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Protein Binding

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A Deep Learning Model for RNA-Protein Binding Preference Prediction Based on Hierarchical LSTM and Attention Network.

IEEE/ACM transactions on computational biology and bioinformatics
Attention mechanism has the ability to find important information in the sequence. The regions of the RNA sequence that can bind to proteins are more important than those that cannot bind to proteins. Neither conventional methods nor deep learning-ba...

Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4.

Journal of computer-aided molecular design
Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engine...

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction.

Journal of molecular biology
Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating ...

PharmRF: A machine-learning scoring function to identify the best protein-ligand complexes for structure-based pharmacophore screening with high enrichments.

Journal of computational chemistry
Structure-based pharmacophore models are often developed by selecting a single protein-ligand complex with good resolution and better binding affinity data which prevents the analysis of other structures having a similar potential to act as better te...

DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...

Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) Based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes.

Molecular informatics
Accurate prediction of binding poses is crucial to structure-based drug design. We employ two powerful artificial intelligence (AI) approaches, data-mining and machine-learning, to design artificial neural network (ANN) based pose-scoring function. I...

Base-resolution prediction of transcription factor binding signals by a deep learning framework.

PLoS computational biology
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framewo...

Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Journal of computational chemistry
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweigh...

Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics.

Journal of chemical theory and computation
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensiv...

Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland)
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...