Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

Aysegul Ozkan; Meera Sitharam; Jose C Flores-Canales; Rahul Prabhu; Maria Kurnikova

doi:10.1021/acs.jctc.0c00945

Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization

J Chem Theory Comput. 2021 Mar 9;17(3):1967-1987. doi: 10.1021/acs.jctc.0c00945. Epub 2021 Feb 12.

Authors

Aysegul Ozkan¹, Meera Sitharam¹, Jose C Flores-Canales², Rahul Prabhu¹, Maria Kurnikova³

Affiliations

¹ CISE Department, University of Florida, Gainesville, Florida 32611-6120, United States.
² Department of Chemistry, Aarhus University, Aarhus, Denmark 8000.
³ Chemistry Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

PMID: 33576635
DOI: 10.1021/acs.jctc.0c00945

Abstract

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.