Folding protein models with a simple hydrophobic energy function: The fundamental importance of monomer inside/outside segregation

The present study explores a “hydrophobic” energy function for folding simulations of the protein lattice model. The contribution of each monomer to conformational energy is the product of its “hydrophobicity” and the number of contacts it makes, i.e., E(h⃗, c⃗) = −Σi=1N cihi = −(h⃗.c⃗) is the negative scalar product between two vectors in N-dimensional cartesian space: h⃗ = (h1, … , hN), which represents monomer hydrophobicities and is sequence-dependent; and c⃗ = (c1, … , cN), which represents the number of contacts made by each monomer and is conformation-dependent. A simple theoretical analysis shows that restrictions are imposed concomitantly on both sequences and native structures if the stability criterion for protein-like behavior is to be satisfied. Given a conformation with vector c⃗, the best sequence is a vector h⃗ on the direction upon which the projection of c⃗ − c̄⃗ is maximal, where c̄⃗ is the diagonal vector with components equal to c̄, the average number of contacts per monomer in the unfolded state. Best native conformations are suggested to be not maximally compact, as assumed in many studies, but the ones with largest variance of contacts among its monomers, i.e., with monomers tending to occupy completely buried or completely exposed positions. This inside/outside segregation is reflected on an apolar/polar distribution on the corresponding sequence. Monte Carlo simulations in two dimensions corroborate this general scheme. Sequences targeted to conformations with large contact variances folded cooperatively with thermodynamics of a two-state transition. Sequences targeted to maximally compact conformations, which have lower contact variance, were either found to have degenerate ground state or to fold with much lower cooperativity.

Two amyloid precursor protein transgenic mouse models with Alzheimer disease-like pathology

Mutations in the amyloid precursor protein (APP) gene cause early-onset familial Alzheimer disease (AD) by affecting the formation of the amyloid β (Aβ) peptide, the major constituent of AD plaques. We expressed human APP751 containing these mutations in the brains of transgenic mice. Two transgenic mouse lines develop pathological features reminiscent of AD. The degree of pathology depends on expression levels and specific mutations. A 2-fold overexpression of human APP with the Swedish double mutation at positions 670/671 combined with the V717I mutation causes Aβ deposition in neocortex and hippocampus of 18-month-old transgenic mice. The deposits are mostly of the diffuse type; however, some congophilic plaques can be detected. In mice with 7-fold overexpression of human APP harboring the Swedish mutation alone, typical plaques appear at 6 months, which increase with age and are Congo Red-positive at first detection. These congophilic plaques are accompanied by neuritic changes and dystrophic cholinergic fibers. Furthermore, inflammatory processes indicated by a massive glial reaction are apparent. Most notably, plaques are immunoreactive for hyperphosphorylated tau, reminiscent of early tau pathology. The immunoreactivity is exclusively found in congophilic senile plaques of both lines. In the higher expressing line, elevated tau phosphorylation can be demonstrated biochemically in 6-month-old animals and increases with age. These mice resemble major features of AD pathology and suggest a central role of Aβ in the pathogenesis of the disease.

Dynamic regulation of the tryptophan operon: A modeling study and comparison with experimental data

A mathematical model for regulation of the tryptophan operon is presented. This model takes into account repression, feedback enzyme inhibition, and transcriptional attenuation. Special attention is given to model parameter estimation based on experimental data. The model's system of delay differential equations is numerically solved, and the results are compared with experimental data on the temporal evolution of enzyme activity in cultures of Escherichia coli after a nutritional shift (minimal + tryptophan medium to minimal medium). Good agreement is obtained between the numeric simulations and the experimental results for wild-type E. coli, as well as for two different mutant strains.

Estimating risks in declining populations with poor data

Census data on endangered species are often sparse, error-ridden, and confined to only a segment of the population. Estimating trends and extinction risks using this type of data presents numerous difficulties. In particular, the estimate of the variation in year-to-year transitions in population size (the “process error” caused by stochasticity in survivorship and fecundities) is confounded by the addition of high sampling error variation. In addition, the year-to-year variability in the segment of the population that is sampled may be quite different from the population variability that one is trying to estimate. The combined effect of severe sampling error and age- or stage-specific counts leads to severe biases in estimates of population-level parameters. I present an estimation method that circumvents the problem of age- or stage-specific counts and is markedly robust to severe sampling error. This method allows the estimation of environmental variation and population trends for extinction-risk analyses using corrupted census counts—a common type of data for endangered species that has hitherto been relatively unusable for these analyses.

Kinetic proofreading models for cell signaling predict ways to escape kinetic proofreading

In the context of cell signaling, kinetic proofreading was introduced to explain how cells can discriminate among ligands based on a kinetic parameter, the ligand-receptor dissociation rate constant. In the kinetic proofreading model of cell signaling, responses occur only when a bound receptor undergoes a complete series of modifications. If the ligand dissociates prematurely, the receptor returns to its basal state and signaling is frustrated. We extend the model to deal with systems where aggregation of receptors is essential to signal transduction, and present a version of the model for systems where signaling depends on an extrinsic kinase. We also investigate the kinetics of signaling molecules, “messengers,” that are generated by aggregated receptors but do not remain associated with the receptor complex. We show that the extended model predicts modes of signaling that exhibit kinetic discrimination for some range of parameters but for other parameter values show little or no discrimination and thus escape kinetic proofreading. We compare model predictions with experimental data.

Tuning DNA “strings”: Modulating the rate of DNA replication with mechanical tension

Recent experiments have measured the rate of replication of DNA catalyzed by a single enzyme moving along a stretched template strand. The dependence on tension was interpreted as evidence that T7 and related DNA polymerases convert two (n = 2) or more single-stranded template bases to double helix geometry in the polymerization site during each catalytic cycle. However, we find structural data on the T7 enzyme–template complex indicate n = 1. We also present a model for the “tuning” of replication rate by mechanical tension. This model considers only local interactions in the neighborhood of the enzyme, unlike previous models that use stretching curves for the entire polymer chain. Our results, with n = 1, reconcile force-dependent replication rate studies with structural data on DNA polymerase complexes.