Validating clustering of molecular dynamics simulations using polymer models.

BACKGROUND: Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. RESULTS: We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. CONCLUSIONS: We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

A bimodal distribution of two distinct categories of intrinsically disordered structures with separate functions in FG nucleoporins.

Nuclear pore complexes (NPCs) gate the only conduits for nucleocytoplasmic transport in eukaryotes. Their gate is formed by nucleoporins containing large intrinsically disordered domains with multiple phenylalanine-glycine repeats (FG domains). In combination, these are hypothesized to form a structurally and chemically homogeneous network of random coils at the NPC center, which sorts macromolecules by size and hydrophobicity. Instead, we found that FG domains are structurally and chemically heterogeneous. They adopt distinct categories of intrinsically disordered structures in non-random distributions. Some adopt globular, collapsed coil configurations and are characterized by a low charge content. Others are highly charged and adopt more dynamic, extended coil conformations. Interestingly, several FG nucleoporins feature both types of structures in a bimodal distribution along their polypeptide chain. This distribution functionally correlates with the attractive or repulsive character of their interactions with collapsed coil FG domains displaying cohesion toward one another and extended coil FG domains displaying repulsion. Topologically, these bipartite FG domains may resemble sticky molten globules connected to the tip of relaxed or extended coils. Within the NPC, the crowding of FG nucleoporins and the segregation of their disordered structures based on their topology, dimensions, and cohesive character could force the FG domains to form a tubular gate structure or transporter at the NPC center featuring two separate zones of traffic with distinct physicochemical properties.

Using texture to analyze and manage large collections of remote sensed image and video data.

We describe recent research into using the visual primitive of texture to analyze and manage large collections of remote sensed image and video data. Texture is regarded as the spatial dependence of pixel intensity. It is characterized by the amount of dependence at different scales and orientations, as measured with frequency-selective filters. A homogeneous texture descriptor based on the filter outputs is shown to enable (1) content-based image retrieval in large collections of satellite imagery, (2) semantic labeling and layout retrieval in an aerial video management system, and (3) statistical object modeling in geographic digital libraries.