Hierarchical coexistence of universality and diversity controls robustness and multi-functionality in intermediate filament protein networks

Proteins constitute the elementary building blocks of a vast variety of biological materials such as cellular protein networks, spider silk or bone, where they create extremely robust, multi-functional materials by self-organization of structures over many length- and time scales, from nano to macro. Some of the structural features are commonly found in a many different tissues, that is, they are highly conserved. Examples of such universal building blocks include alpha-helices, beta-sheets or tropocollagen molecules. In contrast, other features are highly specific to tissue types, such as particular filament assemblies, beta-sheet nanocrystals in spider silk or tendon fascicles. These examples illustrate that the coexistence of universality and diversity – in the following referred to as the universality-diversity paradigm (UDP) – is an overarching feature in protein materials. This paradigm is a paradox: How can a structure be universal and diverse at the same time? In protein materials, the coexistence of universality and diversity is enabled by utilizing hierarchies, which serve as an additional dimension beyond the 3D or 4D physical space. This may be crucial to understand how their structure and properties are linked, and how these materials are capable of combining seemingly disparate properties such as strength and robustness. Here we illustrate how the UDP enables to unify universal building blocks and highly diversified patterns through formation of hierarchical structures that lead to multi-functional, robust yet highly adapted structures. We illustrate these concepts in an analysis of three types of intermediate filament proteins, including vimentin, lamin and keratin

Computer simulation of pulsed field gel runs allows the quantitation of radiation-induced double-strand breaks in yeast

A procedure for the quantification of double-strand breaks in yeast is presented that utilizes pulsed field gel electrophoresis (PFGE) and a comparison of the observed DNA mass distribution in the gel lanes with calculated distributions. Calculation of profiles is performed as follows. If double-strand breaks are produced by sparsely ionizing radiation, one can assume that they are distributed randomly in the genome, and the resulting DNA mass distribution in molecular length can be predicted by means of a random breakage model. The input data for the computation of molecular length profiles are the breakage frequency per unit length, , as adjustable parameter, and the molecular lengths of the intact chromosomes. The obtained DNA mass distributions in molecular length must then be transformed into distributions of DNA mass in migration distance. This requires a calibration of molecular length vs. migration distance that is specific for the gel lane in question. The computed profiles are then folded with a Lorentz distribution with adjusted spread parameter to account for and broadening. The DNA profiles are calculated for different breakage frequencies and for different values of , and the parameters resulting in the best fit of the calculated to the observed profile are determined

ECUT (Energy Conversion and Utilization Technologies) program: Biocatalysis Project

Fiscal year 1987 research activities and accomplishments for the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Division are presented. The project's technical activities were organized into three work elements. The Molecular Modeling and Applied Genetics work element includes modeling and simulation studies to verify a dynamic model of the enzyme carboxypeptidase; plasmid stabilization by chromosomal integration; growth and stability characteristics of plasmid-containing cells; and determination of optional production parameters for hyper-production of polyphenol oxidase. The Bioprocess Engineering work element supports efforts in novel bioreactor concepts that are likely to lead to substantially higher levels of reactor productivity, product yields, and lower separation energetics. The Bioprocess Design and Assessment work element attempts to develop procedures (via user-friendly computer software) for assessing the economics and energetics of a given biocatalyst process

The Energy Landscape, Folding Pathways and the Kinetics of a Knotted Protein

The folding pathway and rate coefficients of the folding of a knotted protein are calculated for a potential energy function with minimal energetic frustration. A kinetic transition network is constructed using the discrete path sampling approach, and the resulting potential energy surface is visualized by constructing disconnectivity graphs. Owing to topological constraints, the low-lying portion of the landscape consists of three distinct regions, corresponding to the native knotted state and to configurations where either the N- or C-terminus is not yet folded into the knot. The fastest folding pathways from denatured states exhibit early formation of the N-terminus portion of the knot and a rate-determining step where the C-terminus is incorporated. The low-lying minima with the N-terminus knotted and the C-terminus free therefore constitute an off-pathway intermediate for this model. The insertion of both the N- and C-termini into the knot occur late in the folding process, creating large energy barriers that are the rate limiting steps in the folding process. When compared to other protein folding proteins of a similar length, this system folds over six orders of magnitude more slowly.Comment: 19 page

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference