AIMC Topic: Sequence Analysis, Protein

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Prediction of antioxidant proteins using hybrid feature representation method and random forest.

Genomics Aug 17, 2020
Natural antioxidant proteins are mainly found in plants and animals, which interact to eliminate excessive free radicals and protect cells and DNA from damage, prevent and treat some diseases. Therefore, accurate identification of antioxidant protein...
Machine Learning Antioxidants Proteins Sequence Analysis, Protein
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cnnAlpha: Protein disordered regions prediction by reduced amino acid alphabets and convolutional neural networks.

Proteins Aug 7, 2020
Intrinsically disordered regions (IDR) play an important role in key biological processes and are closely related to human diseases. IDRs have great potential to serve as targets for drug discovery, most notably in disordered binding regions. Accurat...
ROC Curve Computational Biology Area Under Curve Humans Neural Networks, Computer Machine Learning Datasets as Topic Intrinsically Disordered Proteins Sequence Analysis, Protein Multifactor Dimensionality Reduction Benchmarking Amino Acid Sequence Data Mining
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Machine-learning approach expands the repertoire of anti-CRISPR protein families.

Nature communications Jul 29, 2020
The CRISPR-Cas are adaptive bacterial and archaeal immunity systems that have been harnessed for the development of powerful genome editing and engineering tools. In the incessant host-parasite arms race, viruses evolved multiple anti-defense mechani...
CRISPR-Associated Protein 9 Host-Parasite Interactions Gene Editing Archaea Bacteriophages Viral Proteins Sequence Homology, Amino Acid Sequence Analysis, Protein Computational Biology CRISPR-Cas Systems Datasets as Topic Machine Learning Bacteria
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Protein-Protein Interactions Efficiently Modeled by Residue Cluster Classes.

International journal of molecular sciences Jul 6, 2020
Predicting protein-protein interactions (PPI) represents an important challenge in structural bioinformatics. Current computational methods display different degrees of accuracy when predicting these interactions. Different factors were proposed to h...
Machine Learning Algorithms Computational Biology Artificial Intelligence Protein Interaction Mapping Sequence Analysis, Protein Cluster Analysis Proteins Databases, Protein
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HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

Scientific reports Jul 2, 2020
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and f...
Humans Sequence Analysis, Protein Hemolytic Agents Computer Simulation Antimicrobial Cationic Peptides Hemolysis Neural Networks, Computer Machine Learning Software
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Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.

Journal of integrative bioinformatics Jun 29, 2020
To date, many proteins generated by large-scale genome sequencing projects are still uncharacterized and subject to intensive investigations by both experimental and computational means. Knowledge of protein subcellular localization (SCL) is of key i...
Databases, Protein Bacterial Proteins Gram-Negative Bacteria Gram-Positive Bacteria Molecular Sequence Annotation Gene Ontology Machine Learning Gene Expression Profiling Computational Biology Sequence Alignment Sequence Analysis, Protein Subcellular Fractions
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A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences.

Cell systems Jun 25, 2020
Engineering gene and protein sequences with defined functional properties is a major goal of synthetic biology. Deep neural network models, together with gradient ascent-style optimization, show promise for sequence design. The generated sequences ca...
Neural Networks, Computer DNA Sequence Analysis, Protein Humans
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Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation.

Journal of computer-aided molecular design Jun 16, 2020
Phage virion protein (PVP) perforate the host cell membrane and eventually culminates in cell rupture thereby releasing replicated phages. The accurate identification of PVP is thus a crucial step towards improving our understanding of the biological...
Sequence Analysis, Protein Algorithms Computational Biology Support Vector Machine Bacteriophages Viral Proteins Virion Humans Machine Learning Software
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QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding.

PloS one Apr 29, 2020
Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. However, the existing...
Protein Structure, Quaternary Support Vector Machine Proteins Databases, Protein Protein Domains Viral Proteins Sequence Analysis, Protein Algorithms Animals Computational Biology Humans Machine Learning Software
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ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning.

PLoS computational biology Apr 27, 2020
Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of ant...
Software Algorithms Computational Biology Humans Machine Learning Matrix Metalloproteinase Inhibitors Sequence Analysis, Protein Databases, Protein Antibodies Matrix Metalloproteinases
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