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Amino Acid Sequence

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Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy.

Amino acids
Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is...

Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences.

Computational biology and chemistry
Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learnin...

Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.

Medical & biological engineering & computing
Advances in high-throughput techniques lead to evolving a large number of unknown protein sequences (UPS). Functional characterization of UPS is significant for the investigation of disease symptoms and drug repositioning. Protein subcellular localiz...

FALCON2: a web server for high-quality prediction of protein tertiary structures.

BMC bioinformatics
BACKGROUND: Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a ...

Rosetta:MSF:NN: Boosting performance of multi-state computational protein design with a neural network.

PloS one
Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a pr...

On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins.

Journal of molecular biology
Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no ...

HemoNet: Predicting hemolytic activity of peptides with integrated feature learning.

Journal of bioinformatics and computational biology
Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides...

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning.

Journal of bioinformatics and computational biology
DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver...

Accurate prediction of protein structures and interactions using a three-track neural network.

Science (New York, N.Y.)
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track ...