Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.#R##N##R##N#Results:  report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations beten the native and the predicted with L-pfSeqGNM B-factors and beten the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein–protein interactions and protein functions.#R##N##R##N#Contact: moc.621@auhzorez#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Hua Zhang', 'Lukasz A. Kurgan']
I am writing a research paper on a idea Sequence-based Gaussian network model for protein dynamics
[{"from": "human", "value": "I am writing a research paper on a idea Sequence-based Gaussian network model for protein dynamics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Gaussian network model (GNM) is widely adopted to analyze and understand protein dynamics, function and conformational changes. The existing GNM-based approaches require atomic coordinates of the corresponding protein and cannot be used when only the sequence is known.#R##N##R##N#Results:  report, first of its kind, GNM model that allows modeling using the sequence. Our linear regression-based, parameter-free, sequence-derived GNM (L-pfSeqGNM) uses contact maps predicted from the sequence and models local, in the sequence, contact neighborhoods with the linear regression. Empirical benchmarking shows relatively high correlations beten the native and the predicted with L-pfSeqGNM B-factors and beten the cross-correlations of residue fluctuations derived from the structure- and the sequence-based GNM models. Our results demonstrate that L-pfSeqGNM is an attractive platform to explore protein dynamics. In contrast to the highly used GNMs that require protein structures that number in thousands, our model can be used to study motions for the millions of the readily available sequences, which finds applications in modeling conformational changes, protein\u2013protein interactions and protein functions.#R##N##R##N#Contact: moc.621@auhzorez#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]