AI Medical Compendium Topic:
Protein Binding

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A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.

PloS one
BACKGROUND: Predicting protein-DNA binding sites in vivo is a challenging but urgent task in many fields such as drug design and development. Most promoters contain many transcription factor (TF) binding sites, yet only a few have been identified thr...

Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning.

Methods in molecular biology (Clifton, N.J.)
The interactions between long noncoding RNA (lncRNA) and protein play crucial roles in various biological processes. Computational methods are essential for predicting lncRNA-protein interactions and deciphering their mechanisms. In this chapter, we ...

SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.

Protein science : a publication of the Protein Society
Computational methods to predict binding affinity of protein-ligand complex have been used extensively to design inhibitors for proteins selected as drug targets. In recent years machine learning (ML) is being increasingly used for design of drugs/in...

An in vitro and machine learning framework for quantifying serum albumin binding of per- and polyfluoroalkyl substances.

Toxicological sciences : an official journal of the Society of Toxicology
Per- and polyfluoroalkyl substances (PFAS) are a diverse class of anthropogenic chemicals; many are persistent, bioaccumulative, and mobile in the environment. Worldwide, PFAS bioaccumulation causes serious adverse health impacts, yet the physiochemi...

TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Bioinformatics (Oxford, England)
MOTIVATION: T-cell receptors (TCRs) elicit and mediate the adaptive immune response by recognizing antigenic peptides, a process pivotal for cancer immunotherapy, vaccine design, and autoimmune disease management. Understanding the intricate binding ...

Improving generalizability of drug-target binding prediction by pre-trained multi-view molecular representations.

Bioinformatics (Oxford, England)
MOTIVATION: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent divers...

Examining the Influenza A Virus Sialic Acid Binding Preference Predictions of a Sequence-Based Convolutional Neural Network.

Influenza and other respiratory viruses
BACKGROUND: Though receptor binding specificity is well established as a contributor to host tropism and spillover potential of influenza A viruses, determining receptor binding preference of a specific virus still requires expensive and time-consumi...

Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.

Briefings in bioinformatics
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I m...

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Alth...

Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...