm(-)(1) upon hydration with H(2)(18)O. The high frequency of the O-H stretch of water indicates that the water O-H group does not form hydrogen bonds in pG, and newly forms these upon pB formation at 295 K, but not at 250 K. Among 92 water molecules in the crystal structure of PYP, only 1 water molecule, water-200, is present in a hydrophobic core inside the protein. The amide N-H of Gly-7 and the imidazole nitrogen atom of His-108 are its possible hydrogen-bonding partners, indicating that one O-H group of water-200 is free to form an additional hydrogen bond. The water band at 3658 cm(-)(1) was indeed diminished in the H108F protein, which strongly suggests that the water band originates from water-200. Structural changes of amide bands in pB were much greater in the wild-type protein at 295 K than at 250 K or in the H108F protein at 295 K. The position of water-200 is >15 A remote from the chromophore. Virtually no structural changes were reported for regions larger than a few angstroms away from the chromophore, in the time-resolved X-ray crystallography experiments on pB. On the basis of the present results, as well as other spectroscopic observations, we conclude that water-200 (buried in a hydrophobic core in pG) is exposed to the aqueous phase upon formation of pB in solution. In neither crystalline PYP nor at low temperature is this structural transition observed, presumably because of the restrictions on global structural changes in the protein under these conditions.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.     

6-Thioguanine in DNA as CD-spectroscopic probe to study local structural changes upon protein binding

A combination of experimental and theoretical circular dichroism (CD) spectroscopy was used to study local deformations of DNA caused by binding of the base flipping DNA methyltransferase M.TaqI. To selectively study the structural changes within the DNA, we replaced single guanine residues at six different positions in duplex DNA with 6-thioguanine (s(6)G), which absorbs at 342 nm where unmodified DNA and the enzyme are transparent. The shape and the transition wavelength of a CD signal around 340 nm in the spectra of the free DNA and the M.TaqI-bound DNA were found to depend on the position of the s(6)G probe. Theoretical rotational strengths were calculated employing the matrix method which is frequently used to model the CD of large biomolecules. The only chromophores in these calculations were the nucleic acid bases. Comparison of the measured and the calculated CD spectra showed that the applied computational method qualitatively reproduces the dominant band observed around 340 nm in all cases. From our results we conclude that the spectral changes observed upon binding of the enzyme to the DNA are indeed predominantly due to structural changes within the DNA and not to other effects caused by the presence of the enzyme.     

UV Resonance Raman explores protein structural modification upon fibrillation and ligand interaction

Amyloids are proteinaceous deposits considered an underlying pathological hallmark of several degenerative diseases. The mechanism of amyloid formation and its inhibition still represent challenging issues, especially when protein structure cannot be investigated by classical biophysical techniques as for the intrinsically disordered proteins (IDPs). In this view, the need to find an alternative way for providing molecular and structural information regarding IDPs prompted us to set a novel, to our knowledge, approach focused on UV Resonance Raman (UVRR) spectroscopy. To test its applicability, we study the fibrillation of hen-egg white lysozyme (HEWL) and insulin as well as their interaction with resveratrol, employing also intrinsic fluorescence spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and atomic force microscopy (AFM). The increasing of the β-sheet structure content at the end of protein fibrillation probed by FTIR occurs simultaneously with a major solvent exposure of tryptophan (Trp) and tyrosine (Tyr) residues of HEWL and insulin, respectively, as revealed by UVRR and intrinsic fluorescence spectroscopy. However, because the latter technique is successfully used when proteins naturally contain Trp residues, it shows poor performances in the case of insulin, and the information regarding its tertiary structure is exclusively provided by UVRR spectroscopy. The presence of an increased concentration of resveratrol induces mild changes in the secondary structure of both protein fibrils while remodeling HEWL fibril length and promoting the formation of amorphous aggregates in the case of insulin. Although the intrinsic fluorescence spectra of proteins are hidden by resveratrol signal, UVRR Trp and Tyr bands are resonantly enhanced, showing a good sensitivity to the presence of resveratrol and marking a modification in the noncovalent interactions in which they are involved. Our findings demonstrate that UVRR is successfully employed in the study of aggregation-prone proteins and of their interaction with ligands, especially in the case of Trp-lacking proteins.     

Structural changes in the BH3 domain of SOUL protein upon interaction with the anti-apoptotic protein Bcl-xL

The SOUL protein is known to induce apoptosis by provoking the mitochondrial permeability transition, and a sequence homologous with the BH3 (Bcl-2 homology 3) domains has recently been identified in the protein, thus making it a potential new member of the BH3-only protein family. In the present study, we provide NMR, SPR (surface plasmon resonance) and crystallographic evidence that a peptide spanning residues 147-172 in SOUL interacts with the anti-apoptotic protein Bcl-xL. We have crystallized SOUL alone and the complex of its BH3 domain peptide with Bcl-xL, and solved their three-dimensional structures. The SOUL monomer is a single domain organized as a distorted β-barrel with eight anti-parallel strands and two α-helices. The BH3 domain extends across 15 residues at the end of the second helix and eight amino acids in the chain following it. There are important structural differences in the BH3 domain in the intact SOUL molecule and the same sequence bound to Bcl-xL.     

Hydrophobic interaction and structural changes in the solvent

The effect of structural changes in the solvent (usually water) on the thermodynamics of the hydrophobic interaction process is examined within the framework of classical statistical mechanics. The concept of the “structure of water” is first defined in a precise way, yet reflecting the conventional definition that has been, implicitly and qualitatively, employed by many authors. Using this concept, we proceed to show that structural changes in the solvent, induced by the hydrophobic interaction process, cannot affect the strength of the hydrophobic interaction. On the other hand, the entropy and enthalpy changes, associated with the same process, may well be affected. Some qualitative arguments are presented showing that large structural changes are expected from a complex solvent such as water.     

Consistency in structural energetics of protein folding and peptide recognition.

We report a new free energy decomposition that includes structure-derived atomic contact energies for the desolvation component, and show that it applies equally well to the analysis of single-domain protein folding and to the binding of flexible peptides to proteins. Specifically, we selected the 17 single-domain proteins for which the three-dimensional structures and thermodynamic unfolding free energies are available. By calculating all terms except the backbone conformational entropy change and comparing the result to the experimentally measured free energy, we estimated that the mean entropy gain by the backbone chain upon unfolding (delta Sbb) is 5.3 cal/K per mole of residue, and that the average backbone entropy for glycine is 6.7 cal/K. Both numbers are in close agreement with recent estimates made by entirely different methods, suggesting a promising degree of consistency between data obtained from disparate sources. In addition, a quantitative analysis of the folding free energy indicates that the unfavorable backbone entropy for each of the proteins is balanced predominantly by favorable backbone interactions. Finally, because the binding of flexible peptides to receptors is physically similar to folding, the free energy function should, in principle, be equally applicable to flexible docking. By combining atomic contact energies, electrostatics, and sequence-dependent backbone entropy, we calculated a priori the free energy changes associated with the binding of four different peptides to HLA-A2, 1 MHC molecule and found agreement with experiment to within 10% without parameter adjustment.     

A systematic survey of centrality measures for protein-protein interaction networks

Background

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results

We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions

The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

     

A study of energetics of cooperative interaction using a mutant lambda-repressor

A lambda-repressor mutant, S228N, which is defective in tetramer formation in the free state but retains full cooperativity, was studied in detail. Isolated single operator-bound S228N repressor shows association properties similar to those of the wild-type repressor. Fluorescence anisotropy studies with dansyl chloride-labeled repressor show a dimer-monomer dissociation constant of around 10(-5) M. The structure of the mutant repressor was studied by circular dichroism, acrylamide quenching and sulfhydryl reactivity at protein concentrations of < or =10(-6) M, where it is predominantly monomeric. The results suggest no significant perturbations in the structure of the S228N mutant repressor from that of the wild-type repressor. Urea denaturation studies also indicate no significant change in the stability of the repressor. The results were used to calculate energetics of loop formation in the cooperative binding process.     

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.     

Antagonism and bistability in protein interaction networks

A protein interaction network (PIN) is a set of proteins that modulate one another's activities by regulated synthesis and degradation, by reversible binding to form complexes, and by catalytic reactions (e.g., phosphorylation and dephosphorylation). Most PINs are so complex that their dynamical characteristics cannot be deduced accurately by intuitive reasoning alone. To predict the properties of such networks, many research groups have turned to mathematical models (differential equations based on standard biochemical rate laws, e.g., mass-action, Michaelis-Menten, Hill). When using Michaelis-Menten rate expressions to model PINs, care must be exercised to avoid making inconsistent assumptions about enzyme-substrate complexes. We show that an appealingly simple model of a PIN that functions as a bistable switch is compromised by neglecting enzyme-substrate intermediates. When the neglected intermediates are put back into the model, bistability of the switch is lost. The theory of chemical reaction networks predicts that bistability can be recovered by adding specific reaction channels to the molecular mechanism. We explore two very different routes to recover bistability. In both cases, we show how to convert the original 'phenomenological' model into a consistent set of mass-action rate laws that retains the desired bistability properties. Once an equivalent model is formulated in terms of elementary chemical reactions, it can be simulated accurately either by deterministic differential equations or by Gillespie's stochastic simulation algorithm.     

Shaping dots and lines: adding modularity into protein interaction networks using structural information

Determining protein interaction networks and generating models to simulate network changes in time and space are crucial for understanding a biological system and for predicting the effect of mutants found in diseases. In this review we discuss the great potential of using structural information together with computational tools towards reaching this goal: the prediction of new protein interactions, the estimation of affinities and kinetic rate constants between protein complexes, and finally the determination of which interactions are compatible with each other and which interactions are exclusive. The latter one will be important to reorganize large scale networks into functional modular networks.     

Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map)

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.     

Direct measurement of the energetics of association between myelin basic protein and phosphatidylserine vesicles

A newly designed high-sensitivity isothermal reaction calorimetry system has been used to investigate the thermodynamics of the association between myelin basic protein and phosphatidylserine vesicles. This instrument has allowed us to measure directly the energetics of the protein-lipid interaction under various conditions. Above the phospholipid phase transition temperature the enthalpy of association is highly exothermic amounting to -160 kcal/mol of protein. Below the phospholipid phase transition temperature the enthalpy of association is exothermic at protein/lipid ratios smaller than 1/50 and endothermic at higher protein/lipid ratios. These studies indicate that the association of myelin basic protein to phosphatidylserine vesicles consists of at least two stages involving different types of binding. The first stage, at low protein/lipid ratios, involves a strong exothermic association of the protein to the membrane and the second, at high protein/lipid ratios, a weaker association probably involving attachment of the protein to the membrane surface only. In the gel phase the second binding stage is endothermic and appears to be correlated with the formation of large vesicle aggregates. This vesicle aggregation is a reversible process dependent upon the physical state of the membrane. The isothermal titration studies have been complemented with high-sensitivity differential scanning calorimetry experiments. It is shown that the dependence of the phospholipid transition enthalpy on the protein/lipid molar ratio can be expressed in terms of the different protein-membrane association enthalpies in the gel and fluid phases of the membrane.     

Kinetic and structural study of the interaction of myelin basic protein with dipalmitoylphosphatidylglycerol layers

The interaction of myelin basic protein (MBP) with dipalmitoylphosphatidylglycerol films has been investigated by means of a microgravimetric gauge sensitive to the changes in load and structural modifications of the layer deposited onto its surface. Fourier transform infrared spectroscopy, circular dichroism, and x-ray diffraction have confirmed protein uptake by the lipid phase along with a global disordering effect onto the lipid alkyl chains and have shown a temporal evolution of the structure of water penetrating the lipid phase together with the protein. These effects are clearly related to the temporal variation of the microgravimetric gauge signal. Finally, measurements carried out on pre-annealed samples point out the role of mesoscopic morphology in determining the pathways through which MBP penetrates the lipid multilayer. The results obtained in our model system could be useful in clarifying the mechanisms of the myelinating and demyelinating processes that take place in the natural membrane.     

Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.     

Probing the extent of randomness in protein interaction networks

Protein-protein interaction (PPI) networks are commonly explored for the identification of distinctive biological traits, such as pathways, modules, and functional motifs. In this respect, understanding the underlying network structure is vital to assess the significance of any discovered features. We recently demonstrated that PPI networks show degree-weighted behavior, whereby the probability of interaction between two proteins is generally proportional to the product of their numbers of interacting partners or degrees. It was surmised that degree-weighted behavior is a characteristic of randomness. We expand upon these findings by developing a random, degree-weighted, network model and show that eight PPI networks determined from single high-throughput (HT) experiments have global and local properties that are consistent with this model. The apparent random connectivity in HT PPI networks is counter-intuitive with respect to their observed degree distributions; however, we resolve this discrepancy by introducing a non-network-based model for the evolution of protein degrees or "binding affinities." This mechanism is based on duplication and random mutation, for which the degree distribution converges to a steady state that is identical to one obtained by averaging over the eight HT PPI networks. The results imply that the degrees and connectivities incorporated in HT PPI networks are characteristic of unbiased interactions between proteins that have varying individual binding affinities. These findings corroborate the observation that curated and high-confidence PPI networks are distinct from HT PPI networks and not consistent with a random connectivity. These results provide an avenue to discern indiscriminate organizations in biological networks and suggest caution in the analysis of curated and high-confidence networks.     

A comparative study of electronic and neural networks involved in pattern recognition

The comparative study of electronic and neural networks involved in pattern recognition starts with the analogies of structure and function which exist between the electronic “basic integrative unit” and the neuron. Both elements represent the basic components in each system of networks and may be considered as functionally equivalent.According to the kind of response given to a standard input signal, four types of integrative units, either electronic or neural, may be distinguished: the fixed, the accommodative, the signal prolongating and the adaptive type.The integrative units perform many different functions. Those involved in pattern recognition, however, can all be grouped into three categories according to one of the following functions they perform: contrast detection, pattern detection and pattern discrimination. A “contrast detecting unit” gives responses in two senses, positive or negative, according to the position of the stimulus over its receptive field. A “pattern detecting unit” gives responses in one sense only, with a maximum for a pattern having the spatial distribution corresponding to the positive acting receptors of its receptive field. For performing the function of discrimination, which leads to reliable identification of any pattern, a network arrangement called a “maximum amplitude filter” is necessary. Examples of such units and arrangements existing in the nervous system are provided.It is concluded that a “logical analysis of neural networks” based on engineering principles is possible and that this could provide a new tool to the neurophysiologist in the study of the nervous system.