AIMC Topic: Protein Interaction Mapping

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Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry.

Nature communications May 28, 2021
Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits t...
Peptides Time Factors Proteomics Escherichia coli Neural Networks, Computer Chromatography, Reverse-Phase Protein Interaction Mapping Multiprotein Complexes Cross-Linking Reagents Chromatography, High Pressure Liquid Tandem Mass Spectrometry Escherichia coli Proteins
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Drug Target Identification with Machine Learning: How to Choose Negative Examples.

International journal of molecular sciences May 12, 2021
Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target I...
Pharmaceutical Preparations Humans Protein Interaction Mapping Machine Learning Support Vector Machine Drug Discovery Software Proteins Computational Biology
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Protein Complexes Detection Based on Semi-Supervised Network Embedding Model.

IEEE/ACM transactions on computational biology and bioinformatics Apr 8, 2021
A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, th...
Protein Interaction Mapping Cluster Analysis Proteins Supervised Machine Learning Protein Interaction Maps Computational Biology Algorithms
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Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Scientific reports Apr 1, 2021
Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593...
Humans Machine Learning Molecular Docking Simulation Structure-Activity Relationship Molecular Structure Molecular Dynamics Simulation Ligands Drug Discovery Protein Interaction Maps Protein Binding Small Molecule Libraries Protein Interaction Mapping Kelch-Like ECH-Associated Protein 1 Molecular Conformation NF-E2-Related Factor 2 Binding Sites
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New machine learning and physics-based scoring functions for drug discovery.

Scientific reports Feb 4, 2021
Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different tar...
Software Internet Protease Inhibitors Research Design Ligands Humans Peptide Hydrolases Protein Interaction Mapping Hydrophobic and Hydrophilic Interactions Entropy Molecular Docking Simulation Drugs, Investigational Datasets as Topic Drug Discovery Support Vector Machine
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Protein2Vec: Aligning Multiple PPI Networks with Representation Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Research of Protein-Protein Interaction (PPI) Network Alignment is playing an important role in understanding the crucial underlying biological knowledge such as functionally homologous proteins and conserved evolutionary pathways across different sp...
Computational Biology Protein Interaction Mapping Sequence Alignment Machine Learning Animals Humans Protein Interaction Maps Algorithms
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A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are...
Computational Biology Protein Interaction Mapping Saccharomyces cerevisiae Proteins Intracellular Space Transcriptome Deep Learning Protein Interaction Maps
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Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins Feb 2, 2021
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help des...
Models, Molecular Binding Sites Protein Interaction Mapping Protein Interaction Domains and Motifs Humans Proteins Machine Learning Databases, Protein ROC Curve Principal Component Analysis Protein Binding Computational Biology
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Classification and prediction of protein-protein interaction interface using machine learning algorithm.

Scientific reports Jan 19, 2021
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therap...
Models, Molecular Support Vector Machine Molecular Docking Simulation Protein Interaction Mapping Proteins Binding Sites Protein Interaction Maps Databases, Protein Thermodynamics Protein Binding
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A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.

Interdisciplinary sciences, computational life sciences Jan 12, 2021
The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public heal...
ROC Curve Genome, Viral COVID-19 Protein Binding Reproducibility of Results Algorithms Humans Protein Interaction Mapping Amino Acids SARS-CoV-2 Deep Learning
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