Amino Acid Sequence Autocorrelation Vectors and Ensembles of Bayesian-Regularized Genetic Neural Networks for Prediction of Conformational Stability of Human Lysozyme Mutants

2006 ◽  
Vol 46 (3) ◽  
pp. 1255-1268 ◽  
Author(s):  
Julio Caballero ◽  
Leyden Fernández ◽  
José Ignacio Abreu ◽  
Michael Fernández
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Conformational Stability ◽  
Human Lysozyme

Role of Amino Acid Residues at Turns in the Conformational Stability and Folding of Human Lysozyme†,‡

Biochemistry ◽  
2000 ◽  
Vol 39 (29) ◽  
pp. 8655-8665 ◽  
Author(s):  
Kazufumi Takano ◽  
Yuriko Yamagata ◽  
Katsuhide Yutani
Keyword(s):  
Amino Acid ◽  
Conformational Stability ◽  
Amino Acid Residues ◽  
Human Lysozyme

scholarly journals Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics

2020 ◽  
Author(s):  
Gabriele Orlando ◽  
Daniele Raimondi ◽  
Francesco Codice ◽  
Francesco Tabaro ◽  
Wim Vranken
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Amino Acid Sequence ◽  
Protein Dynamics ◽  
Recurrent Neural Networks ◽  
Backbone Dynamics ◽  
Evolutionary Information ◽  
Emergent Properties ◽  
Intrinsically Disordered ◽  
Disordered Protein

AbstractThe role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use only the protein amino acid sequence, without taking into account likely emergent properties that are sequence context dependent, such as protein backbone dynamics.The DisoMine method predicts protein disorder with recurrent neural networks not directly from the amino acid sequence, but instead from more generic predictions of key biophysical properties, here protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine compares well to 10 state of the art predictors, also if these use evolutionary information.DisoMine is freely available through an interactive webserver at http://bio2byte.com/disomine/

scholarly journals Protein Secondary Structure Prediction using Recurrent Neural Networks

2020 ◽  
Vol 8 (5) ◽  
pp. 660-663
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Recurrent Neural Networks ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure

In bioinformatics the prediction of the secondary structure of the protein from its primary amino acid sequence is very difficult, which has a huge impact on the field of science and medicine. The hardest part is how to learn the most effective and correct protein features to improve prediction. Here, we carry out a deep learning model to enhance structure prediction. The core achievement of this paper is a group of recurrent neural networks (RNNs) that can manage high-level relational features from a pair of input protein sequence and target protein sequences. This paper contrasts the different type of recurrent network in recurrent neural networks (RNNs). In addition, the emphasis is on more advanced systems which incorporate a gating utility is called long short term memory (LSTM) unit and the newly added gated recurrent unit (GRU). This recurrent units has been calculated on the basis of predicting protein secondary structure using an amino acid sequence. The dataset has been taken from a publicly available database server (RCSB), and this study shows that advanced recurrent units LSTM is better than GRU for a long protein sequence.

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