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Algorithmic challenges in computational molecular biophysics. (English) Zbl 0935.92008
Computational techniques for modeling large biological molecules have emerged rapidly in recent years as an important complement to experiment. Computer-generated models and simulation data are essential for analyzing structural and kinetic details that are difficult to capture experimentally for large, floppy macromolecules in solution. Modeling approaches also permit systematic studies of the dependence of equilibrium and kinetic properties on internal (e.g., the amino acid sequence) and external (e.g., the salt concentration in the environment) factors. Such studies can enhance our understanding of biological function through the structure/function connection. New lines of experimentation can be proposed on this basis, and many important practical applications, from medicine to technology, follow.
This article describes some accomplishments of a collaborative group funded for 5 years by the National Science Foundation, commencing in 1993, under the High-Performance Computing and Communication initiative. The work described herein also provides a perspective on activities in the field in general. The algorithmic areas covered in this article are long-time integration (Section 2), rapid evaluation of electrostatic potentials (Section 3), high-performance implementations of large simulation packages (Section 5), and experimental-data refinement (Section 6). The remainder of Section 1 describes four examples to illustrate the above computational challenges in terms of specific biomolecular applications. Section 4 presents a case study in the merging of accelerated timestepping and electrostatic protocols in a protein dynamics application. Future perspectives are described in the final section.

92C05 Biophysics
92-08 Computational methods for problems pertaining to biology
65Y99 Computer aspects of numerical algorithms
Full Text: DOI
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