Restriction enzymes use a 24 dimensional coding space to recognize 6 base long DNA sequences

Schneider, Thomas D.; Jejjala, Vishnu

doi:10.1371/journal.pone.0222419

Cited by 4 publications

(6 citation statements)

References 97 publications

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“…Taking the informational metaphor one step further, risking to take the sword by the wrong end, we may ask: What are the characteristics of the “machine” that “reads” and “processes” this “coded” biological “information”? For this we will mostly rely on the molecular information theory developed by Thomas D. Schneider − originally developed for protein–DNA interactions and further applied to other types of molecular machines . Here we will extend it to explore how it can be applied to protein sequences, structures, energetics, and evolution.…”

Section: Introductionmentioning

confidence: 99%

Molecular Information Theory Meets Protein Folding

et al. 2022

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We propose an application of molecular information theory to analyze the folding of single domain proteins. We analyze results from various areas of protein science, such as sequence-based potentials, reduced amino acid alphabets, backbone configurational entropy, secondary structure content, residue burial layers, and mutational studies of protein stability changes. We found that the average information contained in the sequences of evolved proteins is very close to the average information needed to specify a fold ∼2.2 ± 0.3 bits/(site·operation). The effective alphabet size in evolved proteins equals the effective number of conformations of a residue in the compact unfolded state at around 5. We calculated an energy-to-information conversion efficiency upon folding of around 50%, lower than the theoretical limit of 70%, but much higher than human-built macroscopic machines. We propose a simple mapping between molecular information theory and energy landscape theory and explore the connections between sequence evolution, configurational entropy, and the energetics of protein folding.

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Section: Introductionmentioning

confidence: 99%

Molecular Information Theory Meets Protein Folding

et al. 2022

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“…For example, we have recently shown how to determine the dimensionality of DNA binding proteins. Surprisingly EcoRI and other 6-base cutting restriction enzymes function in 24 dimensional space and probably use the famous Leech lattice for their sphere packing [23].…”

Section: High Dimensionality and Noise Lead To Hyperspheresmentioning

confidence: 99%

“…The purpose of this paper is to generalize the efficiency mathematics for application across biology. The key idea that many biological systems approach ln2 � 70% efficiency, and its explanation, is already published [19] and has been used to explain why restriction enzymes frequently have binding sites that are six bases long: they likely use the 24 dimensional Leech lattice for coding [23]. Extensive data will be presented elsewhere, so the 70% observation does not warrant further support here.…”

Section: Introductionmentioning

confidence: 97%

Generalizing the isothermal efficiency by using Gaussian distributions

Schneider

2023

PLoS ONE

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Unlike the Carnot heat engine efficiency published in 1824, an isothermal efficiency derived from thermodynamics and information theory can be applied to biological systems. The original approach by Pierce and Cutler in 1959 to derive the isothermal efficiency equation came from Shannon’s channel capacity of 1949 and from Felker’s 1952 determination of the minimum energy dissipation needed to gain a bit. In 1991 and 2010 Schneider showed how the isothermal efficiency equation can be applied to molecular machines and that this can be used to explain why several molecular machines are 70% efficient. Surprisingly, some macroscopic biological systems, such as whole ecosystems, are also 70% efficient but it is hard to see how this could be explained by a thermodynamic and molecular theory. The thesis of this paper is that the isothermal efficiency can be derived without using thermodynamics by starting from a set of independent Gaussian distributions. This novel derivation generalizes the isothermal efficiency equation for use at all levels of biology, from molecules to ecosystems.

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“…The copyright holder for this preprint this version posted December 14, 2022. ; https://doi.org/10.1101/2022.12.12.520049 doi: bioRxiv preprint enzymes frequently have binding sites that are six bases long: they likely use the 24 dimensional Leech lattice for coding [23]. Extensive data will be presented elsewhere, so the 70% observation does not warrant further support here.…”

mentioning

confidence: 96%

“…The purpose of this paper is to generalize the efficiency mathematics for application across biology. The key idea that many biological systems approach ln 2 ≈ 70% efficiency, and its explanation, is already published [19] and has been used to explain why restriction enzymes frequently have binding sites that are six bases long: they likely use the 24 dimensional Leech lattice for coding [23]. Extensive data will be presented elsewhere, so the 70% observation does not warrant further support here.…”

mentioning

confidence: 96%

Generalizing the isothermal efficiency by using Gaussian distributions

Schneider

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Unlike the Carnot heat engine efficiency published in 1824, an isothermal efficiency derived from thermodynamics and information theory can be applied to biological systems. The original approach by Pierce and Cutler in 1959 to derive the isothermal efficiency equation came from Shannon's channel capacity of 1949 and from Felker's 1952 determination of the minimum energy dissipation needed to gain a bit. In 1991 and 2010 Schneider showed how the isothermal efficiency equation can be applied to molecular machines and that this can be used to explain why several molecular machines are 70% efficient. Surprisingly, some macroscopic biological systems, such as whole ecosystems, are also 70% efficient but it is hard to see how this could be explained by a thermodynamic and molecular theory. The thesis of this paper is that the isothermal efficiency can be derived without using thermodynamics by starting from a set of independent Gaussian distributions. This novel derivation generalizes the isothermal efficiency equation for use at all levels of biology, from molecules to ecosystems.

show abstract

Restriction enzymes use a 24 dimensional coding space to recognize 6 base long DNA sequences

Cited by 4 publications

References 97 publications

Molecular Information Theory Meets Protein Folding

Molecular Information Theory Meets Protein Folding

Generalizing the isothermal efficiency by using Gaussian distributions

Generalizing the isothermal efficiency by using Gaussian distributions

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