Anharmonic Normal Mode Analysis of Elastic Network Model Improves the Modeling of Atomic Fluctuations in Protein Crystal Structures

Protein conformational dynamics, despite its significant anharmonicity, has been widely explored by normal mode analysis (NMA) based on atomic or coarse-grained potential functions. To account for the anharmonic aspects of protein dynamics, this study proposes, and has performed, an anharmonic NMA (ANMA) based on the C_α-only elastic network models, which assume elastic interactions between pairs of residues whose C_α atoms or heavy atoms are within a cutoff distance. The key step of ANMA is to sample an anharmonic potential function along the directions of eigenvectors of the lowest normal modes to determine the mean-squared fluctuations along these directions. ANMA was evaluated based on the modeling of anisotropic displacement parameters (ADPs) from a list of 83 high-resolution protein crystal structures. Significant improvement was found in the modeling of ADPs by ANMA compared with standard NMA. Further improvement in the modeling of ADPs is attained if the interactions between a protein and its crystalline environment are taken into account. In addition, this study has determined the optimal cutoff distances for ADP modeling based on elastic network models, and these agree well with the peaks of the statistical distributions of distances between C_α atoms or heavy atoms derived from a large set of protein crystal structures.     

Optimization of an Elastic Network Augmented Coarse Grained Model to Study CCMV Capsid Deformation

The major protective coat of most viruses is a highly symmetric protein capsid that forms spontaneously from many copies of identical proteins. Structural and mechanical properties of such capsids, as well as their self-assembly process, have been studied experimentally and theoretically, including modeling efforts by computer simulations on various scales. Atomistic models include specific details of local protein binding but are limited in system size and accessible time, while coarse grained (CG) models do get access to longer time and length scales but often lack the specific local interactions. Multi-scale models aim at bridging this gap by systematically connecting different levels of resolution. Here, a CG model for CCMV (Cowpea Chlorotic Mottle Virus), a virus with an icosahedral shell of 180 identical protein monomers, is developed, where parameters are derived from atomistic simulations of capsid protein dimers in aqueous solution. In particular, a new method is introduced to combine the MARTINI CG model with a supportive elastic network based on structural fluctuations of individual monomers. In the parametrization process, both network connectivity and strength are optimized. This elastic-network optimized CG model, which solely relies on atomistic data of small units (dimers), is able to correctly predict inter-protein conformational flexibility and properties of larger capsid fragments of 20 and more subunits. Furthermore, it is shown that this CG model reproduces experimental (Atomic Force Microscopy) indentation measurements of the entire viral capsid. Thus it is shown that one obvious goal for hierarchical modeling, namely predicting mechanical properties of larger protein complexes from models that are carefully parametrized on elastic properties of smaller units, is achievable.     

Protein Interactions and Fluctuations in a Proteomic Network using an Elastic Network Model

Abstract

A set of protein conformations are analyzed by normal mode analysis. An elastic network model is used to obtain fluctuation and cooperativity of residues with low amplitude fluctuations across different species. Slow modes that are associated with the function of proteins have common features among different protein structures. We show that the degree of flexibility of the protein is important for proteins to interact with other proteins and as the species gets more complex its proteins become more flexible. In the complex organism, higher cooperativity arises due to protein structure and connectivity.     

A Coarse Grained Model for a Lipid Membrane with Physiological Composition and Leaflet Asymmetry

The resemblance of lipid membrane models to physiological membranes determines how well molecular dynamics (MD) simulations imitate the dynamic behavior of cell membranes and membrane proteins. Physiological lipid membranes are composed of multiple types of phospholipids, and the leaflet compositions are generally asymmetric. Here we describe an approach for self-assembly of a Coarse-Grained (CG) membrane model with physiological composition and leaflet asymmetry using the MARTINI force field. An initial set-up of two boxes with different types of lipids according to the leaflet asymmetry of mammalian cell membranes stacked with 0.5 nm overlap, reliably resulted in the self-assembly of bilayer membranes with leaflet asymmetry resembling that of physiological mammalian cell membranes. Self-assembly in the presence of a fragment of the plasma membrane protein syntaxin 1A led to spontaneous specific positioning of phosphatidylionositol(4,5)bisphosphate at a positively charged stretch of syntaxin consistent with experimental data. An analogous approach choosing an initial set-up with two concentric shells filled with different lipid types results in successful assembly of a spherical vesicle with asymmetric leaflet composition. Self-assembly of the vesicle in the presence of the synaptic vesicle protein synaptobrevin 2 revealed the correct position of the synaptobrevin transmembrane domain. This is the first CG MD method to form a membrane with physiological lipid composition as well as leaflet asymmetry by self-assembly and will enable unbiased studies of the incorporation and dynamics of membrane proteins in more realistic CG membrane models.     

REACH Coarse-Grained Normal Mode Analysis of Protein Dimer Interaction Dynamics

The REACH (realistic extension algorithm via covariance Hessian) coarse-grained biomolecular simulation method is a self-consistent multiscale approach directly mapping atomistic molecular dynamics simulation results onto a residue-scale model. Here, REACH is applied to calculate the dynamics of protein-protein interactions. The intra- and intermolecular fluctuations and the intermolecular vibrational densities of states derived from atomistic molecular dynamics are well reproduced by the REACH normal modes. The phonon dispersion relations derived from the REACH lattice dynamics model of crystalline ribonuclease A are also in satisfactory agreement with the corresponding all-atom results. The REACH model demonstrates that increasing dimer interaction strength decreases the translational and rotational intermolecular vibrational amplitudes, while their vibrational frequencies are relatively unaffected. A comparative study of functionally interacting biological dimers with crystal dimers, which are formed artificially via crystallization, reveals a relation between their static structures and the interprotein dynamics: i.e., the consequence of the extensive interfaces of biological dimers is reduction of the intermonomer translational and rotational amplitudes, but not the frequencies.     

Comparative Network Analysis of Preterm vs. Full-Term Infant-Mother Interactions

Several studies have reported that interactions of mothers with preterm infants show differential characteristics compared to that of mothers with full-term infants. Interaction of preterm dyads is often reported as less harmonious. However, observations and explanations concerning the underlying mechanisms are inconsistent. In this work 30 preterm and 42 full-term mother-infant dyads were observed at one year of age. Free play interactions were videotaped and coded using a micro-analytic coding system. The video records were coded at one second resolution and studied by a novel approach using network analysis tools. The advantage of our approach is that it reveals the patterns of behavioral transitions in the interactions. We found that the most frequent behavioral transitions are the same in the two groups. However, we have identified several high and lower frequency transitions which occur significantly more often in the preterm or full-term group. Our analysis also suggests that the variability of behavioral transitions is significantly higher in the preterm group. This higher variability is mostly resulted from the diversity of transitions involving non-harmonious behaviors. We have identified a maladaptive pattern in the maternal behavior in the preterm group, involving intrusiveness and disengagement. Application of the approach reported in this paper to longitudinal data could elucidate whether these maladaptive maternal behavioral changes place the infant at risk for later emotional, cognitive and behavioral disturbance.     

Phylogenetic Gaussian Process Model for the Inference of Functionally Important Regions in Protein Tertiary Structures

A critical question in biology is the identification of functionally important amino acid sites in proteins. Because functionally important sites are under stronger purifying selection, site-specific substitution rates tend to be lower than usual at these sites. A large number of phylogenetic models have been developed to estimate site-specific substitution rates in proteins and the extraordinarily low substitution rates have been used as evidence of function. Most of the existing tools, e.g. Rate4Site, assume that site-specific substitution rates are independent across sites. However, site-specific substitution rates may be strongly correlated in the protein tertiary structure, since functionally important sites tend to be clustered together to form functional patches. We have developed a new model, GP4Rate, which incorporates the Gaussian process model with the standard phylogenetic model to identify slowly evolved regions in protein tertiary structures. GP4Rate uses the Gaussian process to define a nonparametric prior distribution of site-specific substitution rates, which naturally captures the spatial correlation of substitution rates. Simulations suggest that GP4Rate can potentially estimate site-specific substitution rates with a much higher accuracy than Rate4Site and tends to report slowly evolved regions rather than individual sites. In addition, GP4Rate can estimate the strength of the spatial correlation of substitution rates from the data. By applying GP4Rate to a set of mammalian B7-1 genes, we found a highly conserved region which coincides with experimental evidence. GP4Rate may be a useful tool for the in silico prediction of functionally important regions in the proteins with known structures.     

Normal Mode Analysis Theoretical and Applications to Biological and Chemical Systems.

