Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project
Sign Up to like & getrecommendations! Published in 2019 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.8b00794
Abstract: The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes novel… read more here.
Keywords: machine learning; project; androgen; predict binding ... See more keywords
PRIME-3D2D is a 3D2D model to predict binding sites of protein–RNA interaction
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DOI: 10.1038/s42003-020-1114-y
Abstract: Protein-RNA interaction participates in many biological processes. So, studying protein–RNA interaction can help us to understand the function of protein and RNA. Although the protein–RNA 3D3D model, like PRIME, was useful in building 3D structural complexes,… read more here.
Keywords: rna interaction; prime 3d2d; predict binding; binding sites ... See more keywords
GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs
Sign Up to like & getrecommendations! Published in 2022 at "Nucleic Acids Research"
DOI: 10.1093/nar/gkac323
Abstract: Abstract Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are… read more here.
Keywords: grasp web; residue neighborhood; based residue; strategy predict ... See more keywords