期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2015
卷号：112
期号：11
页码：3235-3240
DOI：10.1073/pnas.1418241112
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceThe problem of generating and navigating high-dimensional free energy surfaces is a significant challenge in the study of complex systems. The approach introduced represents an advance in this area, and its ability to generate and organize the key features of a high-dimensional free energy surface, i.e., its landmarks, with high efficiency impacts numerous problems in the materials and biomolecular sciences for which prediction of optimal structures is key. These include polypeptide and nucleic acid structure and crystal design and structure prediction. Moreover, as the algorithm targets the free energy surface, candidate structures can be ranked based on their relative free energies, which is not possible with algorithms that target only the bare potential energy surface. Coarse graining of complex systems possessing many degrees of freedom can often be a useful approach for analyzing and understanding key features of these systems in terms of just a few variables. The relevant energy landscape in a coarse-grained description is the free energy surface as a function of the coarse-grained variables, which, despite the dimensional reduction, can still be an object of high dimension. Consequently, navigating and exploring this high-dimensional free energy surface is a nontrivial task. In this paper, we use techniques from multiscale modeling, stochastic optimization, and machine learning to devise a strategy for locating minima and saddle points (termed "landmarks") on a high-dimensional free energy surface "on the fly" and without requiring prior knowledge of or an explicit form for the surface. In addition, we propose a compact graph representation of the landmarks and connections between them, and we show that the graph nodes can be subsequently analyzed and clustered based on key attributes that elucidate important properties of the system. Finally, we show that knowledge of landmark locations allows for the efficient determination of their relative free energies via enhanced sampling techniques.
