AIMC Topic: Binding Sites

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Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Biomolecules
Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the va...

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.

Toxicon : official journal of the International Society on Toxinology
Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several pr...

Identification of active compounds as novel dipeptidyl peptidase-4 inhibitors through machine learning and structure-based molecular docking simulations.

Journal of biomolecular structure & dynamics
The enzyme dipeptidyl peptidase 4 (DPP4) is a potential therapeutic target for type 2 diabetes (T2DM). Many synthetic anti-DPP4 medications are available to treat T2DM. The need for secure and efficient medicines has been unmet due to the adverse sid...

CSM-Potential2: A comprehensive deep learning platform for the analysis of protein interacting interfaces.

Proteins
Proteins are molecular machinery that participate in virtually all essential biological functions within the cell, which are tightly related to their 3D structure. The importance of understanding protein structure-function relationship is highlighted...

KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

Journal of translational medicine
This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and ...

CRBSP:Prediction of CircRNA-RBP Binding Sites Based on Multimodal Intermediate Fusion.

IEEE/ACM transactions on computational biology and bioinformatics
Circular RNA (CircRNA) is widely expressed and has physiological and pathological significance, regulating post-transcriptional processes via its protein-binding activity. However, whereas much work has been done on linear RNA and RNA binding protein...

RefinePocket: An Attention-Enhanced and Mask-Guided Deep Learning Approach for Protein Binding Site Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Protein binding site prediction is an important prerequisite task of drug discovery and design. While binding sites are very small, irregular and varied in shape, making the prediction very challenging. Standard 3D U-Net has been adopted to predict b...

WVDL: Weighted Voting Deep Learning Model for Predicting RNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-binding proteins are important for the process of cell life activities. High-throughput technique experimental method to discover RNA-protein binding sites is time-consuming and expensive. Deep learning is an effective theory for predicting RNA-p...

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

Molecular cell
RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains ...