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. 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.
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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Cites_doi	10.1073/pnas.1315751111 10.1038/ncomms11244 10.1002/prot.21123 10.1371/journal.pcbi.1003020 10.1016/S0024-3795(00)00095-1 10.1063/1.3565032 10.1016/0304-3975(85)90224-5 10.1063/1.2116947 10.1016/j.sbi.2010.10.006 10.1021/acs.jctc.5b00498 10.1063/1.3301140 10.1021/acs.jctc.5b01233 10.1021/jp076377+ 10.1063/1.2959573 10.1016/j.sbi.2014.04.002 10.1063/1.2714538 10.1016/j.eswa.2008.01.039 10.1080/00031305.1992.10475879 10.1016/S0022-2836(83)80304-0 10.1126/science.1242072 10.1016/S0003-2670(01)95359-0 10.1016/j.ymeth.2010.06.002 10.1021/ct4009156 10.1021/acs.jctc.5b00737 10.1371/journal.pcbi.1004404 10.1145/1364782.1364802 10.1021/ct5007168 10.1002/prot.20033 10.1021/ct500827g 10.1002/jcc.23110 10.1021/jp0761665 10.1021/acs.accounts.5b00536 10.1021/ct900620b 10.1073/pnas.1103547108 10.1109/TIT.1982.1056489 10.1287/moor.10.2.180 10.1021/ct5002363 10.1063/1.4802007 10.1016/j.ymeth.2009.04.013 10.1038/268765a0 10.1021/acs.jctc.5b00553 10.1073/pnas.0909088106 10.1371/journal.pcbi.1003767 10.1371/journal.pcbi.1001015
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References	2007; 126 2000; 315 2015; 11 2008 2014; 25 2008; 129 1993 2008; 51 2014; 111 1977; 268 2011; 134 2009; 49 2013; 9 2016; 12 2004; 55 2009; 36 2016; 6 2016; 7 1983; 168 1982; 28 2011; 108 2006; 65 2005; 123 2013; 34 2013; 138 1987 2010; 132 2011; 21 1992; 46 1982; 136 2008; 112 2016; 49 1985; 10 2010; 52 1985; 38 2010; 6 1967 2014; 10 2009; 106 2014; 344 Zhang (10.1002/jcc.24664-BIB0036\|jcc24664-cit-0037) 2016; 49 Gonzalez (10.1002/jcc.24664-BIB0004\|jcc24664-cit-0005) 1985; 38 Park (10.1002/jcc.24664-BIB0010\|jcc24664-cit-0011) 2009; 36 Morcos (10.1002/jcc.24664-BIB0020\|jcc24664-cit-0021) 2010; 6 Buchete (10.1002/jcc.24664-BIB0017\|jcc24664-cit-0018) 2008; 112 Pande (10.1002/jcc.24664-BIB0011\|jcc24664-cit-0012) 2010; 52 Prinz (10.1002/jcc.24664-BIB0013\|jcc24664-cit-0014) 2011; 134 Vitalini (10.1002/jcc.24664-BIB0033\|jcc24664-cit-0034) 2015; 11 Zhu (10.1002/jcc.24664-BIB0037\|jcc24664-cit-0038) Zheng (10.1002/jcc.24664-BIB0015\|jcc24664-cit-0016) 2008; 112 Da (10.1002/jcc.24664-BIB0024\|jcc24664-cit-0025) 2013; 9 10.1002/jcc.24664-BIB0009\|jcc24664-cit-0010 Malmstrom (10.1002/jcc.24664-BIB0026\|jcc24664-cit-0027) 2014; 10 Coomans (10.1002/jcc.24664-BIB0041\|jcc24664-cit-0042) 1982; 136 Deuflhard (10.1002/jcc.24664-BIB0047\|jcc24664-cit-0048) 2000; 315 Buch (10.1002/jcc.24664-BIB0022\|jcc24664-cit-0023) 2011; 108 Rodriguez (10.1002/jcc.24664-BIB0039\|jcc24664-cit-0040) 2014; 344 Chodera (10.1002/jcc.24664-BIB0014\|jcc24664-cit-0015) 2007; 126 Da (10.1002/jcc.24664-BIB0035\|jcc24664-cit-0036) 2016; 7 Singhal (10.1002/jcc.24664-BIB0049\|jcc24664-cit-0050) 2005; 123 Bowman (10.1002/jcc.24664-BIB0019\|jcc24664-cit-0020) 2009; 49 Keller (10.1002/jcc.24664-BIB0038\|jcc24664-cit-0039) 2010; 132 Altman (10.1002/jcc.24664-BIB0042\|jcc24664-cit-0043) 1992; 46 McCammon (10.1002/jcc.24664-BIB0001\|jcc24664-cit-0002) 1977; 268 Yao (10.1002/jcc.24664-BIB0023\|jcc24664-cit-0024) 2013; 138 