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Signal Transduction

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Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

eLife
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allo...

Multi-source transfer learning with Graph Neural Network for excellent modelling the bioactivities of ligands targeting orphan G protein-coupled receptors.

Mathematical biosciences and engineering : MBE
G protein-coupled receptors (GPCRs) have been the targets for more than 40% of the currently approved drugs. Although neural networks can effectively improve the accuracy of prediction with the biological activity, the result is undesirable in the li...

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning.

GigaScience
BACKGROUND: A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we condu...

Multimodal multi-task deep neural network framework for kinase-target prediction.

Molecular diversity
Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods f...

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Science (New York, N.Y.)
Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations ...

Deep-Learning-Enhanced Diffusion Imaging Assay for Resolving Local-Density Effects on Membrane Receptors.

Analytical chemistry
G-protein-coupled receptor (GPCR) density at the cell surface is thought to regulate receptor function. Spatially resolved measurements of local-density effects on GPCRs are needed but technically limited by density heterogeneity and mobility of memb...

PPICT: an integrated deep neural network for predicting inter-protein PTM cross-talk.

Briefings in bioinformatics
Post-translational modifications (PTMs) fine-tune various signaling pathways not only by the modification of a single residue, but also by the interplay of different modifications on residue pairs within or between proteins, defined as PTM cross-talk...

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

Computers in biology and medicine
The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we desig...

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

Nature methods
Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mec...

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions.

International journal of biological macromolecules
Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphory...