AIMC Topic: Sequence Analysis, Protein

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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...
Protein Interaction Mapping Sequence Analysis, Protein Cluster Analysis Proteins Databases, Protein Algorithms Computational Biology Artificial Intelligence Machine Learning
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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 Neural Networks, Computer Antimicrobial Cationic Peptides Hemolysis Sequence Analysis, Protein Hemolytic Agents Machine Learning Software Computer Simulation
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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...
Gene Expression Profiling Computational Biology Databases, Protein Bacterial Proteins Machine Learning Gene Ontology Sequence Alignment Sequence Analysis, Protein Subcellular Fractions Gram-Negative Bacteria Gram-Positive Bacteria Molecular Sequence Annotation
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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...
DNA Sequence Analysis, Protein Humans Neural Networks, Computer
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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...
Viral Proteins Virion Sequence Analysis, Protein Algorithms Humans Computational Biology Support Vector Machine Machine Learning Bacteriophages 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...
Support Vector Machine Algorithms Proteins Animals Sequence Analysis, Protein Computational Biology Humans Databases, Protein Protein Domains Protein Structure, Quaternary Machine Learning Viral Proteins 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...
Humans Matrix Metalloproteinase Inhibitors Sequence Analysis, Protein Computational Biology Machine Learning Antibodies Databases, Protein Software Matrix Metalloproteinases Algorithms
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ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.

Journal of molecular biology Mar 4, 2020
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system...
Nucleic Acid Conformation Binding Sites Eukaryota Sequence Analysis, Protein Prokaryotic Cells Humans Software DNA Animals Protein Binding RNA Neural Networks, Computer Protein Conformation Computational Biology Machine Learning Proteins
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Sequence-Based Prediction of Fuzzy Protein Interactions.

Journal of molecular biology Feb 27, 2020
It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are em...
Fuzzy Logic Databases, Protein Models, Molecular Protein Domains Protein Folding Humans Algorithms Sequence Analysis, Protein Amino Acid Sequence Protein Binding Protein Interaction Domains and Motifs Sequence Homology Intrinsically Disordered Proteins Protein Conformation
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Computational Identification and Analysis of Ubiquinone-Binding Proteins.

Cells Feb 24, 2020
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will...
Ubiquinone Gene Ontology Binding Sites Position-Specific Scoring Matrices Sequence Analysis, Protein Protein Interaction Domains and Motifs Computational Biology Membrane Proteins Carrier Proteins Amino Acid Sequence Humans Machine Learning Protein Binding
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