Using evolutionary covariance to infer protein sequence-structure relationships

During the last half century, a deep knowledge of the actions of proteins has emerged from a broad range of experimental and computational methods. This means that there are now many opportunities for understanding how the varieties of proteins affect larger scale behaviors of organisms, in terms of phenotypes and diseases. It is broadly acknowledged that sequence, structure and dynamics are the three essential components for understanding proteins. Learning about the relationships among protein sequence, structure and dynamics becomes one of the most important steps for understanding the mechanisms of proteins. Together with the rapid growth in the efficiency of computers, there has been a commensurate growth in the sizes of the public databases for proteins. The field of computational biology has undergone a paradigm shift from investigating single proteins to looking collectively at sets of related proteins and broadly across all proteins. we develop a novel approach that combines the structure knowledge from the PDB, the CATH database with sequence information from the Pfam database by using co-evolution in sequences to achieve the following goals: (a) Collection of co-evolution information on the large scale by using protein domain family data; (b) Development of novel amino acid substitution matrices based on the structural information incorporated; (c) Higher order co-evolution correlation detection. The results presented here show that important gains can come from improvements to the sequence matching. What has been done here is simple and the pair correlations in sequence have been decomposed into singlet terms, which amounts to discarding much of the correlation information itself. The gains shown here are encouraging, and we would like to develop a sequence matching method that retains the pair (or higher order) correlation information, and even higher order correlations directly, and this should be possible by developing the sequence matching separately for different domain structures. The many body correlations in particular have the potential to transform the common perceptions in biology from pairs that are not actually so very informative to higher-order interactions. Fully understanding cellular processes will require a large body of higher-order correlation information such as has been initiated here for single proteins

Automated annotation of gene expression image sequences via non-parametric factor analysis and conditional random fields

Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously. Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points. Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages

Probing DNA-Induced Colloidal Interactions and Dynamics with Scanning-Line Optical Tweezers

A promising route to forming novel nanoparticle-based materials is directed self-assembly, where the interactions among multiple species of suspended particles are intentionally designed to favor the self-assembly of a specific cluster arrangement or nanostructure. DNA provides a natural tool for directed particle assembly because DNA double helix formation is chemically specific — particles with short single-stranded DNA grafted on their surfaces will be bridged together only if those strands have complementary base sequences. Moreover, the temperature-dependent stability of such DNA bridges allows the resulting attraction to be modulated from negligibly weak to effectively irreversible over a convenient range of temperatures. Surprisingly, existing models for DNA-induced particle interactions are typically in error by more than an order of magnitude, which has hindered efforts to design complex temperature, sequence and time-dependent interactions needed for the most interesting applications. Here we report the first spatially resolved measurements of DNA-induced interactions between pairs of polystyrene microspheres at binding strengths comparable to those used in self-assembly experiments. The pair-interaction energies measured with our optical tweezers instrument can be modeled quantitatively with a conceptually straightforward and numerically tractable model, boding well for their application to direct self-assembly. In addition to understanding the equilibrium interactions between DNA-labeled particles, it is also important to consider the dynamics with which they bind to and unbind from one another. Here we demonstrate for the first time that carefully designed systems of DNA-functionalized particles exhibit effectively diffusion-limited binding, suggesting that these interactions are suitable to direct efficient self-assembly. We systematically explore the transition from diffusion-limited to reaction-limited binding by decreasing the DNA labeling density, and develop a simple dynamic model that is able to reproduce some of the anomalous kinetics observed in multivalent binding processes. Specifically, we find that when compounded, static disorder in the melting rate of single DNA duplexes gives rise to highly non-exponential lifetime distributions in multivalent binding. Together, our findings motivate a nanomaterial design approach where novel functional structures can be found computationally and then reliably realized in experiment

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988