AIMC Topic: Binding Sites

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Encoding the space of protein-protein binding interfaces by artificial intelligence.

Computational biology and chemistry
The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underl...

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites.

Journal of molecular graphics & modelling
This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-vi...

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

eLife
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accuratel...

PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.

Journal of chemical theory and computation
The Protein Structure Transformer (PeSTo), a geometric transformer, has exhibited exceptional performance in predicting protein-protein binding interfaces and distinguishing interfaces with nucleic acids, lipids, small molecules, and ions. In this st...

CRIECNN: Ensemble convolutional neural network and advanced feature extraction methods for the precise forecasting of circRNA-RBP binding sites.

Computers in biology and medicine
Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is crucial in circRNA research. Existing prediction models suffer from limited availa...

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

The protein journal
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA b...

Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network.

Interdisciplinary sciences, computational life sciences
Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, c...

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures.

International journal of biological macromolecules
Magnesium ions (Mg) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, cha...

Machine learning approaches in predicting allosteric sites.

Current opinion in structural biology
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked t...

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland)
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...