Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics

2015 ◽  
Vol 142 (10) ◽  
pp. 105101 ◽  
Author(s):  
Rachael A. Mansbach ◽  
Andrew L. Ferguson
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Single Molecule ◽  
Structure And Dynamics ◽  
Energy Surfaces ◽  
The Impact

Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables

2020 ◽  
Vol 124 (18) ◽  
pp. 3647-3660 ◽  
Author(s):  
Joseph R. Cendagorta ◽  
Jocelyn Tolpin ◽  
Elia Schneider ◽  
Robert Q. Topper ◽  
Mark E. Tuckerman
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
High Dimensional ◽  
Learning Models ◽  
Energy Surfaces ◽  
Machine Learning Models

scholarly journals Accurate Prediction of B-form/A-form DNA Conformation Propensity from Primary Sequence: A Machine Learning and Free energy Handshake

2020 ◽  
Author(s):  
Abhijit Gupta ◽  
Mandar Kulkarni ◽  
Arnab Mukherjee
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Genomic Sequence ◽  
Dna Conformation ◽  
Biological Functions ◽  
Primary Sequence ◽  
Novel Approach ◽  
The Impact ◽  
Made In ◽  
Polymorphic Nature

<div> <div> <div> <p>DNA carries the genetic code of life. Different conformations of DNA are associated with various biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. Although a few efforts were made in this regard, currently there exists no method that can accurately predict the conformation of right- handed DNA solely from the sequence. In this study, we present a novel approach based on machine learning that predicts A-DNA and B-DNA conformational propensities of a sequence with high accuracy (~95%). In addition, we show that the impact of the dinucleotide steps in determining the conformation agrees qualitatively with the free energy cost for A-DNA formation in water. This method enables us to examine the genomic sequence to understand the prospective biological roles played by the A-form of DNA. </p> </div> </div> </div>

scholarly journals REFOLDING OF HOMOPOLYMER UNDER QUENCHED FORCE

2018 ◽  
Vol 55 (6A) ◽  
pp. 1
Author(s):  
Maksim Kouza
Keyword(s):  
Free Energy ◽  
Single Molecule ◽  
Energy Landscape ◽  
Coarse Grain ◽  
Free Energy Landscape ◽  
Single Molecule Force Spectroscopy ◽  
Denatured State ◽  
Dna And Rna ◽  
Go Model ◽  
The Impact

Recently single molecule force spectroscopy has become an useful tool to study protein, DNA and RNA. However, very little attention was paid to homopolymer which plays an important role in many domains of science. In this paper we make the first attempt to decipher the free energy landscape of homopolymer using the external force as reaction coordinate. The impact of the quenched force on the free energy landscape was studied using simplified coarse-grain Go model. Similar to protein, we have obtained a clear switch from the thermal regime to force-driven regime. The distance between the denatured state and transition state in the temperature-driven regime is smaller than in the force-driven one.  Having a rugged free energy landscape without a pronounced funnel the homopolymer folding is much slower than that of protein making study of homopolymer very time consuming.

scholarly journals Accurate Prediction of B-form/A-form DNA Conformation Propensity from Primary Sequence: A Machine Learning and Free energy Handshake

2020 ◽  
Author(s):  
Abhijit Gupta ◽  
Mandar Kulkarni ◽  
Arnab Mukherjee
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Genomic Sequence ◽  
Dna Conformation ◽  
Biological Functions ◽  
Primary Sequence ◽  
Novel Approach ◽  
The Impact ◽  
Made In ◽  
Polymorphic Nature

<div> <div> <p>DNA carries the genetic code of life. Different conformations of DNA are associated with various biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. Although a few efforts were made in this regard, currently there exists no method that can accurately predict the conformation of right-handed DNA solely from the sequence. In this study, we present a novel approach based on machine learning that predicts A-DNA and B-DNA conformational propensities of a sequence with high accuracy (~<a>93</a>%). In addition, we show that the impact of the dinucleotide steps in determining the conformation agrees qualitatively with the free energy cost for A-DNA formation in water. We are hopeful that our methodology can be employed on segments of the genomic sequence to understand the prospective biological roles played by the A-form of DNA.</p><p> </p><div> <br><div><div> </div> </div> </div> </div> </div>

scholarly journals Accurate Prediction of B-form/A-form DNA Conformation Propensity from Primary Sequence: A Machine Learning and Free energy Handshake

2020 ◽  
Author(s):  
Abhijit Gupta ◽  
Mandar Kulkarni ◽  
Arnab Mukherjee
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Genomic Sequence ◽  
Dna Conformation ◽  
Biological Functions ◽  
Primary Sequence ◽  
Novel Approach ◽  
The Impact ◽  
Made In ◽  
Polymorphic Nature

<div> <div> <p>DNA carries the genetic code of life. Different conformations of DNA are associated with various biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. Although a few efforts were made in this regard, currently there exists no method that can accurately predict the conformation of right-handed DNA solely from the sequence. In this study, we present a novel approach based on machine learning that predicts A-DNA and B-DNA conformational propensities of a sequence with high accuracy (~<a>93</a>%). In addition, we show that the impact of the dinucleotide steps in determining the conformation agrees qualitatively with the free energy cost for A-DNA formation in water. We are hopeful that our methodology can be employed on segments of the genomic sequence to understand the prospective biological roles played by the A-form of DNA.</p><p> </p><div> <br><div><div> </div> </div> </div> </div> </div>

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