Automatic domain decomposition of proteins by a Gaussian Network Model

Proteins are often comprised of domains of apparently independent folding units. These domains can be defined in various ways, but one useful definition divides the protein into substructures that seem to move more or less independently. The same methods that allow fairly accurate calculation of motion can be used to help classify these substructures. We show how the Gaussian Network Model (GNM), commonly used for determining motion, can also be adapted to automatically classify domains in proteins. Parallels between this physical network model and graph theory implementation are apparent. The method is applied to a nonredundant set of 55 proteins, and the results are compared to the visual assignments by crystallographers. Apart from decomposing proteins into structural domains, the algorithm can generally be applied to any large macromolecular system to decompose it into motionally decoupled sub-systems.     

Topological thermal instability and length of proteins

We present an analysis of the effects of global topology on the structural stability of folded proteins in thermal equilibrium with a heat bath. For a large class of single domain proteins, we computed the harmonic spectrum within the Gaussian Network Model (GNM) and determined their spectral dimension, a parameter describing the low frequency behavior of the density of modes. We found a surprisingly strong correlation between the spectral dimension and the number of amino acids in the protein. Considering that larger spectral dimension values relate to more topologically compact folded states, our results indicate that, for a given temperature and length of protein, the folded structure corresponds to a less compact folding, one compatible with thermodynamic stability.     

Modelling the effect of environmental factors on the “trade-off” between growth and defensive compounds in young apple trees

The allocation of plant internal resources to growth processes (primary metabolism) and to defensive compounds (secondary metabolism) is determined by plant internal competition for common substrates and energy. In order to contribute to the discussion about environmental impacts on this trade-off between demands for growth and defence, we extended a complex plant growth model to simulate the formation of defensive compounds on the whole plant level, depending on the dynamics of the environmental conditions light, nutrients and water. In this paper, we present and apply the model to simulate the effects of different N fertilizer applications on growth and resistance of young apple trees (cv Golden Delicious). The results show that model predictions are able to describe the observed relation between growth rate and phenylpropanoid concentrations in young leaves of apple trees, and can assist in the interpretation of experimental findings. Finally, we estimate costs and benefits of investment into defence in a scenario, in which an attack by the leaf pathogen Venturia inaequalis is simulated.     

An in silico investigation into the causes of telomere length heterogeneity and its implications for the Hayflick limit

In telomerase-negative cell populations the mean telomere length (TL) decreases with increasing population doubling number (PD). A critically small TL is believed to stop cell proliferation at a cell-, age- and species-specific PD thus defining the Hayflick limit. However, positively skewed TL distributions are broad compared to differences between initial and final mean TL and strongly overlap at middle and late PD, which is inconsistent with a limiting role of TL. We used computer-assisted modelling to define what set of premises may account for the above. Our model incorporates the following concepts. DNA end replication problem: telomeres loose 1 shortening unit (SU) upon each cell division. Free radical-caused TL decrease: telomeres experience random events resulting in the loss of a random SU number within a remaining TL. Stochasticity of gene expression and cell differentiation: cells experience random events inducing mitoses or committing cells to proliferation arrest, the latter option requiring a specified number of mitoses to be passed. Cells whose TL reaches 1SU cannot divide. The proliferation kinetics of such virtual cells conforms to the transition probability model of cell cycle. When no committing events occur and at realistic SU estimates of the initial TL, maximal PD values far exceed the Hayflick limit observed in normal cells and are consistent with the crisis stage entered by transformed cells that have surpassed the Hayflick limit. At intermediate PD, symmetrical TL distributions are yielded. Upon introduction of committing events making the ratio of the rates of proliferating and committing events (P/C) range from 1.10 to 1.25, TL distributions at intermediate PD become positively skewed, and virtual cell clones show bimodal size distributions. At P/C as high as 1.25 the majority of virtual cells at maximal PD contain telomeres with TL>1SU. A 10% increase in P/C within the 1.10-1.25 range produces a two-fold increase in the maximal PD, which can reach values of up to 25 observed in rodent and some human cells. Increasing the number of committed mitoses from 0 to 10 can increases PD to about 50 observed in human fibroblasts. Introduction of the random TL breakage makes the shapes of TL distributions quite dissimilar from those observed in real cells. CONCLUSIONS: Telomere length decrease is a correlate of cell proliferation that cannot alone account for the Hayflick limit, which primarily depends on parameters of cell population kinetics. Free radical damage influences the Hayflick limit not through TL but rather by affecting the ratio of the rates of events that commit cells to mitoses or to proliferation arrest.     

Latent class model diagnosis from a frequentist point of view

This is in response to Garrett and Zeger (2000, Biometrics 56, 1055-1067) who, within the Bayesian framework, developed mainly graphical methods for latent class model diagnosis. Possible problems with this approach, and with its application to both generated and empirical data, are pointed out. The impact of the proposed tools cannot be understood by their reader, as no comparisons are made to results obtainable using established methods for latent class model diagnosis; this applies especially to overall goodness-of-fit tests, for which alternatives (bootstrap, Rudas-Clogg-Lindsay index of fit) are mentioned. Further, in one case of generated data, the methods proposed by Garrett and Zeger seem to give problematic results as to identifiability; in the case of the empirical data on major depression, they lead to accepting a suboptimal three-class model. In the latter case, one can be rather sure that an identifiable, well-fitting latent class model could have been identified--if Garrett and Zeger had also considered restricted latent class models.     

Fold recognition with minimal gaps

Here we present a simplified form of threading that uses only a 20 x 20 two-body residue-based potential and restricted number of gaps. Despite its simplicity and transparency the Monte Carlo-based threading algorithm performs very well in a rigorous test of fold recognition. The results suggest that by simplifying and constraining the decoy space, one can achieve better fold recognition. Fold recognition results are compared with and supplemented by a PSI-BLAST search. The statistical significance of threading results is rigorously evaluated from statistics of extremes by comparison with optimal alignments of a large set of randomly shuffled sequences. The statistical theory, based on the Random Energy Model, yields a cumulative statistical parameter, epsilon, that attests to the likelihood of correct fold recognition. A large epsilon indicates a significant energy gap between the optimal alignment and decoy alignments and, consequently, a high probability that the fold is correctly recognized. For a particular number of gaps, the epsilon parameter reaches its maximal value, and the fold is recognized. As the number of gaps further increases, the likelihood of correct fold recognition drops off. This is because the decoy space is small when gaps are restricted to a small number, but the native alignment is still well approximated, whereas unrestricted increase of the number of gaps leads to rapid growth of the number of decoys and their statistical dominance over the correct alignment. It is shown that best results are obtained when a combination of one-, two-, and three-gap threading is used. To this end, use of the epsilon parameter is crucial for rigorous comparison of results across the different decoy spaces belonging to a different number of gaps.     

Trimerization of cell adhesion molecule L1 mimics clustered L1 expression on the cell surface: influence on L1-ligand interactions and on promotion of neurite outgrowth

Several studies indicate that cell adhesion molecules have to be clustered on the cell surface to engage in adhesive functions. We investigated adhesive functions of clustered versus monomeric L1 extracellular parts in vitro to distinguish how clustering affects ligand binding and promotion of neurite outgrowth. Trimeric L1 was recombinantly expressed and covalently assembled by the cartilage matrix protein's coiled-coil domain. Trimeric L1 has an apparent molecular mass of approximately 380 kDa in the nonreduced form and approximately 130 kDa in the reduced form. Rotary shadowing electron micrographs of trimeric L1 revealed a rod-like shape terminating in three globular domains. Monomeric L1 assumes a horseshoe shape of domains Ig I-IV followed by a rod-like structure consisting of Ig V and VI and fibronectin type III 1-5. Circular dichroism measurements showed that the secondary structure consists of beta-sheets. Trimeric L1 binds to itself, to monomeric L1, to laminin-1, and to alpha5beta1 integrin in a concentration-dependent manner. In contrast, binding of monomeric L1 could only be saturated with itself but not with laminin-1 and with alpha5beta1 integrin. Promotion of neurite outgrowth from PC12 cells cultured on adsorbed trimeric L1 was increased by 100%, whereas on monomeric L1 the increase was only 50% over the control value. Promotion of neurite outgrowth from PC12 cells was specifically inhibited in a concentration-dependent manner by a polyclonal antibody against L1. These findings show that clustering of only three extracellular domains increases considerably L1's binding affinity to different ligands and enhances neurite outgrowth, suggesting that adhesive functions of L1 on the cell surface depend on cluster formation.     

Spatial modelling of multinomial data with latent structure: an application to geographical mapping of human gene and haplotype frequencies

We develop hierarchical models for spatial multinomial data with missing categories, to analyse a database of HLA-A and -B gene and haplotype frequencies from Papua New Guinea, with a highly variable number of samples per spatial unit. The spatial structure of the multinomial data is incorporated by adopting conditional autoregressive (CAR) priors for the random effects, reflecting extra-multinomial variation. Different spatial structures are investigated, and covariate effects are evaluated using a novel model selection criterion. Tables and maps reveal strong spatial association and the importance of altitude, a covariate anticipated to be significant in explaining genetic variation. Our approach can be used in identifying associations with environmental factors, linguistic or epidemiological patterns and hence potential causes of genetic diversity (population movements, natural selection, stochastic effects).     

AAV载体介导的蓬佩病模型小鼠体内基因治疗研究*

Objective: Pompe disease is a lysosomal glycogen storage disease caused by acid α-glucosidase (GAA) deficiency, which is characterized by glycogen accumulation in the heart, skeletal muscle, and central nervous system (CNS). AAV vector-mediated gene therapy is expected to be a breakthrough in the treatment of Pompe disease. In this study, AAV9 vector was used to mediate GAA gene transfer in Pompe disease model mice, and the changes of GAA protease activity, glycogen accumulation in tissues and pathological changes in mice after transgenic intervention were evaluated. Methods: Codon optimized GAA gene (coGAA) was carried by AAV9 vector, and the AAV vector was packaged by baculovirus production process. Adult Pompe model mice were given a single intravenous injection at the dose of 1.1×10¹³, 3.0×10¹³, 1.2×10¹⁴ vg/kg, and aged Pompe model mice were given a single intravenous injection at the dose of 3.0×10¹³ vg/kg. After reaching the end point of the experiment, the mice were euthanized, GAA protease activity was determined by fluorescence spectrophotometry, glycogen accumulation was observed by PAS staining, and pathological changes were detected by HE staining. Results: Five weeks after administration, GAA protein was widely expressed in all tissues of adult model mice, with higher expression levels in heart and liver, and lower expression levels in brain and spinal cord. After rAAV9-coGAA treatment, glycogen content in myocardium, skeletal muscle and brain decreased, and vacuolar degeneration in myocardium and skeletal muscle decreased significantly. After treatment, the tissue enzyme activity of the aged animals was significantly increased compared with that of the model mice. The vacuolar degeneration and inflammatory cell infiltration of the myocardium were decreased, but the pathological improvement of skeletal muscle was limited. Conclusion: A single intravenous injection of rAAV9-coGAA can enhance GAA enzyme activity, reduce glycogen accumulation and improve pathology in Pompe model mice. The therapeutic effect was dose-dependent, and the injection also had certain therapeutic effect on aged animals. This study laid a theoretical foundation for the clinical application of AAV9 mediated gene therapy via intravenous route in Pompe disease.