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Protein Domains

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QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding.

PloS one
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...

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

Biomolecules
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our anal...

GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

Cells
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling...

DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

Journal of bioinformatics and computational biology
Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are f...

FUpred: detecting protein domains through deep-learning-based contact map prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available...

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...

Structures of the β-barrel assembly machine recognizing outer membrane protein substrates.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
β-barrel outer membrane proteins (β-OMPs) play critical roles in nutrition acquisition, protein import/export, and other fundamental biological processes. The assembly of β-OMPs in Gram-negative bacteria is mediated by the β-barrel assembly machinery...

Characterizing the function of domain linkers in regulating the dynamics of multi-domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence.

Proteins
Multi-domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called mul...

Improving protein domain classification for third-generation sequencing reads using deep learning.

BMC genomics
BACKGROUND: With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce impor...