Modeling Biomolecules: Larger Scales, Longer Durations
The fast growth of molecular modeling as a research tool in biology and
medicine has been tightly coupled to the advent of the supercomputer and
advances in applied and computational mathematics over the past decade.
Three features characterize the progress made to date: bigger molecular
systems described in atomic detail, longer simulation time scales, and
more realistic representations of interatomic forces. With these improvements,
molecular modeling by computer has given us many insights into the relationship
between structure and function of biopolymers and drugs. Researchers now
find it indispensable for structure refinement.
Still the state of the art in molecular modeling leaves much room for more
Simulations of biopolymers must be extended from the current few thousand
atoms to systems of 100,000 or more atoms.
The time scale of simulations in molecular dynamics must reach beyond the
present nanosecond horizon to describe longer processes of biologically
relevant duration. Examples are substrate binding, enzyme reactions, and
the folding of proteins into their native form.
Descriptions of interatomic forces must be improved to include such factors
as atomic polarizabilities and to combine molecular modeling and quantum
Molecular dynamics simulations must speed up by several orders of magnitude
to allow the increases in system size and time scale that researchers need.
To achieve this speed and improve the quality of force-field representations,
scientists will exploit advances in processor speed and in numerical and
parallel algorithms. The increasingly important role of molecular
modeling in biology and chemistry has already led to the formation of multidisciplinary
teams to provide the needed knowledge of hardware, software, mathematics
Our groups are actively developing computer programs with improved schemes
for numerical integration, parallelization and efficient and scalable evaluation
of electrostatic force fields. These programs are targeted for a variety
of scalable shared-memory and distributed-memory machines, including networks
of workstations. Describing this current research and its background will
give readers a taste of the challenges ahead in computational biology.
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