Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes
Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its non...
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2024
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| _version_ | 1852024879267708928 |
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| author | Donghui Shao (20347008) |
| author2 | Zhiteng Zhang (2033161) Xuyang Liu (310954) Haohao Fu (1351521) Xueguang Shao (1271313) Wensheng Cai (1271310) |
| author2_role | author author author author author |
| author_facet | Donghui Shao (20347008) Zhiteng Zhang (2033161) Xuyang Liu (310954) Haohao Fu (1351521) Xueguang Shao (1271313) Wensheng Cai (1271310) |
| author_role | author |
| dc.creator.none.fl_str_mv | Donghui Shao (20347008) Zhiteng Zhang (2033161) Xuyang Liu (310954) Haohao Fu (1351521) Xueguang Shao (1271313) Wensheng Cai (1271310) |
| dc.date.none.fl_str_mv | 2024-11-27T15:06:47Z |
| dc.identifier.none.fl_str_mv | 10.1021/acs.jctc.4c01282.s003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/media/Screening_Fast-Mode_Motion_in_Collective_Variable_Discovery_for_Biochemical_Processes/27918870 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Genetics Biotechnology Sociology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified providing reliable insights negligible computational cost dimensionality reduction techniques creating markov models collective variable discovery protein simulation trajectories describing slow degrees screen fast motion slow motion fast motion protein dynamics screening fast mode motion wide range sampling calculations remarkable ability often struggle nonlinear successor lagged autoencoder frequency signals finding algorithms finding algorithm conformational changes biomolecular assemblies alanine dipeptide |
| dc.title.none.fl_str_mv | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| dc.type.none.fl_str_mv | Dataset Media info:eu-repo/semantics/publishedVersion dataset |
| description | Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a “free lunch” for CV extraction, applicable to a wide range of CV-finding algorithms. |
| eu_rights_str_mv | openAccess |
| id | Manara_ec175cf40ae5ff0b3e26dcbd8cfd64e9 |
| identifier_str_mv | 10.1021/acs.jctc.4c01282.s003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27918870 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical ProcessesDonghui Shao (20347008)Zhiteng Zhang (2033161)Xuyang Liu (310954)Haohao Fu (1351521)Xueguang Shao (1271313)Wensheng Cai (1271310)BiochemistryGeneticsBiotechnologySociologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedproviding reliable insightsnegligible computational costdimensionality reduction techniquescreating markov modelscollective variable discoveryprotein simulation trajectoriesdescribing slow degreesscreen fast motionslow motionfast motionprotein dynamicsscreening fastmode motionwide rangesampling calculationsremarkable abilityoften strugglenonlinear successorlagged autoencoderfrequency signalsfinding algorithmsfinding algorithmconformational changesbiomolecular assembliesalanine dipeptideCollective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a “free lunch” for CV extraction, applicable to a wide range of CV-finding algorithms.2024-11-27T15:06:47ZDatasetMediainfo:eu-repo/semantics/publishedVersiondataset10.1021/acs.jctc.4c01282.s003https://figshare.com/articles/media/Screening_Fast-Mode_Motion_in_Collective_Variable_Discovery_for_Biochemical_Processes/27918870CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279188702024-11-27T15:06:47Z |
| spellingShingle | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes Donghui Shao (20347008) Biochemistry Genetics Biotechnology Sociology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified providing reliable insights negligible computational cost dimensionality reduction techniques creating markov models collective variable discovery protein simulation trajectories describing slow degrees screen fast motion slow motion fast motion protein dynamics screening fast mode motion wide range sampling calculations remarkable ability often struggle nonlinear successor lagged autoencoder frequency signals finding algorithms finding algorithm conformational changes biomolecular assemblies alanine dipeptide |
| status_str | publishedVersion |
| title | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| title_full | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| title_fullStr | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| title_full_unstemmed | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| title_short | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| title_sort | Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes |
| topic | Biochemistry Genetics Biotechnology Sociology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified providing reliable insights negligible computational cost dimensionality reduction techniques creating markov models collective variable discovery protein simulation trajectories describing slow degrees screen fast motion slow motion fast motion protein dynamics screening fast mode motion wide range sampling calculations remarkable ability often struggle nonlinear successor lagged autoencoder frequency signals finding algorithms finding algorithm conformational changes biomolecular assemblies alanine dipeptide |