The realization that many of the important biochemical phenomenaoccur in the micro- to millisecond timescale has resulted inthe need for computational methods that would extend beyondthe nanosecond regime that is currently accessible with explicit-solventmolecular dynamics simulations. The normal mode analysis method,initially developed in solid-phase physics, met that need byenabling the exploration of large scale collective motions thatoccur in systems ranging from small proteins and nucleic acidsto the bacterial ribosome. This method requires the derivationof eigenvectors and eigenvalues through the diagonalizationof the Hessian matrix, which is composed of mass-weighed secondderivatives of the potential energy function. The     

Fabrication of Stable Packed Capillary Reversed-Phase Columns for Protein Structural Analysis

Capillary column (320-m ID) liquid chromatography is an essential tool for the separation and concentration of low-picomole amounts of proteins and peptides for mass-spectrometric based structural analysis. We describe a detailed procedure for the fabrication of stable and efficient 50- to 180-m ID polyimide fused-silica columns. Columns were packed by conventional slurry packing with reversed-phase silica-based supports followed by column bed consolidation with acetonitrile and sonication. PVDF membrane or internal fused-silica particles were employed for column end-frit construction. The ability of these columns to withstand high back pressures (300–400 bar) enabled their use for rapid chromatography (>3400 cm/hr; i.e., 40 l/min for 200-m ID columns) and the loading of large sample volumes (up to 500 l). The accurate low flow rates (0.4–4.0 l/min) and precise gradient formation necessary to operate these columns were achieved by a simple modification of conventional HPLC systems [Moritz et al. (1992), J. Chromatogr. 599, 119–130]. Column performance was evaluated for ability to resolve low-fmol amounts of all components of a mixture of PTH-amino acids and to separate peptides for on-line LC/MS analysis of peptide mixtures derived from in situ digestion of 2-DE resolved protein spots.     

生态环境系统结构模型解析及其应用

一、系统结构模型解析及其作用在生态环境研究中,经常遇到复杂的生态环境系统。为了把握这样的复杂系统的总体结构,我们一般用图这个数学工具为其建立模型。把系统所含的因素当作结点,因素两两之间的关系连成线,即可得到我们研究对象的系     

Network Based Prediction Model for Genomics Data Analysis

Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. We employ the gradient descent boosting procedure to build an additive tree model and propose a new algorithm to utilize the network structure in fitting small tree weak learners. We illustrate by simulation studies and a real data example that, by making use of the network information, NetBoosting outperforms a few existing methods in terms of accuracy of prediction and variable selection.     

The Use of Electron Tomography for Structural Analysis of Disordered Protein Arrays

A method based on Fourier transforms is described for obtaining a 3-D reconstruction from a paracrystalline object with static disorder. The method is derived from the standard methods used in 3-D reconstruction of 2-D crystals except that all of the Fourier coefficients are used and not just the sampled data from the periodic lattice. Thus, not only is the spatially ordered part of the structure visualized in 3-D, but also the spatially disordered part. Application of the method to 3-D reconstructions of insect flight muscle is described as well as prospects for extension of the method to radiation-sensitive specimens.     

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662–C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662–C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662–C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.     

Validation of Network Communicability Metrics for the Analysis of Brain Structural Networks

Computational network analysis provides new methods to analyze the brain''s structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.     

Model of costs of care for dementia: community-dwelling vs. institutionalization

This article describes a model of costs for Alzheimer's Disease. The cost of institutionalization and the cost of living at home for patients with Alzheimer's Disease are calculated and compared. The average yearly cost of living at home is 10,810 Dutch guilders for male patients and 12,771 Dutch guilders for female patients (prices of 1996). Institutionalization is considerably more expensive: the average yearly cost associated with admission to a residential home is 48,180 Dutch guilders and 98,915 versus 102,930 Dutch guilders for costs associated with admission to a specialised respectively nonspecialized nursing home. Differences between men and women could not be taken into account in these last three estimations. The cost of living at home increases when the cost of informal care is included. However, when the cost were valued with a tariff of 29.50 guilders per hour, institutionalisation remains more expensive than living at home. This is also the case for severely demented patients, as the costs of institutions are also related to the level of care.     

Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease

Alzheimer''s disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients'' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.     

A Real-Time SUMO-Binding Assay for the Analysis of the SUMO-SIM Protein Interaction Network

SUMO-interacting motifs (SIMs) play a central role in the fate of SUMO-modified proteins. Here we report a real-time SUMO-binding assay. It can be applied to the identification of SIMs and to screening for the identification of novel SUMO-binding proteins. Using this assay, we investigated the SIMs in SETDB1 and MCAF1 to gain insight into the assembly of SETDB1-MCAF1-mediated gene silencing.     

