Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways. Seyler, S. L., Kumar, A., Thorpe, M. F., & Beckstein, O. PLoS Comput Biol, 11(10):e1004568, October, 2015.
Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways [link]Paper  doi  abstract   bibtex   
Author Summary Many proteins are nanomachines that perform mechanical or chemical work by changing their three-dimensional shape and cycle between multiple conformational states. Computer simulations of such conformational transitions provide mechanistic insights into protein function but such simulations have been challenging. In particular, it is not clear how to quantitatively compare current simulation methods or to assess their accuracy. To that end, we present a general and flexible computational framework for quantifying transition paths—by measuring mutual geometric similarity—that, compared with existing approaches, requires minimal a-priori assumptions and can take advantage of full atomic detail alongside heuristic information derived from intuition. Using our Path Similarity Analysis (PSA) framework in parallel with several existing quantitative approaches, we examine transitions generated for a toy model of a transition and two biological systems, the enzyme adenylate kinase and diphtheria toxin. Our results show that PSA enables the quantitative comparison of different path sampling methods and aids the identification of potentially important atomistic motions by exploiting geometric information in transition paths. The method has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing macromolecular conformational transitions.
@article{seyler_path_2015,
	title = {Path {Similarity} {Analysis}: {A} {Method} for {Quantifying} {Macromolecular} {Pathways}},
	volume = {11},
	shorttitle = {Path {Similarity} {Analysis}},
	url = {https://doi.org/10.1371/journal.pcbi.1004568},
	doi = {10.1371/journal.pcbi.1004568},
	abstract = {Author Summary Many proteins are nanomachines that perform mechanical or chemical work by changing their three-dimensional shape and cycle between multiple conformational states. Computer simulations of such conformational transitions provide mechanistic insights into protein function but such simulations have been challenging. In particular, it is not clear how to quantitatively compare current simulation methods or to assess their accuracy. To that end, we present a general and flexible computational framework for quantifying transition paths—by measuring mutual geometric similarity—that, compared with existing approaches, requires minimal a-priori assumptions and can take advantage of full atomic detail alongside heuristic information derived from intuition. Using our Path Similarity Analysis (PSA) framework in parallel with several existing quantitative approaches, we examine transitions generated for a toy model of a transition and two biological systems, the enzyme adenylate kinase and diphtheria toxin. Our results show that PSA enables the quantitative comparison of different path sampling methods and aids the identification of potentially important atomistic motions by exploiting geometric information in transition paths. The method has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing macromolecular conformational transitions.},
	number = {10},
	urldate = {2015-10-22},
	journal = {PLoS Comput Biol},
	author = {Seyler, Sean L. and Kumar, Avishek and Thorpe, M. F. and Beckstein, Oliver},
	month = oct,
	year = {2015},
	pages = {e1004568},
}
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