AmbiPack: a systematic algorithm for packing of macromolecular structures with ambiguous distance constraints.

The determination of structures of multimers presents interesting new challenges. The structure(s) of the individual monomers must be found and the transformations to produce the packing interfaces must be described. A substantial difficulty results from ambiguities in assigning intermolecular distance measurements (from nuclear magnetic resonance, for example) to particular intermolecular interfaces in the structure. Here we present a rapid and efficient method to solve the packing and the assignment problems simultaneously given rigid monomer structures and (potentially ambiguous) intermolecular distance measurements. A promising application of this algorithm is to couple it with a monomer searching protocol such that each monomer structure consistent with intramolecular constraints can be subsequently input to the current algorithm to check whether it is consistent with (potentially ambiguous) intermolecular constraints. The algorithm AmbiPack uses a hierarchical division of the search space and the branch-and-bound algorithm to eliminate infeasible regions of the space. Local search methods are then focused on the remaining space. The algorithm generally runs faster as more constraints are included because more regions of the search space can be eliminated. This is not the case for other methods, for which additional constraints increase the complexity of the search space. The algorithm presented is guaranteed to find all solutions to a predetermined resolution. This resolution can be chosen arbitrarily to produce outputs at various level of detail. Illustrative applications are presented for the P22 tailspike protein (a trimer) and portions of beta-amyloid (an ordered aggregate).

An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization.

There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors, and to precisely identify locations of neighboring critical structures. The authors have developed an automatic technique for registering clinical data, such as segmented magnetic resonance imaging (MRI) or computed tomography (CT) reconstructions, with any view of the patient on the operating table. The authors demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient and the segmented three-dimensional (3-D) MRI or CT model. This supports enhanced reality techniques for planning and guiding neurosurgical procedures and allows us to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures, and clinical studies involving change detection over time sequences of images.

A shape-based machine learning tool for drug design.

Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.

Localizing overlapping parts by searching the interpretation tree.

This paper discusses how local measurements of positions and surface normals may be used to identify and locate overlapping objects. The objects are modeled as polyhedra (or polygons) having up to six degrees of positional freedom relative to the sensors. The approach operates by examining all hypotheses about pairings between sensed data and object surfaces and efficiently discarding inconsistent ones by using local constraints on: distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between sensed points. The method described here is an extension of a method for recognition and localization of nonoverlapping parts previously described in [18] and [15].

Tactile recognition and localization using object models: the case of polyhedra on a plane.

This paper discusses how data from multiple tactile sensors may be used to identify and locate one object, from among a set of known objects. We use only local information from sensors: 1) the position of contact points and 2) ranges of surface normals at the contact points. The recognition and localization process is structured as the development and pruning of a tree of consistent hypotheses about pairings between contact points and object surfaces. In this paper, we deal with polyhedral objects constrained to lie on a known plane, i.e., having three degrees of positioning freedom relative to the sensors. We illustrate the performance of the algorithm by simulation.