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Amino Acid Sequence

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A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2.

Computational and mathematical methods in medicine
Modeling antigenic variation in influenza (flu) virus A H3N2 using amino acid sequences is a promising approach for improving the prediction accuracy of immune efficacy of vaccines and increasing the efficiency of vaccine screening. Antigenic drift a...

Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks.

Interdisciplinary sciences, computational life sciences
The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-int...

Genetic variant effect prediction by supervised nonnegative matrix tri-factorization.

Molecular omics
Discriminating between deleterious and neutral mutations among numerous non-synonymous single nucleotide variants (nsSNVs) that may be observed through whole exome sequencing (WES) is considered a great challenge. In this regard, many machine learnin...

Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Molecular diversity
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance a...

Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks.

BMC bioinformatics
BACKGROUND: Protein protein interactions (PPIs) are essential to most of the biological processes. The prediction of PPIs is beneficial to the understanding of protein functions and thus is helpful to pathological analysis, disease diagnosis and drug...

Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics
Next-generation sequencing techniques provide us with an opportunity for generating sequenced proteins and identifying the biological families and functions of these proteins. However, compared with identified proteins, uncharacterized proteins consi...

AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.

IEEE/ACM transactions on computational biology and bioinformatics
Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid seque...

WinBinVec: Cancer-Associated Protein-Protein Interaction Extraction and Identification of 20 Various Cancer Types and Metastasis Using Different Deep Learning Models.

IEEE journal of biomedical and health informatics
Biophysical protein-protein interactions perform dominant roles in the initiation and progression of many cancer-related pathways. A protein-protein interaction might play different roles in diverse cancer types. Hence, prioritizing the PPIs in each ...

ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Nature communications
Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited....

Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Nature communications
Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversifi...