Best of Both Worlds: Uniform sampling in Cartesian and Cayley Molecular Assembly Configuration Space
Aysegul Ozkan, Meera Sitharam

TL;DR
This paper enhances the EASAL geometric sampling method for molecular assembly landscapes by enabling uniform sampling in Cartesian space, improving accuracy in free energy and kinetics calculations.
Contribution
It introduces modifications to EASAL that ensure uniform sampling in Cartesian space while maintaining the advantages of Cayley parameterization, addressing Jacobian ill-conditioning issues.
Findings
Modified EASAL achieves better coverage in Cartesian space.
Performance tested on human and rat islet amylin polypeptides.
Improved accuracy in configurational entropy estimation.
Abstract
EASAL (efficient atlasing and sampling of assembly landscapes) is a recently reported geometric method for representing, visualizing, sampling and computing integrals over the potential energy landscape tailored for small molecular assemblies. EASAL's efficiency arises from the fact that small assembly landscapes permit the use of so-called Cayley parameters (inter-atomic distances) for geometric representation and sampling of the assembly configuration space regions; this results in their isolation, convexification, customized sampling and systematic traversal using a comprehensive topological roadmap, ensuring reasonable coverage of crucial but narrow regions of low effective dimension. However, this alone is inadequate for accurate computation of configurational entropy and other integrals, required for estimation of both free energy and kinetics - where it is essential to obtain…
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Taxonomy
TopicsMachine Learning in Materials Science · Protein Structure and Dynamics · Supramolecular Self-Assembly in Materials