Zhao (10.1002/jcc.24664-BIB0044\|jcc24664-cit-0045) 2013; 34 Pan (10.1002/jcc.24664-BIB0016\|jcc24664-cit-0017) 2008; 129 Levitt (10.1002/jcc.24664-BIB0002\|jcc24664-cit-0003) 1983; 168 10.1002/jcc.24664-BIB0043\|jcc24664-cit-0044 Hornak (10.1002/jcc.24664-BIB0045\|jcc24664-cit-0046) 2006; 65 Kaufman (10.1002/jcc.24664-BIB0005\|jcc24664-cit-0006) 2008 Nüske (10.1002/jcc.24664-BIB0027\|jcc24664-cit-0028) 2014; 10 10.1002/jcc.24664-BIB0007\|jcc24664-cit-0008 Hochbaum (10.1002/jcc.24664-BIB0006\|jcc24664-cit-0007) 1985; 10 Jiang (10.1002/jcc.24664-BIB0032\|jcc24664-cit-0033) 2015; 11 Sheong (10.1002/jcc.24664-BIB0031\|jcc24664-cit-0032) 2015; 11 Gu (10.1002/jcc.24664-BIB0028\|jcc24664-cit-0029) 2014; 10 Noé (10.1002/jcc.24664-BIB0034\|jcc24664-cit-0035) 2015; 11 Onufriev (10.1002/jcc.24664-BIB0046\|jcc24664-cit-0047) 2004; 55 Trendelkamp-Schroer (10.1002/jcc.24664-BIB0048\|jcc24664-cit-0049) 2016; 6 Chodera (10.1002/jcc.24664-BIB0012\|jcc24664-cit-0013) 2014; 25 Bowman (10.1002/jcc.24664-BIB0050\|jcc24664-cit-0051) 2010; 6 Bowman (10.1002/jcc.24664-BIB0021\|jcc24664-cit-0022) 2011; 21 Sittel (10.1002/jcc.24664-BIB0040\|jcc24664-cit-0041) 2016; 12 Voelz (10.1002/jcc.24664-BIB0025\|jcc24664-cit-0026) 2014; 10 Huang (10.1002/jcc.24664-BIB0018\|jcc24664-cit-0019) 2009; 106 Shaw (10.1002/jcc.24664-BIB0003\|jcc24664-cit-0004) 2008; 51 Lloyd (10.1002/jcc.24664-BIB0008\|jcc24664-cit-0009) 1982; 28 Silva (10.1002/jcc.24664-BIB0029\|jcc24664-cit-0030) 2014; 111 Zimmerman (10.1002/jcc.24664-BIB0030\|jcc24664-cit-0031) 2015; 11
References_xml	– volume: 10 start-page: 1739 year: 2014 publication-title: J. Chem. Theory Comput. – volume: 52 start-page: 99 year: 2010 publication-title: Methods – volume: 268 start-page: 765 year: 1977 publication-title: Nature – volume: 10 start-page: 2648 year: 2014 publication-title: J. Chem. Theory Comput. – volume: 315 start-page: 39 year: 2000 publication-title: Linear Algebra Appl. – publication-title: Phys. Chem. Chem. Phys. – start-page: 281 year: 1967 end-page: 297 – volume: 136 start-page: 15 year: 1982 publication-title: Anal. Chim. Acta – volume: 112 start-page: 6083 year: 2008 publication-title: J. Phys. Chem. B – start-page: 311 year: 1993 end-page: 321 – volume: 11 start-page: e1004404 year: 2015 publication-title: PLoS Comput. Biol. – volume: 7 start-page: 11244 year: 2016 publication-title: Nat. Commun. – volume: 106 start-page: 19765 year: 2009 publication-title: Proc. Natl. Acad. Sci. USA – volume: 9 start-page: e1003020 year: 2013 publication-title: PLoS Comput. Biol. – volume: 21 start-page: 4 year: 2011 publication-title: Curr. Opin. Struct. Biol. – volume: 46 start-page: 175 year: 1992 publication-title: Am. Stat. – volume: 134 start-page: 174105 year: 2011 publication-title: J. Chem. Phys. – volume: 28 start-page: 129 year: 1982 publication-title: IEEE Trans. Inf. Theory – volume: 123 start-page: 204909 year: 2005 publication-title: J. Chem. Phys. – year: 2008 – volume: 49 start-page: 197 year: 2009 publication-title: Methods – volume: 108 start-page: 10184 year: 2011 publication-title: Proc. Natl. Acad. Sci. USA – volume: 112 start-page: 6057 year: 2008 publication-title: J. Phys. Chem. B – volume: 38 start-page: 293 year: 1985 publication-title: Theor. Comput. Sci. – volume: 344 start-page: 1492 year: 2014 publication-title: Science – volume: 6 start-page: 011009 year: 2016 publication-title: Phys. Rev. X – volume: 126 start-page: 155101 year: 2007 publication-title: J. Chem. Phys. – volume: 6 start-page: 787 year: 2010 publication-title: J. Chem. Theory Comput. – volume: 168 start-page: 595 year: 1983 publication-title: J. Mol. Biol. – volume: 36 start-page: 3336 year: 2009 publication-title: Expert Syst. Appl. – volume: 49 start-page: 687 year: 2016 publication-title: Acc. Chem. Res. – volume: 11 start-page: 3992 year: 2015 publication-title: J. Chem. Theory Comput. – year: 1987 – volume: 138 start-page: 174106 year: 2013 publication-title: J. Chem. Phys. – volume: 111 start-page: 7665 year: 2014 publication-title: Proc. Natl. Acad. Sci. USA – volume: 55 start-page: 383 year: 2004 publication-title: Proteins Struct. Funct. Bioinform. – volume: 6 start-page: e1001015 year: 2010 publication-title: PLoS Comput. Biol. – volume: 10 start-page: 180 year: 1985 publication-title: Math. Oper. Res. – volume: 65 start-page: 712 year: 2006 publication-title: Proteins Struct. Funct. Bioinform. – volume: 25 start-page: 135 year: 2014 publication-title: Curr. Opin. Struct. Biol. – volume: 10 start-page: e1003767 year: 2014 publication-title: PLoS Comput. Biol. – volume: 11 start-page: 5002 year: 2015 publication-title: J. Chem. Theory Comput. – volume: 132 start-page: 074110 year: 2010 publication-title: J. Chem. Phys. – volume: 34 start-page: 95 year: 2013 publication-title: J. Comput. Chem. – volume: 11 start-page: 17 year: 2015 publication-title: J. Chem. Theory Comput. – volume: 129 start-page: 064107 year: 2008 publication-title: J. Chem. Phys. – volume: 11 start-page: 5747 year: 2015 publication-title: J. Chem. Theory Comput. – volume: 12 start-page: 2426 year: 2016 publication-title: J. Chem. Theory Comput. – volume: 10 start-page: 5716 year: 2014 publication-title: J. Chem. Theory Comput. – volume: 51 start-page: 91 year: 2008 publication-title: Commun. ACM – volume: 111 start-page: 7665 year: 2014 ident: 10.1002/jcc.24664-BIB0029\|jcc24664-cit-0030 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1315751111 – ident: 10.1002/jcc.24664-BIB0043\|jcc24664-cit-0044 – volume: 7 start-page: 11244 year: 2016 ident: 10.1002/jcc.24664-BIB0035\|jcc24664-cit-0036 publication-title: Nat. Commun. doi: 10.1038/ncomms11244 – ident: 10.1002/jcc.24664-BIB0009\|jcc24664-cit-0010 – volume: 65 start-page: 712 year: 2006 ident: 10.1002/jcc.24664-BIB0045\|jcc24664-cit-0046 publication-title: Proteins Struct. Funct. Bioinform. doi: 10.1002/prot.21123 – volume: 9 start-page: e1003020 year: 2013 ident: 10.1002/jcc.24664-BIB0024\|jcc24664-cit-0025 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003020 – volume: 315 start-page: 39 year: 2000 ident: 10.1002/jcc.24664-BIB0047\|jcc24664-cit-0048 publication-title: Linear Algebra Appl. doi: 10.1016/S0024-3795(00)00095-1 – volume: 134 start-page: 174105 year: 2011 ident: 10.1002/jcc.24664-BIB0013\|jcc24664-cit-0014 publication-title: J. Chem. Phys. doi: 10.1063/1.3565032 – volume: 38 start-page: 293 year: 1985 ident: 10.1002/jcc.24664-BIB0004\|jcc24664-cit-0005 