AIMC Topic: Protein Kinases

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Machine Learning Models for Accurate Prediction of Kinase Inhibitors with Different Binding Modes.

Journal of medicinal chemistry
Noncovalent inhibitors of protein kinases have different modes of action. They bind to the active or inactive form of kinases, compete with ATP, stabilize inactive kinase conformations, or act through allosteric sites. Accordingly, kinase inhibitors ...

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation.

Journal of medicinal chemistry
The kinome-wide virtual profiling of small molecules with high-dimensional structure-activity data is a challenging task in drug discovery. Here, we present a virtual profiling model against a panel of 391 kinases based on large-scale bioactivity dat...

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Journal of computer-aided molecular design
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affin...

Redefining the Protein Kinase Conformational Space with Machine Learning.

Cell chemical biology
Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the...

Assisting document triage for human kinome curation via machine learning.

Database : the journal of biological databases and curation
In the era of data explosion, the increasing frequency of published articles presents unorthodox challenges to fulfill specific curation requirements for bio-literature databases. Recognizing these demands, we designed a document triage system with a...

Novel Method Proposing Chemical Structures with Desirable Profile of Activities Based on Chemical and Protein Spaces.

Molecular informatics
Active molecules among numerous chemical structures in a chemical database can be searched easily by statistical prediction of compound-protein interactions. However, constructing a simple prediction model against one protein does not aid drug design...

Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates.

Molecular & cellular proteomics : MCP
Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is...

Classifying kinase conformations using a machine learning approach.

BMC bioinformatics
BACKGROUND: Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and ...

Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Journal of chemical information and modeling
Virtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends ...

Sensitive and versatile electrogenerated chemiluminescence biosensing platform for protein kinase based on Ru(bpy)3(2+) functionalized gold nanoparticles mediated signal transduction.

Analytica chimica acta
A novel, sensitive and versatile electrogenerated chemiluminescence biosensing platform is developed for monitoring activity and inhibition of protein kinase based on Ru(bpy)3(2+) functionalized gold nanoparticles (Ru(bpy)3(2+)-AuNPs) mediated signal...