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Binding Sites

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Annotation of biologically relevant ligands in UniProtKB using ChEBI.

Bioinformatics (Oxford, England)
MOTIVATION: To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support effort...

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Protein science : a publication of the Protein Society
Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains ch...

CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types.

Bioinformatics (Oxford, England)
MOTIVATION: Three-dimensional (3D) genome organization is of vital importance in gene regulation and disease mechanisms. Previous studies have shown that CTCF-mediated chromatin loops are crucial to studying the 3D structure of cells. Although variou...

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Briefings in bioinformatics
Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional ...

DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors.

Nucleic acids research
We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predi...

Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

Bioinformatics (Oxford, England)
MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene ex...

CSM-Potential: mapping protein interactions and biological ligands in 3D space using geometric deep learning.

Nucleic acids research
Recent advances in protein structural modelling have enabled the accurate prediction of the holo 3D structures of almost any protein, however protein function is intrinsically linked to the interactions it makes. While a number of computational appro...

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Nucleic acids research
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 of fund...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) i...