Oliver Brock | PubMed Health

PubMed2005

A methodology for efficiently sampling the conformation space of molecular structures.

Motivated by recently developed computational techniques for studying protein flexibility, and their potential applications in docking, we propose an efficient method for sampling the conformational space of complex molecular structures. We focus on the loop closure problem, identified in the work of Thorpe and Lei (2004 Phil. Mag. 84 1323-31) as a primary bottleneck in the fast simulation of molecular motions. By modeling a molecular structure as a branching robot, we use an intuitive method in which the robot holds onto itself for maintaining loop constraints. New conformations are generated by applying random external forces, while internal, attractive forces pull the loops closed. Our implementation, tested on several model molecules with low number of degrees of freedom but many interconnected loops, gives promising results that show an almost four times speed-up on the benchmark cube-molecule of Thorpe and Lei.

Algorithms↗

PubMed2005

Improving protein structure prediction with model-based search.

MOTIVATION: De novo protein structure prediction can be formulated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search problems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function. This model is incrementally refined in areas of interest, whereas areas that are not of interest are excluded from further exploration. Model-based search derives its efficiency from the fact that the information obtained during the exploration of the search space is used to guide further exploration. In contrast, Monte Carlo-based techniques lack memory and exploration is performed based on random walks, ignoring the information obtained in previous steps. RESULTS: Model-based search is applied to protein structure prediction, where search is employed to find the global minimum of the protein's energy landscape. We show that model-based search uses computational resources more efficiently to find lower-energy conformations of proteins than one of the leading protein structure prediction methods, which relies on a tailored Monte Carlo method to perform a search. The performance improvements become more pronounced as the dimensionality of the search problem increases. We argue that model-based search will enable more accurate protein structure prediction than was previously possible. Furthermore, we believe that similar performance improvements can be expected in other problems that are currently solved using Monte Carlo-based search methods. AVAILABILITY: An implementation of model-based search can be obtained by contacting the authors.

Amino Acids↗