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Computational Prediction of Intrinsically Disordered Proteins Based on Protein Sequences and Convolutional Neural Networks.

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January 2022

School of Computer Science and Technology, Tiangong University, Tianjin 300387, China.

Intrinsically disordered proteins (IDPs) possess at least one region that lacks a single stable structure in vivo, which makes them play an important role in a variety of biological functions. We propose a prediction method for IDPs based on convolutional neural networks (CNNs) and feature selection. The combination of sequence and evolutionary properties is used to describe the differences between disordered and ordered regions.

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Prog Mol Biol Transl Sci

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Institute of Protein Research, Russian Academy of Sciences, Pushchino, Moscow Region, Russia. Electronic address:

Directed stabilization of globular proteins via substitution of a minimal number of amino acid residues is one of the most complicated experimental tasks. In this work, we have successfully used algorithms for the evaluation of intrinsic disorder predisposition (such as PONDR® FIT and IsUnstruct) as tools for searching for the weakened regions in structured globular proteins. We have shown that the weakened regions found by these programs as regions with highest levels of predicted intrinsic disorder predisposition are a suitable target for introduction of stabilizing mutations.

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In this study, we have used an approach that allows us to determine in what region of the polypeptide chain of protein it is required to insert a disulphide bond in order to stabilize it. In our previous paper [Melnik et al., JBSD.

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Institute of Protein Research, Russian Academy of Sciences, Pushchino 142290, Moscow Region, Russia.

Disordered regions play important roles in protein adaptation to challenging environmental conditions. Flexible and disordered residues have the highest propensities to alter the protein packing. Therefore, identification of disordered/flexible regions is important for structural and functional analysis of proteins.

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