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Binding Sites

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Geometric epitope and paratope prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this article, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the ...

DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and bette...

PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.

Briefings in bioinformatics
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitatio...

EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.

Briefings in bioinformatics
Mechanisms of protein-DNA interactions are involved in a wide range of biological activities and processes. Accurately identifying binding sites between proteins and DNA is crucial for analyzing genetic material, exploring protein functions, and desi...

BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Briefings in bioinformatics
Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabol...

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Nucleic acids research
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting prot...

Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning.

GigaScience
Identification of protein-protein and protein-nucleic acid binding sites provides insights into biological processes related to protein functions and technical guidance for disease diagnosis and drug design. However, accurate predictions by computati...

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

GigaScience
BACKGROUND: RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throug...

Estimating protein-ligand interactions with geometric deep learning and mixture density models.

Journal of biosciences
Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any drug design workflow. Multiple experimental and computational methods have been developed to better understand these...

Peptidic Compound as DNA Binding Agent: Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation.

Protein and peptide letters
BACKGROUND: Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G...