Recovery of Protein Structure from Contact Maps

We present an efficient algorithm to recover the three dimensional structure of a protein from its contact map representation. First we show that when a physically realizable map is used as target, our method generates a structure whose contact map is essentially similar to the target. Furthermore, the reconstructed and original structures are similar up to the resolution of the contact map representation. Next we use non-physical target maps, obtained by corrupting a physical one; in this case our method essentially recovers the underlying physical map and structure. Hence our algorithm will help to fold proteins, using dynamics in the space of contact maps. Finally we investigate the manner in which the quality of the recovered structure degrades when the number of contacts is reduced.Comment: 27 pages, RevTex, 12 figures include

Evolutionary advantage of cell size control

We analyze the advantage of cell size control strategies in growing populations under mortality constraints. We demonstrate a general advantage of the adder control strategy in the presence of growth-dependent mortality, and for different size-dependent mortality landscapes. Its advantage stems from epigenetic heritability of cell size, which enables selection to act on the distribution of cell sizes in a population to avoid mortality thresholds and adapt to a mortality landscape

An Analytical Approach to the Protein Designability Problem

We present an analytical method for determining the designability of protein structures. We apply our method to the case of two-dimensional lattice structures, and give a systematic solution for the spectrum of any structure. Using this spectrum, the designability of a structure can be estimated. We outline a heirarchy of structures, from most to least designable, and show that this heirarchy depends on the potential that is used.Comment: 16 pages 4 figure

Evolutionary pressures on simple sequence repeats in prokaryotic coding regions

Simple sequence repeats (SSRs) are indel mutational hotspots in genomes. In prokaryotes, SSR loci can cause phase variation, a microbial survival strategy that relies on stochastic, reversible on–off switching of gene activity. By analyzing multiple strains of 42 fully sequenced prokaryotic species, we measure the relative variability and density distribution of SSRs in coding regions. We demonstrate that repeat type strongly influences indel mutation rates, and that the most mutable types are most strongly avoided across genomes. We thoroughly characterize SSR density and variability as a function of N→C position along protein sequences. Using codon-shuffling algorithms that preserve amino acid sequence, we assess evolutionary pressures on SSRs. We find that coding sequences suppress repeats in the middle of proteins, and enrich repeats near termini, yielding U-shaped SSR density curves. We show that for many species this characteristic shape can be attributed to purely biophysical constraints of protein structure. In multiple cases, however, particularly in certain pathogenic bacteria, we observe over enrichment of SSRs near protein N-termini significantly beyond expectation based on structural constraints. This increases the probability that frameshifts result in non-functional proteins, revealing that these species may evolutionarily tune SSR positions in coding regions to facilitate phase variation

Stochastic De-repression of Rhodopsins in Single Photoreceptors of the Fly Retina

The photoreceptors of the Drosophila compound eye are a classical model for studying cell fate specification. Photoreceptors (PRs) are organized in bundles of eight cells with two major types – inner PRs involved in color vision and outer PRs involved in motion detection. In wild type flies, most PRs express a single type of Rhodopsin (Rh): inner PRs express either Rh3, Rh4, Rh5 or Rh6 and outer PRs express Rh1. In outer PRs, the K50 homeodomain protein Dve is a key repressor that acts to ensure exclusive Rh expression. Loss of Dve results in de-repression of Rhodopsins in outer PRs, and leads to a wide distribution of expression levels. To quantify these effects, we introduce an automated image analysis method to measure Rhodopsin levels at the single cell level in 3D confocal stacks. Our sensitive methodology reveals cell-specific differences in Rhodopsin distributions among the outer PRs, observed over a developmental time course. We show that Rhodopsin distributions are consistent with a two-state model of gene expression, in which cells can be in either high or basal states of Rhodopsin production. Our model identifies a significant role of post-transcriptional regulation in establishing the two distinct states. The timescale for interconversion between basal and high states is shown to be on the order of days. Our results indicate that even in the absence of Dve, the Rhodopsin regulatory network can maintain highly stable states. We propose that the role of Dve in outer PRs is to buffer against rare fluctuations in this network

Quantifying Selective Pressures Driving Bacterial Evolution Using Lineage Analysis

Organisms use a variety of strategies to adapt to their environments and maximize long-term growth potential, but quantitative characterization of the benefits conferred by the use of such strategies, as well as their impact on the whole population’s rate of growth, remains challenging. Here, we use a path-integral framework that describes how selection acts on lineages—i.e., the life histories of individuals and their ancestors—to demonstrate that lineage-based measurements can be used to quantify the selective pressures acting on a population. We apply this analysis to Escherichia coli bacteria exposed to cyclical treatments of carbenicillin, an antibiotic that interferes with cell-wall synthesis and affects cells in an age-dependent manner. While the extensive characterization of the life history of thousands of cells is necessary to accurately extract the age-dependent selective pressures caused by carbenicillin, the same measurement can be recapitulated using lineage-based statistics of a single surviving cell. Population-wide evolutionary pressures can be extracted from the properties of the surviving lineages within a population, providing an alternative and efficient procedure to quantify the evolutionary forces acting on a population. Importantly, this approach is not limited to age-dependent selection, and the framework can be generalized to detect signatures of other trait-specific selection using lineage-based measurements. Our results establish a powerful way to study the evolutionary dynamics of life under selection and may be broadly useful in elucidating selective pressures driving the emergence of antibiotic resistance and the evolution of survival strategies in biological systems

Memory and Fitness Optimization of Bacteria under Fluctuating Environments

Bacteria prudently regulate their metabolic phenotypes by sensing the availability of specific nutrients, expressing the required genes for their metabolism, and repressing them after specific metabolites are depleted. It is unclear, however, how genetic networks maintain and transmit phenotypic states between generations under rapidly fluctuating environments. By subjecting bacteria to fluctuating carbon sources (glucose and lactose) using microfluidics, we discover two types of non-genetic memory in Escherichia coli and analyze their benefits. First, phenotypic memory conferred by transmission of stable intracellular lac proteins dramatically reduces lag phases under cyclical fluctuations with intermediate timescales (1–10 generations). Second, response memory, a hysteretic behavior in which gene expression persists after removal of its external inducer, enhances adaptation when environments fluctuate over short timescales (<1 generation). Using a mathematical model we analyze the benefits of memory across environmental fluctuation timescales. We show that memory mechanisms provide an important class of survival strategies in biology that improve long-term fitness under fluctuating environments. These results can be used to understand how organisms adapt to fluctuating levels of nutrients, antibiotics, and other environmental stresses