publication-title: Theor. Comput. Sci. doi: 10.1016/0304-3975(85)90224-5 – volume: 123 start-page: 204909 year: 2005 ident: 10.1002/jcc.24664-BIB0049\|jcc24664-cit-0050 publication-title: J. Chem. Phys. doi: 10.1063/1.2116947 – volume: 21 start-page: 4 year: 2011 ident: 10.1002/jcc.24664-BIB0021\|jcc24664-cit-0022 publication-title: Curr. Opin. Struct. Biol. doi: 10.1016/j.sbi.2010.10.006 – volume: 6 start-page: 011009 year: 2016 ident: 10.1002/jcc.24664-BIB0048\|jcc24664-cit-0049 publication-title: Phys. Rev. X – volume: 11 start-page: 3992 year: 2015 ident: 10.1002/jcc.24664-BIB0033\|jcc24664-cit-0034 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.5b00498 – volume: 132 start-page: 074110 year: 2010 ident: 10.1002/jcc.24664-BIB0038\|jcc24664-cit-0039 publication-title: J. Chem. Phys. doi: 10.1063/1.3301140 – volume: 12 start-page: 2426 year: 2016 ident: 10.1002/jcc.24664-BIB0040\|jcc24664-cit-0041 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.5b01233 – volume: 112 start-page: 6083 year: 2008 ident: 10.1002/jcc.24664-BIB0015\|jcc24664-cit-0016 publication-title: J. Phys. Chem. B doi: 10.1021/jp076377+ – volume: 129 start-page: 064107 year: 2008 ident: 10.1002/jcc.24664-BIB0016\|jcc24664-cit-0017 publication-title: J. Chem. Phys. doi: 10.1063/1.2959573 – volume: 25 start-page: 135 year: 2014 ident: 10.1002/jcc.24664-BIB0012\|jcc24664-cit-0013 publication-title: Curr. Opin. Struct. Biol. doi: 10.1016/j.sbi.2014.04.002 – volume: 126 start-page: 155101 year: 2007 ident: 10.1002/jcc.24664-BIB0014\|jcc24664-cit-0015 publication-title: J. Chem. Phys. doi: 10.1063/1.2714538 – volume: 36 start-page: 3336 year: 2009 ident: 10.1002/jcc.24664-BIB0010\|jcc24664-cit-0011 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2008.01.039 – volume: 46 start-page: 175 year: 1992 ident: 10.1002/jcc.24664-BIB0042\|jcc24664-cit-0043 publication-title: Am. Stat. doi: 10.1080/00031305.1992.10475879 – volume: 168 start-page: 595 year: 1983 ident: 10.1002/jcc.24664-BIB0002\|jcc24664-cit-0003 publication-title: J. Mol. Biol. doi: 10.1016/S0022-2836(83)80304-0 – volume: 344 start-page: 1492 year: 2014 ident: 10.1002/jcc.24664-BIB0039\|jcc24664-cit-0040 publication-title: Science doi: 10.1126/science.1242072 – volume: 136 start-page: 15 year: 1982 ident: 10.1002/jcc.24664-BIB0041\|jcc24664-cit-0042 publication-title: Anal. Chim. Acta doi: 10.1016/S0003-2670(01)95359-0 – volume: 52 start-page: 99 year: 2010 ident: 10.1002/jcc.24664-BIB0011\|jcc24664-cit-0012 publication-title: Methods doi: 10.1016/j.ymeth.2010.06.002 – volume: 10 start-page: 1739 year: 2014 ident: 10.1002/jcc.24664-BIB0027\|jcc24664-cit-0028 publication-title: J. Chem. Theory Comput. doi: 10.1021/ct4009156 – volume: 11 start-page: 5747 year: 2015 ident: 10.1002/jcc.24664-BIB0030\|jcc24664-cit-0031 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.5b00737 – volume: 11 start-page: e1004404 year: 2015 ident: 10.1002/jcc.24664-BIB0032\|jcc24664-cit-0033 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1004404 – volume: 51 start-page: 91 year: 2008 ident: 10.1002/jcc.24664-BIB0003\|jcc24664-cit-0004 publication-title: Commun. ACM doi: 10.1145/1364782.1364802 – volume: 11 start-page: 17 year: 2015 ident: 