The Energy Landscapes of Repeat-Containing Proteins: Topology, Cooperativity, and the Folding Funnels of One-Dimensional Architectures

Repeat-proteins are made up of near repetitions of 20– to 40–amino acid stretches. These polypeptides usually fold up into non-globular, elongated architectures that are stabilized by the interactions within each repeat and those between adjacent repeats, but that lack contacts between residues distant in sequence. The inherent symmetries both in primary sequence and three-dimensional structure are reflected in a folding landscape that may be analyzed as a quasi–one-dimensional problem. We present a general description of repeat-protein energy landscapes based on a formal Ising-like treatment of the elementary interaction energetics in and between foldons, whose collective ensemble are treated as spin variables. The overall folding properties of a complete “domain” (the stability and cooperativity of the repeating array) can be derived from this microscopic description. The one-dimensional nature of the model implies there are simple relations for the experimental observables: folding free-energy (ΔGwater) and the cooperativity of denaturation (m-value), which do not ordinarily apply for globular proteins. We show how the parameters for the “coarse-grained” description in terms of foldon spin variables can be extracted from more detailed folding simulations on perfectly funneled landscapes. To illustrate the ideas, we present a case-study of a family of tetratricopeptide (TPR) repeat proteins and quantitatively relate the results to the experimentally observed folding transitions. Based on the dramatic effect that single point mutations exert on the experimentally observed folding behavior, we speculate that natural repeat proteins are “poised” at particular ratios of inter- and intra-element interaction energetics that allow them to readily undergo structural transitions in physiologically relevant conditions, which may be intrinsically related to their biological functions

Next-generation whole genome sequencing of dengue virus.

RNA viruses are notorious for their ability to quickly adapt to selective pressure from the host immune system and/or antivirals. This adaptability is likely due to the error-prone characteristics of their RNA-dependent, RNA polymerase [1, 2]. Dengue virus, a member of the Flaviviridae family of positive-strand RNA viruses, is also known to share these error-prone characteristics [3]. Utilizing high-throughput, massively parallel sequencing methodologies, or next-generation sequencing (NGS), we can now accurately quantify these populations of viruses and track the changes to these populations over the course of a single infection. The aim of this chapter is twofold: to describe the methodologies required for sample preparation prior to sequencing and to describe the bioinformatics analyses required for the resulting data

The dawn of evolutionary genome engineering

Genome engineering strategies-such as genome editing, reduction and shuffling, and de novo genome synthesis-enable the modification of specific genomic locations in a directed and combinatorial manner. These approaches offer an unprecedented opportunity to study central evolutionary issues in which natural genetic variation is Limited or biased, which sheds light on the evolutionary forces driving complex and extremely slowly evolving traits; the selective constraints on genome architecture; and the reconstruction of ancestral states of cellular structures and networks

Cognitive Processing Speed in Older Adults: Relationship with White Matter Integrity

Cognitive processing slows with age. We sought to determine the importance of white matter integrity, assessed by diffusion tensor imaging (DTI), at influencing cognitive processing speed among normal older adults, assessed using a novel battery of computerized, non-verbal, choice reaction time tasks. We studied 131 cognitively normal adults aged 55–87 using a cross-sectional design. Each participant underwent our test battery, as well as MRI with DTI. We carried out cross-subject comparisons using tract-based spatial statistics. As expected, reaction time slowed significantly with age. In diffuse areas of frontal and parietal white matter, especially the anterior corpus callosum, fractional anisotropy values correlated negatively with reaction time. The genu and body of the corpus callosum, superior longitudinal fasciculus, and inferior fronto-occipital fasciculus were among the areas most involved. This relationship was not explained by gray or white matter atrophy or by white matter lesion volume. In a statistical mediation analysis, loss of white matter integrity mediated the relationship between age and cognitive processing speed

The Effects of Conflict Types, Dimensions, and Emergent States on Group Outcomes

In this study, we examine three types of conflict (task, relationship, and process) and four dimensions of conflict (emotions, norms, resolution efficacy, and importance) in decision making groups. We also investigate emergent states (e.g., trust, respect, cohesiveness; Marks et al. 2001; Acad Manag Rev 26: 530-547) as mediating the effects of the conflict types and dimensions on group outcomes (productivity and viability). All three types of conflict decreased positive emergent states in groups and this led to a decrease in group viability (the ability of a team to retain its members through their satisfaction and willingness to continue working together; Balkundi and Harrison 2006; Acad Manag J 49: 49-68). This effect was alleviated by resolution efficacy (the belief that the conflict can be easily resolved) regarding process conflict, but could be exacerbated by any negative emotion associated with relationship conflict. Norms that encouraged task conflict also increased positive emergent states within groups, which marginally and positively influenced group performance

Systems-biology approaches for predicting genomic evolution.

Item does not contain fulltextIs evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.1 september 201