Adaptive partitioning by local density‐peaks: An efficient density‐based clustering algorithm for analyzing molecular dynamics trajectories

We present an efficient density‐based adaptive‐resolution clustering method APLoD for analyzing large‐scale molecular dynamics (MD) trajectories. APLoD performs the k‐nearest‐neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same...

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Veröffentlicht in:	Journal of computational chemistry Jg. 38; H. 3; S. 152 - 160
Hauptverfasser:	Liu, Song, Zhu, Lizhe, Sheong, Fu Kit, Wang, Wei, Huang, Xuhui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 30.01.2017
Schlagworte:	Algorithms Binding sites Biochemistry clustering algorithm Density density peaks kNN search Ligands Markov analysis Markov State Models Molecular chemistry molecular dynamics Molecular Dynamics Simulation Proteins Proteins - chemistry Markov State Models clustering algorithm density peaks kNN search molecular dynamics
ISSN:	0192-8651, 1096-987X, 1096-987X
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Abstract	We present an efficient density‐based adaptive‐resolution clustering method APLoD for analyzing large‐scale molecular dynamics (MD) trajectories. APLoD performs the k‐nearest‐neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high‐density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2–3 orders of magnitude for systems ranging from alanine dipeptide to a 370‐residue Maltose‐binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low‐density regions, while smaller clusters at high‐density regions), which is a clear advantage over other popular clustering algorithms including k‐centers and k‐medoids. We anticipate that APLoD can be widely applied to split ultra‐large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. Incorporating the k‐nearest‐neighbors search into the density peaks clustering algorithm results in a novel clustering method Adaptive Partitioning by Local Density‐peaks (APLoD) for analyzing of molecular dynamics (MD) trajectories. APLoD is highly efficient and applicable to large MD datasets containing millions of frames. The density‐based feature and adaptive resolution of APLoD make it particularly useful in constructing Markov State Models for complex processes, especially those with heterogeneous metastable regions.
AbstractList	We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc.We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. We present an efficient density‐based adaptive‐resolution clustering method APLoD for analyzing large‐scale molecular dynamics (MD) trajectories. APLoD performs the k‐nearest‐neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high‐density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2–3 orders of magnitude for systems ranging from alanine dipeptide to a 370‐residue Maltose‐binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low‐density regions, while smaller clusters at high‐density regions), which is a clear advantage over other popular clustering algorithms including k‐centers and k‐medoids. We anticipate that APLoD can be widely applied to split ultra‐large MD datasets containing millions of conformations for subsequent construction of Markov State Models. © 2016 Wiley Periodicals, Inc. Incorporating the k‐nearest‐neighbors search into the density peaks clustering algorithm results in a novel clustering method Adaptive Partitioning by Local Density‐peaks (APLoD) for analyzing of molecular dynamics (MD) trajectories. APLoD is highly efficient and applicable to large MD datasets containing millions of frames. The density‐based feature and adaptive resolution of APLoD make it particularly useful in constructing Markov State Models for complex processes, especially those with heterogeneous metastable regions. We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD performs the k-nearest-neighbors search to estimate the density of MD conformations in a local fashion, which can group MD conformations in the same high-density region into a cluster. APLoD greatly improves the popular density peaks algorithm by reducing the running time and the memory usage by 2-3 orders of magnitude for systems ranging from alanine dipeptide to a 370-residue Maltose-binding protein. In addition, we demonstrate that APLoD can produce clusters with various sizes that are adaptive to the underlying density (i.e., larger clusters at low-density regions, while smaller clusters at high-density regions), which is a clear advantage over other popular clustering algorithms including k-centers and k-medoids. We anticipate that APLoD can be widely applied to split ultra-large MD datasets containing millions of conformations for subsequent construction of Markov State Models.
Author	Zhu, Lizhe Sheong, Fu Kit Huang, Xuhui Liu, Song Wang, Wei
Author_xml	– sequence: 1 givenname: Song surname: Liu fullname: Liu, Song organization: The Hong Kong University of Science and Technology – sequence: 2 givenname: Lizhe surname: Zhu fullname: Zhu, Lizhe email: chlizhezhu@ust.hk organization: Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology – sequence: 3 givenname: Fu Kit surname: Sheong fullname: Sheong, Fu Kit organization: The Hong Kong University of Science and Technology – sequence: 4 givenname: Wei surname: Wang fullname: Wang, Wei organization: Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology – sequence: 5 givenname: Xuhui surname: Huang fullname: Huang, Xuhui email: xuhuihuang@ust.hk organization: Center of Systems Biology and Human Health, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology
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Keywords	Markov State Models clustering algorithm density peaks kNN search molecular dynamics
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Snippet	We present an efficient density‐based adaptive‐resolution clustering method APLoD for analyzing large‐scale molecular dynamics (MD) trajectories. APLoD... We present an efficient density-based adaptive-resolution clustering method APLoD for analyzing large-scale molecular dynamics (MD) trajectories. APLoD...
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SubjectTerms	Algorithms Binding sites Biochemistry clustering algorithm Density density peaks kNN search Ligands Markov analysis Markov State Models Molecular chemistry molecular dynamics Molecular Dynamics Simulation Proteins Proteins - chemistry
Title	Adaptive partitioning by local density‐peaks: An efficient density‐based clustering algorithm for analyzing molecular dynamics trajectories
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