Learning reaction coordinates via cross-entropy minimization: Application to alanine dipeptide
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This work proposes a cross-entropy minimization method for finding the reaction coordinate from a large number of collective variables in complex molecular systems and introduces the L2-norm regularization used in the machine learning field to prevent overfitting when the number of considered collective variables is large.Abstract:
We propose a cross-entropy minimization method for finding the reaction coordinate from a large number of collective variables in complex molecular systems. This method is an extension of the likelihood maximization approach describing the committor function with a sigmoid. By design, the reaction coordinate as a function of various collective variables is optimized such that the distribution of the committor pB* values generated from molecular dynamics simulations can be described in a sigmoidal manner. We also introduce the L2-norm regularization used in the machine learning field to prevent overfitting when the number of considered collective variables is large. The current method is applied to study the isomerization of alanine dipeptide in vacuum, where 45 dihedral angles are used as candidate variables. The regularization parameter is determined by cross-validation using training and test datasets. It is demonstrated that the optimal reaction coordinate involves important dihedral angles, which are consistent with the previously reported results. Furthermore, the points with pB*∼0.5 clearly indicate a separatrix distinguishing reactant and product states on the potential of mean force using the extracted dihedral angles.read more
Citations
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References
More filters
Journal ArticleDOI
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
Mark Abraham,Teemu Murtola,Roland Schulz,Roland Schulz,Szilárd Páll,Jeremy C. Smith,Jeremy C. Smith,Berk Hess,Erik Lindahl,Erik Lindahl +9 more
TL;DR: GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules, and provides a rich set of calculation types.
Journal ArticleDOI
Comparison of multiple Amber force fields and development of improved protein backbone parameters.
Viktor Hornak,Robert Abel,Asim Okur,Bentley Strockbine,Adrian E. Roitberg,Carlos Simmerling,Carlos Simmerling +6 more
TL;DR: An effort to improve the φ/ψ dihedral terms in the ff99 energy function achieves a better balance of secondary structure elements as judged by improved distribution of backbone dihedrals for glycine and alanine with respect to PDB survey data.
Journal ArticleDOI
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling
G.M. Torrie,John P. Valleau +1 more
TL;DR: In this paper, the authors describe the use of arbitrary sampling distributions chosen to facilitate the estimate of the free energy difference between a model system and some reference system, but the conventional Monte Carlo methods of obtaining such averages are inadequate for the free-energy case.
Journal ArticleDOI
Escaping free-energy minima
TL;DR: A powerful method for exploring the properties of the multidimensional free energy surfaces of complex many-body systems by means of coarse-grained non-Markovian dynamics in the space defined by a few collective coordinates is introduced.
Journal ArticleDOI
Replica-exchange molecular dynamics method for protein folding
Yuji Sugita,Yuko Okamoto +1 more
TL;DR: In this article, a replica-exchange method was proposed to overcome the multiple-minima problem by exchanging non-interacting replicas of the system at several temperatures, which allows the calculation of any thermodynamic quantity as a function of temperature in that range.
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