AIMC Topic: Protein Kinases

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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...
Databases, Protein Receptors, G-Protein-Coupled Deep Learning Protein Binding Humans Protein Kinases Algorithms Ligands Molecular Docking Simulation Crystallization Drug Discovery Molecular Structure Drug Evaluation, Preclinical
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Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2021
Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein k...
Neural Networks, Computer Proteome Protein Processing, Post-Translational Substrate Specificity Phosphorylation Phosphoric Monoester Hydrolases Protein Kinases
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The neXtProt knowledgebase in 2020: data, tools and usability improvements.

Nucleic acids research Jan 8, 2020
The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describ...
Internet Protein Kinases Proteins Databases, Protein Knowledge Bases Trypsin User-Computer Interface Sequence Analysis, RNA Mass Spectrometry Protein Processing, Post-Translational Software Peptides Humans
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Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research Nov 18, 2019
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...
Genes Humans Protein Kinases Quantitative Structure-Activity Relationship Small Molecule Libraries Sequence Analysis, Protein Machine Learning Protein Kinase Inhibitors Protein Binding Animals Evolution, Molecular
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A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction.

Bioinformatics (Oxford, England) Apr 1, 2019
MOTIVATION: Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fe...
Phosphorylation Protein Kinases Ligands Neural Networks, Computer Proteins
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Learning with multiple pairwise kernels for drug bioactivity prediction.

Bioinformatics (Oxford, England) Jul 1, 2018
MOTIVATION: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel...
Neoplasms Drug Discovery Support Vector Machine Humans Antineoplastic Agents Signal Transduction Computational Biology Protein Kinases Software Cell Line, Tumor Treatment Outcome
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MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Bioinformatics (Oxford, England) Dec 15, 2017
MOTIVATION: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. ...
Phosphorylation Software Protein Kinases Phosphoproteins Proteins Sequence Analysis, Protein Neural Networks, Computer Machine Learning
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