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EASAL (Efficient Atlasing and Sampling of Assembly Landscapes) is a geometricmethod for sampling and computing integrals over the potential energy landscapeof small molecular assemblies. EASAL's efficiency arises from the fact thatsmall assembly landscapes permit the use of so-called Cayley (inter-atomicdistance based) parameters for geometric representation and sampling of theassembly configuration space regions; this results in their isolation,convexification, customized sampling and systematic traversal using acomprehensive topological roadmap.  We define custom-designed measurements to investigate and compare varioussampling characteristics of EASAL and the traditional Monte Carlo (MC)sampling, including (i) sampling speed, (ii) efficiency and accuracy of uniformgrid coverage, (iii) accuracy of weighted coverage at covering low energyregions, (iv) ability to localize sampling to macrostates, and (v) flexibilityin sampling distributions.  In particular, we compare the sampling characteristics of EASAL and MC insampling the assembly landscape of 2 trans-membrane helices, with short-rangepair-potentials. We demonstrate that EASAL provides a reasonable coverage ofcrucial but narrow regions of the energy landscape of low effective dimension,with much fewer samples and computational resources than MC sampling. Promisingavenues for combining the complementary advantages of the two methods arediscussed.

Fast and Flexible Geometric Method For Enhancing MC Sampling of Compact Configurations For Protein Docking Problem