Abstract: "In the next century, virtual laboratories will play a key role in biotechnology. Computer experiments will not only replace time-consuming and expensive real-world experiments, but they will also provide insights that cannot be obtained using 'wet' experiments. The field that deals with the modeling of atoms, molecules, and their reactions is called Molecular Modeling. The advent of Life Sciences gave rise to numerous new developments in this area. However, the implementation of new simulation tools is extremely time-consuming. This is mainly due to the large amount of supporting code (e.g. for data import/export, visualization, and so on) that is required in addition to the code necessary to implement the new idea. The only way to reduce the development time is to reuse reliable code, preferably using object-oriented approaches. We have designed and implemented BALL, the first object-oriented application framework for rapid prototyping in Molecular Modeling. By the use of the composite design pattern and polymorphism we were able to model the multitude of complex biochemical concepts in a well-structured and comprehensible class hierarchy, the BALL kernel classes. The isomorphism between the biochemical structures and the kernel classes leads to an intuitive interface. Since BALL was designed for rapid software prototyping, ease of use and flexibility were our principal design goals. Besides the kernel classes, BALL provides fundamental components for import/export of data in various file formats, Molecular Mechanics simulations, three-dimensional visualization, and more complex ones like a numerical solver for the Poisson-Boltzmann equation. The usefulness of BALL was shown by the implementation of an algorithm that checks proteins for similarity. Instead of the five months that an earlier implementation took, we were able to implement it within a day using BALL."

Abstract: "We have developed and implemented a parallel distributed algorithm for the rigid-body protein docking problem. The algorithm is based on a new fitness function for evaluating the surface matching of a given conformation. The fitness function is defined as the weighted sum of two contact measures, the geometric contact measure and the chemical contact measure. The geometric contact measure measures the 'size' of the contact area of two molecules. It is a potential function that counts the 'van der Waals contacts' between the atoms of the two molecules (the algorithm does not compute the Lennard-Jones potential). The chemical contact measure is also based on the 'van der Waals contacts' principle: We consider all atom pairs that have a 'van der Waals' contact, but instead of adding a constant for each pair (a, b) we add a 'chemical weight' that depends on the atom pair (a, b). We tested our docking algorithm with a test set that contains the test examples of Norel et al. [NLWN94] and Fischer et al. [FLWN95] and compared the results of our docking algorithm with the results of Norel et al. [NLWN94, NLWN95], with the results of Fischer et al. [FLWN95] and with the results of Meyer et al. [[MWS96]. In 32 of 35 test examples the best conformation with respect to the fitness function was an approximation of the real conformation."

Abstract: "We have implemented a parallel distributed geometric docking algorithm that uses a new measure for the size of the contact area of two molecules. The measure is a potential function that counts the 'van der Waals contacts' between the atoms of the two molecules (the algorithm does not compute the Lennard-Jones potential). An integer constant c[subscript a] is added to the potential for each pair of atoms whose distance is in a certain interval. For each pair whose distance is smaller than the lower bound of the interval an integer constant c[subscript s] is subtracted from the potential (c[subscript a]

We present an algorithm with running time O((n+k+p)log n), where n is the number of spheres in the scene; p is the number of transparent topology changes (the number of different scene topologies visible along the flightpath, assuming that all spheres are transparent); and k denotes the number of vertices (conflicts) which are in the (transparent) visibility graph at the start and do not disappear during the flight."