AIMC Topic: Binding Sites

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TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.

Nature communications Apr 16, 2025
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...
Epigenomics Transcription Factors Gene Expression Regulation Humans Deep Learning Computational Biology Chromatin Binding Sites Regulatory Elements, Transcriptional
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Deep-ProBind: binding protein prediction with transformer-based deep learning model.

BMC bioinformatics Mar 22, 2025
Binding proteins play a crucial role in biological systems by selectively interacting with specific molecules, such as DNA, RNA, or peptides, to regulate various cellular processes. Their ability to recognize and bind target molecules with high speci...
Protein Binding Algorithms Computational Biology Databases, Protein Proteins Deep Learning Binding Sites Neural Networks, Computer Sequence Analysis, Protein
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RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

Nature communications Mar 21, 2025
RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale w...
Riboswitch Nucleic Acid Conformation Binding Sites Molecular Docking Simulation Deep Learning RNA
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TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.

Nature communications Mar 20, 2025
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we...
Algorithms Computational Biology Protein Conformation Binding Sites Enzymes Graph Neural Networks Neural Networks, Computer Software
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics Mar 18, 2025
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...
Computational Biology Algorithms Neural Networks, Computer Transcription Factors Convolutional Neural Networks Software DNA Binding Sites Protein Binding Chromatin Immunoprecipitation Sequencing
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RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.

BMC biology Mar 11, 2025
BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in many biological processes, and computationally identifying RNA-RBP interactions provides insights into the biological mechanism of diseases associated with RBPs.
Binding Sites RNA, Circular Computational Biology RNA-Binding Proteins RNA Software Deep Learning Humans
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Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4.

International journal of biological macromolecules Mar 5, 2025
Affinity plays an essential role in the rate and stability of enzyme-catalyzed reactions, thus directly impacting the catalytic activity. In general, the predictive method for protein-ligand binding affinity mainly relies on high-resolution protein c...
Carboxylic Ester Hydrolases Binding Sites Directed Molecular Evolution Amino Acid Sequence Ligands Neural Networks, Computer Deep Learning Protein Binding Models, Molecular
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Skittles: GNN-Assisted Pseudo-Ligands Generation and Its Application for Binding Sites Classification and Affinity Prediction.

Proteins Feb 28, 2025
Nowadays, multiple solutions are known for identifying ligand-protein binding sites. Another important task is labeling each point of a binding site with the appropriate atom type, a process known as pseudo-ligand generation. The number of solutions ...
Machine Learning Algorithms Protein Binding Neural Networks, Computer Models, Molecular Ligands Binding Sites Computational Biology Proteins Databases, Protein
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Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics Feb 27, 2025
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...
Transcription Factors Deep Learning Nucleosomes Binding Sites Humans SOX9 Transcription Factor Protein Binding Animals Mice Chromatin
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Recent advances in AI-driven protein-ligand interaction predictions.

Current opinion in structural biology Feb 24, 2025
Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligan...
Binding Sites Molecular Docking Simulation Drug Discovery Deep Learning Protein Binding Proteins Ligands Humans Artificial Intelligence
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