Activated Conformations of the ras-gene-Encoded p21 Protein. 1. An Energy-Refined Structure for the Normal p21 Protein Complexed with GDP

Abstract

A complete three-dimensional structure for the ras-gene-encoded p21 protein with Gly 12 and Gin 61, bound to GDP, has been constructed in four stages using the available α-carbon coordinates as deposited in the Brookhaven National Laboratories Protein Data Bank. No all-atom structure has been made available despite the fact that the first crystallographic structure for the p21 protein was reported almost four years ago. In the p21 protein, if amino acid substitutions are made at any one of a number of different positions in the amino acid sequence, the protein becomes permanently activated and causes malignant transformation of normal cells or, in some cell lines, differentiation and maturation. For example, all amino acids except Gly and Pro at position 12 result in an oncogenic protein; all amino acids except Gin, Glu and Pro at position 61 likewise cause malignant transformation of cells. We have constructed our all-atom structure of the non-oncogenic protein from the x-ray structure in order to determine how oncogenic amino acid substitutions affect the three-dimensional structure of this protein. In Stage 1 we generated a poly-alanine backbone (except at Gly and Pro residues) through the α-carbon structure, requiring the individual Ala, Pro or Gly residues to conform to standard amino acid geometry and to form trans-planar peptide bonds. Since no a-carbon coordinates for residues 60–65 have been determined these residues were modeled by generating them in the extended conformation and then subjecting them to molecular dynamics using the computer application DISCOVER and energy minimization using DISCOVER and the ECEPP (Empirical Conformational Energies for Peptides Program). In Stage 2, the positions of residues that are homologous to corresponding residues of bacterial elongation factor Tu (EF-Tu) to which p21 bears an overall 40% sequence homology, were determined from their corresponding positions in a high-resolution structure of EF-Tu. Non-homologous loops were taken from the structure generated in Stage 1 and were placed between the appropriate homologous segments so as to connect them. In Stage 3, all bad contacts that occurred in this resulting structure were removed, and the coordinates of the α-carbon atoms were forced to superimpose as closely as possible on the corresponding atoms of the reference (x-ray) structure. Then the side chain positions of residues of the nonhomologous loop regions were modeled using a combination of molecular dynamics and energy minimization using DISCOVER and ECEPP respectively. All of the residues of the structure were then allowed to move under restrained energy minimization where the restraints were gradually removed. In Stage 4, the nucleotide GOP was added to the model and further energy minimization was carried out. The energy of the protein-GOP complex was minimized by allowing the atoms of GOP to move with the protein held fixed and then by allowing both the nucleotide and the residues of the protein to move together. The reconstructed model agrees with the published features of the p21 protein-GOP complex including the hydrogen bonding scheme, the distribution of backbone dihedral angles, the residues contacting the nucleotide, and the orientation of loops with respect to one another in the protein. The structure also agrees with one that was predicted previously (Chen, J.M. et al., J. Biomol. Struct. Dynamics 6, 850–875 (1989)). In our molecular dynamics-energy minimization procedures, we also have been able to place all residues except Ala 66, which occurs in a poorly-defined region crystallographically, in local single residue minima, including residues reported to be in high energy regions in the x-ray structure. The constructed model can explain observed physical phenomena such as autophosphorylation by GTP on Thr 59 in proteins containing Thr in place of Ala 59.     

Structural Analysis of Mitochondrial Mutations Reveals a Role for Bigenomic Protein Interactions in Human Disease

Mitochondria are the energy producing organelles of the cell, and mutations within their genome can cause numerous and often severe human diseases. At the heart of every mitochondrion is a set of five large multi-protein machines collectively known as the mitochondrial respiratory chain (MRC). This cellular machinery is central to several processes important for maintaining homeostasis within cells, including the production of ATP. The MRC is unique due to the bigenomic origin of its interacting proteins, which are encoded in the nucleus and mitochondria. It is this, in combination with the sheer number of protein-protein interactions that occur both within and between the MRC complexes, which makes the prediction of function and pathological outcome from primary sequence mutation data extremely challenging. Here we demonstrate how 3D structural analysis can be employed to predict the functional importance of mutations in mtDNA protein-coding genes. We mined the MITOMAP database and, utilizing the latest structural data, classified mutation sites based on their location within the MRC complexes III and IV. Using this approach, four structural classes of mutation were identified, including one underexplored class that interferes with nuclear-mitochondrial protein interactions. We demonstrate that this class currently eludes existing predictive approaches that do not take into account the quaternary structural organization inherent within and between the MRC complexes. The systematic and detailed structural analysis of disease-associated mutations in the mitochondrial Complex III and IV genes significantly enhances the predictive power of existing approaches and our understanding of how such mutations contribute to various pathologies. Given the general lack of any successful therapeutic approaches for disorders of the MRC, these findings may inform the development of new diagnostic and prognostic biomarkers, as well as new drugs and targets for gene therapy.