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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.25218
|2 doi
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|a pubmed24n0941.xml
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zhou, Hongyu
|e verfasserin
|4 aut
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| 245 |
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|a Recognition of protein allosteric states and residues
|b Machine learning approaches
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 18.09.2019
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|a Date Revised 10.12.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2018 Wiley Periodicals, Inc.
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|a Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with individual residues, hindering effective selection of key residues for mutagenesis studies. The machine learning models including decision tree (DT) and artificial neural network (ANN) models were applied to develop classification model for a cell signaling allosteric protein with two states showing extremely similar tertiary structures in both crystallographic structures and molecular dynamics simulations. Both DT and ANN models were developed with 75% and 80% of predicting accuracy, respectively. Good agreement between machine learning models and previous experimental as well as computational studies of the same protein validates this approach as an alternative way to analyze protein dynamics simulations and allostery. In addition, the difference of distributions of key features in two allosteric states also underlies the population shift hypothesis of dynamics-driven allostery model. © 2018 Wiley Periodicals, Inc
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a allostery
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| 650 |
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4 |
|a classification
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a molecular dynamics
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| 650 |
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|a protein
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| 650 |
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|a Proteins
|2 NLM
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| 700 |
1 |
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|a Dong, Zheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tao, Peng
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 39(2018), 20 vom: 30. Juli, Seite 1481-1490
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:39
|g year:2018
|g number:20
|g day:30
|g month:07
|g pages:1481-1490
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| 856 |
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|u http://dx.doi.org/10.1002/jcc.25218
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