EASAL (efficient atlasing and sampling of assembly landscapes) is a recentlyreported geometric method for representing, visualizing, sampling and computingintegrals over the potential energy landscape tailored for small molecularassemblies. EASAL's efficiency arises from the fact that small assemblylandscapes permit the use of so-called Cayley parameters (inter-atomicdistances) for geometric representation and sampling of the assemblyconfiguration space regions; this results in their isolation, convexification,customized sampling and systematic traversal using a comprehensive topologicalroadmap. By sampling the assembly landscape of 2 TransMembrane Helices, withshort-range pair-potentials, this paper demonstrates that EASAL providesreasonable coverage of crucial but narrow regions of low effective dimensionwith much fewer samples and computational resources than traditional MonteCarloor Molecular Dynamics based sampling. Promising avenues are discussed, forcombining the complementary advantages of the two methods. Additionally, since accurate computation of configurational entropy and otherintegrals is required for estimation of both free energy and kinetics, it isessential to obtain uniform sampling in appropriate cartesian or moduli spaceparameterization. EASAL's flexibility is demonstrated with a variety ofsampling distributions, from Cayley sampling skewed towards lower energyregions, to uniform Cartesian sampling at the two ends of the spectrum.
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