scholarly journals Peer Review #2 of "Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go (v0.1)"

2019 ◽  
Keyword(s):  
Peer Review ◽  
Chemical Shifts ◽  
13C Chemical Shifts ◽  
Rna Backbone

scholarly journals Classification of RNA backbone conformation into rotamers using 13C′ chemical shifts: How far we can go?

2019 ◽  
Author(s):  
A. A. Icazatti ◽  
J.M. Loyola ◽  
I. Szleifer ◽  
J.A. Vila ◽  
O. A. Martin
Keyword(s):  
Chemical Shifts ◽  
Conformational Space ◽  
Phosphate Backbone ◽  
Ribose Phosphate ◽  
Conformational Preferences ◽  
Backbone Conformation ◽  
Rna Backbone ◽  
Rna Backbone Conformation ◽  
Torsional Angles

ABSTRACTThe conformational space of the ribose–phosphate backbone is very complex as is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences 46 rotamers have been defined in terms of the these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the use of experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

scholarly journals Classification of RNA backbone conformations into rotamers using 13C′ chemical shifts: exploring how far we can go

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7904 ◽  
Author(s):  
Alejandro A. Icazatti ◽  
Juan M. Loyola ◽  
Igal Szleifer ◽  
Jorge A. Vila ◽  
Osvaldo A. Martin
Keyword(s):  
Machine Learning ◽  
Chemical Shifts ◽  
Conformational Space ◽  
Machine Learning Methods ◽  
Phosphate Backbone ◽  
Ribose Phosphate ◽  
Conformational Preferences ◽  
Rna Backbone ◽  
Torsional Angles

The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

scholarly journals Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

2021 ◽  
Author(s):  
Yanfei Guan ◽  
S. V. Shree Sowndarya ◽  
Liliana C. Gallegos ◽  
Peter C. St. John ◽  
Robert S. Paton
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
13C Chemical Shifts ◽  
Time Prediction ◽  
Nmr Data ◽  
Proton Chemical Shifts

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

Introduction of a new AAC symbol classification system: the multidimensional quaternary symbol continuum (MQSC)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eliada Pampoulou ◽  
Donald R. Fuller
Keyword(s):  
Peer Review ◽  
Classification System ◽  
Augmentative And Alternative Communication ◽  
Social Issues ◽  
Content Type ◽  
Design Attributes ◽  
Wide Range ◽  
Current Classification ◽  
Symbol Classification

PurposeWhen the augmentative and alternative communication (ACC) model (Lloyd et al., 1990) was proposed, these components of symbols were not considered, nor were they contemplated when superordinate (Lloyd and Fuller, 1986) and subordinate levels (Fuller et al., 1992) of AAC symbol taxonomy were developed. The purpose of this paper is to revisit the ACC model and propose a new symbol classification system called multidimensional quaternary symbol continuum (MQSC)Design/methodology/approachThe field of AAC is evolving at a rapid rate in terms of its clinical, social, research and theoretical underpinnings. Advances in assessment and intervention methods, technology and social issues are all responsible to some degree for the significant changes that have occurred in the field of AAC over the last 30 years. For example, the number of aided symbol collections has increased almost exponentially over the past couple of decades. The proliferation of such a large variety of symbol collections represents a wide range of design attributes, physical attributes and linguistic characteristics for aided symbols and design attributes and linguistic characteristics for unaided symbols.FindingsTherefore, it may be time to revisit the AAC model and more specifically, one of its transmission processes referred to as the means to represent.Originality/valueThe focus of this theoretical paper then, is on the current classification of symbols, issues with respect to the current classification of symbols in terms of ambiguity of terminology and the evolution of symbols, and a proposal for a new means of classifying the means to represent.Peer reviewThe peer review history for this article is available at: https://publons.com/publon10.1108/JET-04-2021-0024

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