Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Fold recognition programs align a probe protein sequence onto protein three-dimensional (3D) structure templates. The alignment beten the probe sequence and the most suitable template can be used to predict the 3D structure and often biological function of the probe. Here  present a new threading scoring function of protein sequence–structure compatibility. An artificial neural network model is trained to predict compatibility of amino acid side-chains with structural environments. Log-odds scores of predicted probabilities from this model can then be used to construct protein sequence–structure alignments. Results: Our model is tested on discrimination of native and decoy protein 3D structures. With a residue level structural description, its performance is comparable to those of pseudo-energy functions with atom level structural descriptions, better than the two functions with residue level structural descriptions. Availability: The C++ source code of our neural network model is available at http://mathbio.nimr.mrc.ac.uk/∼kxlin.
['Kuang Lin', 'Alex C. W. May', 'William R. Taylor']
I am writing a research paper on a idea Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility.
[{"from": "human", "value": "I am writing a research paper on a idea Threading using neural nEtwork (TUNE): the measure of protein sequence-structure compatibility."}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Fold recognition programs align a probe protein sequence onto protein three-dimensional (3D) structure templates. The alignment beten the probe sequence and the most suitable template can be used to predict the 3D structure and often biological function of the probe. Here  present a new threading scoring function of protein sequence\u2013structure compatibility. An artificial neural network model is trained to predict compatibility of amino acid side-chains with structural environments. Log-odds scores of predicted probabilities from this model can then be used to construct protein sequence\u2013structure alignments. Results: Our model is tested on discrimination of native and decoy protein 3D structures. With a residue level structural description, its performance is comparable to those of pseudo-energy functions with atom level structural descriptions, better than the two functions with residue level structural descriptions. Availability: The C++ source code of our neural network model is available at http://mathbio.nimr.mrc.ac.uk/\u223ckxlin."}]