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
2021 ◽  
Author(s):  
Colin Swenson ◽  
Hershel H Lackey ◽  
Eric James Reece ◽  
Joel M. Harris ◽  
Jennifer Heemstra ◽  
...  

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...


Biochemistry ◽  
2001 ◽  
Vol 40 (29) ◽  
pp. 8444-8451 ◽  
Author(s):  
Naoki Sugimoto ◽  
Naonori Satoh ◽  
Kyohko Yasuda ◽  
Shu-ichi Nakano

2021 ◽  
Vol 23 (1) ◽  
pp. 219-228
Author(s):  
Nabanita Saikia ◽  
Mohamed Taha ◽  
Ravindra Pandey

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.


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<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>


Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole

<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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