NDLI: A tree-based search to bias sampling of protein decoy conformations

Content Provider	IEEE Xplore Digital Library
Author	Molloy, K. Shehu, A.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, George Mason University, Fairfax, VA, 22030, United States (Molloy, K.; Shehu, A.)
Abstract	A central challenge in ab-initio structure prediction is the attainment of a large diverse ensemble of decoy conformations at low resolution so that subsequent refinement of selected decoys at higher resolution can identify the native structure among them [1]. The focus on the size and diversity is due to the weak discriminatory power of potential energy. Even state-of-the-art coarse grained energy functions are often weakly funneled, i.e., incapable of energetically distinguishing structurally dissimilar conformations [2]. While obtaining low-energy decoys increases the chance that a percentage of them are near-native, lowest energies are not necessarily associated with the native structure. Instead, the most effective selection techniques use redundancy, often in the form of the most populous conformational clusters identified, to identify the native structure. While redundancy is useful in this context, it is also a reason for the computational inefficiency of many conformational search algorithms.
Starting Page	978
Ending Page	978
File Size	135555
Page Count	1
File Format	PDF
ISBN	9781467327466
e-ISBN	9781467327473
e-ISBN	9781467327442
DOI	10.1109/BIBMW.2012.6470290
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Monte Carlo methods Conferences Redundancy Energy resolution Trajectory Convergence
Content Type	Text
Resource Type	Article

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