Investigating Information Dynamics in Living Systems through the Structure and Function of Enzymes

Persistent Link:
http://hdl.handle.net/10150/614945
Title:
Investigating Information Dynamics in Living Systems through the Structure and Function of Enzymes
Author:
Gatenby, Robert; Frieden, B. Roy
Affiliation:
Univ Arizona, Coll Opt Sci
Issue Date:
2016-05-05
Publisher:
Public Library of Science
Citation:
Investigating Information Dynamics in Living Systems through the Structure and Function of Enzymes 2016, 11 (5):e0154867 PLOS ONE
Journal:
PLOS ONE
Rights:
© 2016 Gatenby, Frieden. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Collection Information:
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
Abstract:
Enzymes are proteins that accelerate intracellular chemical reactions often by factors of 10(5) - 10(12)s(-1). We propose the structure and function of enzymes represent the thermodynamic expression of heritable information encoded in DNA with post-translational modifications that reflect intra- and extra-cellular environmental inputs. The 3 dimensional shape of the protein, determined by the genetically-specified amino acid sequence and post translational modifications, permits geometric interactions with substrate molecules traditionally described by the key-lock best fit model. Here we apply Kullback-Leibler (K-L) divergence as metric of this geometric "fit" and the information content of the interactions. When the K-L 'distance' between interspersed substrate p(n) and enzyme r(n) positions is minimized, the information state, reaction probability, and reaction rate are maximized. The latter obeys the Arrhenius equation, which we show can be derived from the geometrical principle of minimum K-L distance. The derivation is first limited to optimum substrate positions for fixed sets of enzyme positions. However, maximally improving the key/lock fit, called 'induced fit,' requires both sets of positions to be varied optimally. We demonstrate this permits and is maximally efficient if the key and lock particles p(n), r(n) are quantum entangled because the level of entanglement obeys the same minimized value of the Kullback-Leibler distance that occurs when all p(n) approximate to r(n). This implies interchanges p(n) reversible arrow br(n) randomly taking place during a reaction successively improves key/lock fits, reducing the activation energy E-a and increasing the reaction rate k. Our results demonstrate the summation of heritable and environmental information that determines the enzyme spatial configuration, by decreasing the K-L divergence, is converted to thermodynamic work by reducing Ea and increasing k of intracellular reactions. Macroscopically, enzyme information increases the order in living systems, similar to the Maxwell demon gedanken, by selectively accelerating specific reaction thus generating both spatial and temporal concentration gradients.
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0154867
Version:
Final published version
Additional Links:
http://dx.plos.org/10.1371/journal.pone.0154867

Full metadata record

DC FieldValue Language
dc.contributor.authorGatenby, Roberten
dc.contributor.authorFrieden, B. Royen
dc.date.accessioned2016-06-28T23:54:53Z-
dc.date.available2016-06-28T23:54:53Z-
dc.date.issued2016-05-05-
dc.identifier.citationInvestigating Information Dynamics in Living Systems through the Structure and Function of Enzymes 2016, 11 (5):e0154867 PLOS ONEen
dc.identifier.issn1932-6203-
dc.identifier.doi10.1371/journal.pone.0154867-
dc.identifier.urihttp://hdl.handle.net/10150/614945-
dc.description.abstractEnzymes are proteins that accelerate intracellular chemical reactions often by factors of 10(5) - 10(12)s(-1). We propose the structure and function of enzymes represent the thermodynamic expression of heritable information encoded in DNA with post-translational modifications that reflect intra- and extra-cellular environmental inputs. The 3 dimensional shape of the protein, determined by the genetically-specified amino acid sequence and post translational modifications, permits geometric interactions with substrate molecules traditionally described by the key-lock best fit model. Here we apply Kullback-Leibler (K-L) divergence as metric of this geometric "fit" and the information content of the interactions. When the K-L 'distance' between interspersed substrate p(n) and enzyme r(n) positions is minimized, the information state, reaction probability, and reaction rate are maximized. The latter obeys the Arrhenius equation, which we show can be derived from the geometrical principle of minimum K-L distance. The derivation is first limited to optimum substrate positions for fixed sets of enzyme positions. However, maximally improving the key/lock fit, called 'induced fit,' requires both sets of positions to be varied optimally. We demonstrate this permits and is maximally efficient if the key and lock particles p(n), r(n) are quantum entangled because the level of entanglement obeys the same minimized value of the Kullback-Leibler distance that occurs when all p(n) approximate to r(n). This implies interchanges p(n) reversible arrow br(n) randomly taking place during a reaction successively improves key/lock fits, reducing the activation energy E-a and increasing the reaction rate k. Our results demonstrate the summation of heritable and environmental information that determines the enzyme spatial configuration, by decreasing the K-L divergence, is converted to thermodynamic work by reducing Ea and increasing k of intracellular reactions. Macroscopically, enzyme information increases the order in living systems, similar to the Maxwell demon gedanken, by selectively accelerating specific reaction thus generating both spatial and temporal concentration gradients.en
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0154867en
dc.rights© 2016 Gatenby, Frieden. This is an open access article distributed under the terms of the Creative Commons Attribution License.en
dc.titleInvestigating Information Dynamics in Living Systems through the Structure and Function of Enzymesen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Coll Opt Scien
dc.identifier.journalPLOS ONEen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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