This work measures baseline sampling
characteristics that highlight
fundamental differences between sampling methods for assembly driven
by short-ranged pair potentials. Such granular comparison is essential
for fast, flexible, and accurate hybridization of complementary methods.
Besides sampling speed, efficiency, and accuracy of uniform grid coverage,
other sampling characteristics measured are (i) accuracy of covering
narrow low energy regions that have low effective dimension (ii) ability
to localize sampling to specific basins, and (iii) flexibility in
sampling distributions. As a proof of concept, we compare a recently
developed geometric methodology EASAL (Efficient Atlasing and Search
of Assembly Landscapes) and the traditional Monte Carlo (MC) method
for sampling the energy landscape of two assembling trans-membrane
helices, driven by short-range pair potentials. By measuring the above-mentioned
sampling characteristics, we demonstrate that EASAL provides localized
and accurate coverage of crucial regions of the energy landscape of
low effective dimension, under flexible sampling distributions, with
much fewer samples and computational resources than MC sampling. EASAL’s
empirically validated theoretical guarantees permit credible extrapolation
of these measurements and comparisons to arbitrary number and size
of assembling units. Promising avenues for hybridizing the complementary
advantages of the two methods are discussed.