On the Sensitivity of Peptide Nucleic Acid Duplex Formation and Crystal Dissolution to a Variation of Force-Field Parameters

2013 ◽  
Vol 10 (1) ◽  
pp. 391-400 ◽  
Author(s):  
Stephan J. Bachmann ◽  
Zhixiong Lin ◽  
Thorsten Stafforst ◽  
Wilfred F. van Gunsteren ◽  
Jožica Dolenc
Keyword(s):  
Nucleic Acid ◽  
Force Field ◽  
Peptide Nucleic Acid ◽  
Crystal Dissolution ◽  
Force Field Parameters ◽  
Duplex Formation

scholarly journals Evaluating the Effect of Ionic Strength on PNA:DNA Duplex Formation Kinetics

2021 ◽  
Author(s):  
Colin Swenson ◽  
Hershel H Lackey ◽  
Eric James Reece ◽  
Joel M. Harris ◽  
Jennifer Heemstra ◽  
...  
Keyword(s):  
Ionic Strength ◽  
Nucleic Acid ◽  
Peptide Nucleic Acid ◽  
Biological Applications ◽  
Base Pairing ◽  
Formation Kinetics ◽  
Acid Analog ◽  
Duplex Formation

Peptide nucleic acid (PNA) is a unique synthetic nucleic acid analog that has been adopted for use in many biological applications. These applications rely upon the robust Franklin-Watson-Crick base pairing...

Stabilization Factors Affecting Duplex Formation of Peptide Nucleic Acid with DNA†

Biochemistry ◽  
2001 ◽  
Vol 40 (29) ◽  
pp. 8444-8451 ◽  
Author(s):  
Naoki Sugimoto ◽  
Naonori Satoh ◽  
Kyohko Yasuda ◽  
Shu-ichi Nakano
Keyword(s):  
Nucleic Acid ◽  
Peptide Nucleic Acid ◽  
Factors Affecting ◽  
Duplex Formation

Molecular insights on the dynamic stability of peptide nucleic acid functionalized carbon and boron nitride nanotubes

2021 ◽  
Vol 23 (1) ◽  
pp. 219-228
Author(s):  
Nabanita Saikia ◽  
Mohamed Taha ◽  
Ravindra Pandey
Keyword(s):  
Nucleic Acid ◽  
Boron Nitride ◽  
Nucleic Acids ◽  
Dynamic Stability ◽  
Peptide Nucleic Acid ◽  
Rational Design ◽  
Peptide Nucleic Acids ◽  
Boron Nitride Nanotubes ◽  
Molecular Hybrids ◽  
Single Wall Nanotubes

The rational design of self-assembled nanobio-molecular hybrids of peptide nucleic acids with single-wall nanotubes rely on understanding how biomolecules recognize and mediate intermolecular interactions with the nanomaterial's surface.

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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