10.1002/jcc.24664-BIB0031\|jcc24664-cit-0032 publication-title: J. Chem. Theory Comput. doi: 10.1021/ct5007168 – volume-title: Finding Groups in Data: An Introduction to Cluster Analysis year: 2008 ident: 10.1002/jcc.24664-BIB0005\|jcc24664-cit-0006 – volume: 55 start-page: 383 year: 2004 ident: 10.1002/jcc.24664-BIB0046\|jcc24664-cit-0047 publication-title: Proteins Struct. Funct. Bioinform. doi: 10.1002/prot.20033 – volume: 10 start-page: 5716 year: 2014 ident: 10.1002/jcc.24664-BIB0025\|jcc24664-cit-0026 publication-title: J. Chem. Theory Comput. doi: 10.1021/ct500827g – volume: 34 start-page: 95 year: 2013 ident: 10.1002/jcc.24664-BIB0044\|jcc24664-cit-0045 publication-title: J. Comput. Chem. doi: 10.1002/jcc.23110 – volume: 112 start-page: 6057 year: 2008 ident: 10.1002/jcc.24664-BIB0017\|jcc24664-cit-0018 publication-title: J. Phys. Chem. B doi: 10.1021/jp0761665 – volume: 49 start-page: 687 year: 2016 ident: 10.1002/jcc.24664-BIB0036\|jcc24664-cit-0037 publication-title: Acc. Chem. Res. doi: 10.1021/acs.accounts.5b00536 – volume: 6 start-page: 787 year: 2010 ident: 10.1002/jcc.24664-BIB0050\|jcc24664-cit-0051 publication-title: J. Chem. Theory Comput. doi: 10.1021/ct900620b – volume: 108 start-page: 10184 year: 2011 ident: 10.1002/jcc.24664-BIB0022\|jcc24664-cit-0023 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.1103547108 – volume: 28 start-page: 129 year: 1982 ident: 10.1002/jcc.24664-BIB0008\|jcc24664-cit-0009 publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.1982.1056489 – ident: 10.1002/jcc.24664-BIB0037\|jcc24664-cit-0038 publication-title: Phys. Chem. Chem. Phys. – volume: 10 start-page: 180 year: 1985 ident: 10.1002/jcc.24664-BIB0006\|jcc24664-cit-0007 publication-title: Math. Oper. Res. doi: 10.1287/moor.10.2.180 – volume: 10 start-page: 2648 year: 2014 ident: 10.1002/jcc.24664-BIB0026\|jcc24664-cit-0027 publication-title: J. Chem. Theory Comput. doi: 10.1021/ct5002363 – ident: 10.1002/jcc.24664-BIB0007\|jcc24664-cit-0008 – volume: 138 start-page: 174106 year: 2013 ident: 10.1002/jcc.24664-BIB0023\|jcc24664-cit-0024 publication-title: J. Chem. Phys. doi: 10.1063/1.4802007 – volume: 49 start-page: 197 year: 2009 ident: 10.1002/jcc.24664-BIB0019\|jcc24664-cit-0020 publication-title: Methods doi: 10.1016/j.ymeth.2009.04.013 – volume: 268 start-page: 765 year: 1977 ident: 10.1002/jcc.24664-BIB0001\|jcc24664-cit-0002 publication-title: Nature doi: 10.1038/268765a0 – volume: 11 start-page: 5002 year: 2015 ident: 10.1002/jcc.24664-BIB0034\|jcc24664-cit-0035 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.5b00553 – volume: 106 start-page: 19765 year: 2009 ident: 10.1002/jcc.24664-BIB0018\|jcc24664-cit-0019 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0909088106 – volume: 10 start-page: e1003767 year: 2014 ident: 10.1002/jcc.24664-BIB0028\|jcc24664-cit-0029 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1003767 – volume: 6 start-page: e1001015 year: 2010 ident: 10.1002/jcc.24664-BIB0020\|jcc24664-cit-0021 publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1001015
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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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