AIMC Topic: Amino Acid Sequence

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

Molecular diversity Oct 9, 2021
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...
Ligands Tubulin Support Vector Machine Amino Acid Sequence Algorithms
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Protein-protein interaction prediction based on ordinal regression and recurrent convolutional neural networks.

BMC bioinformatics Oct 8, 2021
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...
Neural Networks, Computer Proteins Humans Amino Acid Sequence Saccharomyces cerevisiae
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Protein Family Classification from Scratch: A CNN Based Deep Learning Approach.

IEEE/ACM transactions on computational biology and bioinformatics Oct 8, 2021
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...
Humans Algorithms Cluster Analysis Proteins Amino Acid Sequence Deep Learning Computational Biology Sequence Analysis, Protein
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AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
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...
Amino Acid Sequence Sequence Analysis, Protein Peptides Drug Discovery Models, Statistical Anti-Inflammatory Agents Computational Biology Machine Learning Algorithms
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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 Oct 5, 2021
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 ...
Humans Neural Networks, Computer Neoplasms Proteins Amino Acid Sequence Protein Interaction Mapping Deep Learning Computational Biology
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ECNet is an evolutionary context-integrated deep learning framework for protein engineering.

Nature communications Sep 30, 2021
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....
Deep Learning High-Throughput Screening Assays beta-Lactam Resistance Protein Engineering Genetic Fitness Mutation Datasets as Topic Evolution, Molecular Sequence Homology, Amino Acid Amino Acid Sequence beta-Lactamases
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Mapping the glycosyltransferase fold landscape using interpretable deep learning.

Nature communications Sep 27, 2021
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...
Protein Folding Sequence Alignment Glycosyltransferases Protein Structure, Tertiary Datasets as Topic Deep Learning Protein Structure, Secondary Computational Biology Databases, Genetic Glycosylation 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 Sep 25, 2021
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...
Sequence Alignment Receptors, G-Protein-Coupled Machine Learning Amino Acid Sequence Algorithms Multigene Family Evolution, Molecular
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Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences.

Computational biology and chemistry Sep 24, 2021
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...
Proteins Neural Networks, Computer Amino Acid Sequence Computational Biology
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Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.

Medical & biological engineering & computing Sep 20, 2021
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...
Bayes Theorem Amino Acid Sequence Neural Networks, Computer Support Vector Machine Proteins Algorithms
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