Many powerful tools have been created to detect and describe the similarities between multiple nucleic acid or multiple protein sequences. Frequently these take the form of a sequence consensus, expressing either simple most-popular positional identities, positional identities with allowances for varying positions, or some type of statistical description of the positional frequency characteristics of the defining sequence family. Despite the fact that some provide intuitively interpretable descriptions of the consenses themselves, they typically do not give the viewer any information about regions of the sequence that might have inter-positional dependencies, and that therefore do not obey a strict consensus behavior.

Such non-consensus behavior may be related to inter- or intra-molecular structural interactions, to phylogentic relationships, or to less understood biological causes. Recognizing these interactions can be useful for developing better motif and family descriptions, or for defining possible structural constraints on molecular families with unknown structure.

We present MAVL (Multiple Alignment Variation Linker) and StickWRLD. MAVL is our web-based application for detecting and displaying correlations in biomolecular sequence families. MAVL examines all positional pairs in each of a collection of pre-aligned sequences and determines any pairs that occur with unexpected frequency, and constructs either a static, downloadable VRML graph of the alignment properties, or a JAVA-powered interactive interface to the alignment.

MAVL/StickWRLD can be used through its web interface at http://www.microbial-pathogenesis.org/stickwrld/

This research has produced several publications in Nucleic Acids Research, including an invited cover. Additional information is available from our publications section, and on the MAVL/StickWRLD website.

Visualization of molecular structures is important for understanding basic molecular conformations and because structure is intimately tied to function. Molecular function however is also associated with molecular flexibility.

Visualization methods used for examining structural change however, are typically derived from methods for static structures. Typically these representations are overlays, or animations of multiple traces of the molecular backbone, with each trace representing a different point in time. Such representations are effectively flow representations using timelines (the linked location of the atoms at each discrete point in time) to depict motion. Timelines are not ideal for visualizing certain flow properties. In fact they are orthogonal to the representation most commonly and intuitively used to represent flow and motion.

We propose the use of atomic pathlines (the path of each atom over time) as an alternative for representing molecular change. This orthogonal transposition of the visualization presents molecular motion in a fashion analogous to a time-lapse photograph, and allows the 3-dimensional motion of individual atoms to be examined in detail, as well as the overall motion of domains to be understood in concert.

MoFlow analysis and rendering will be available through an online service, hopefully in early 2009.

This research has produced two posters accepted and presented at ACM SIGGraph.

Using classical computational chemistry techniques, the time required for docking small molecules to large molecules (i.e. repressor molecules to enzymes) is dominated by electrostatic force calculations. This computation is expensive due to both the unavoidable mathematical complexity of the calculation, and the rotational and translational degrees of freedom that must be afforded the system to produce a realistic simulation of the small molecule's trajectory as it approaches, and ultimately docks with the large molecule. These factors conspire to produce a system where many time-intensive instructions must be executed at each timepoint of a docking simulation, and where the expensive results can neither be pre-computed, nor cached for re-use, due to the relative movement of the molecules between timepoints.

Despite the necessity for numerous, expensive calculations for physically correct simulations of docking, we propose that computational screens that will accept or reject potential binding candidates from a library, can be accomplished with fewer, less time-consuming calculations.

Using an extension of the FlatWorld surface mapping proposed by Ray (Rustbelt RNA 1998), we produce topologically planar descriptions of macromolecular and candidate ligand surfaces. These descriptions capture local topography and electrostatic configuration as projected on a plane. The molecule is effectively "skinned".

We propose that these dramatically simplified descriptions contain sufficient detail to be used as a rapid binding pre-screen, to eliminate completely non-viable binding candidates, while eliminating at least 3 degrees of freedom which must be explored for the calculation.

This project has received phase 1 and phase 2 STTR funding from the US Department of Defense Medical Directorate, and a commercializable application implementing the docking screen is being produced.

Recurrent Urinary Tract infections in women, are typically thought to be caused by repeated re-introduction of infectious bacteria. Nationwide Children's Hospital investigator Dr. Sheryl Justice has proposed that recurrent infections are instead caused by a residual population of the original colonizing bacteria which become quiescent and evade the host's immune response, persisting intracellularly within the bladder epithelium until such time as some event induces their reactivation, whereupon a new cycle of infection begins.

Preliminary evidence from mouse models suggests that the primary determinant of survival is a morphological change (filamentation) undergone by a subset of the bacterial population, which apparently protects them from phagocytosis and possibly other host-immune effectors. The signals that mediate the morphological fates of the individual bacteria are so-far unknown. In this collaborative project we are developing a computer-vision and object-recognition application which will help to quantitate intracellular bacterial colony architecture, organization and composition.

The computational problem involves volume visualization and shape analysis within reconstructed 3D models extracted by optical microscopy. It is analogous to a visual attempt to quantitate the composition and organization of a plate of mixed rice and spaghetti.