scholarly journals Accurate prediction of B-form/A-form DNA conformation propensity from primary sequence: A machine learning and free energy handshake

Patterns ◽  
2021 ◽  
pp. 100329
Author(s):  
Abhijit Gupta ◽  
Mandar Kulkarni ◽  
Arnab Mukherjee
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Accurate Prediction ◽  
Dna Conformation ◽  
Primary Sequence

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 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>

scholarly journals Accurate Prediction of B-form/A-form DNA Conformation 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 Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

2021 ◽  
Author(s):  
Kjell Jorner ◽  
Tore Brinck ◽  
Per-Ola Norrby ◽  
David Buttar
Keyword(s):  
Machine Learning ◽  
Activation Energies ◽  
Accurate Prediction ◽  
Nucleophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Mechanistic Modelling

Hybrid reactivity models, combining mechanistic calculations and machine learning with descriptors, are used to predict barriers for nucleophilic aromatic substitution.

scholarly journals A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules

2020 ◽  
Author(s):  
Jenke Scheen ◽  
Wilson Wu ◽  
Antonia S. J. S. Mey ◽  
Paolo Tosco ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Learning Approaches ◽  
Free Energies ◽  
Training Set ◽  
Set Size ◽  
Correction Terms ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations, and to flag molecules which will benefit the most from bespoke forcefield parameterisation efforts.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine

2018 ◽  
Vol 45 (5) ◽  
pp. 2243-2251 ◽  
Author(s):  
Baozhou Sun ◽  
Dao Lam ◽  
Deshan Yang ◽  
Kevin Grantham ◽  
Tiezhi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach

DFT-Machine Learning Approach for Accurate Prediction of pKa

2021 ◽  
Author(s):  
Robin Lawler ◽  
Yao-Hao Liu ◽  
Nessa Majaya ◽  
Omar Allam ◽  
Hyunchul Ju ◽  
...  
Keyword(s):  
Machine Learning ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Machine Learning Approach
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