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Protein Binding

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Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Molecular diversity Feb 3, 2021
Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 re...
Reproducibility of Results Drug Discovery ROC Curve Protein Binding Machine Learning Algorithms Receptors, Leukotriene Binding Sites Cheminformatics Leukotriene Antagonists Models, Molecular Molecular Structure Quantitative Structure-Activity Relationship
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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary D...
Transcription Factors Binding Sites Histone Code Protein Binding Computational Biology Epigenesis, Genetic Chromatin Immunoprecipitation Sequencing Deep Learning
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Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins Feb 2, 2021
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help des...
ROC Curve Protein Binding Computational Biology Humans Machine Learning Models, Molecular Protein Interaction Mapping Protein Interaction Domains and Motifs Proteins Binding Sites Databases, Protein Principal Component Analysis
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DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.

BMC bioinformatics Feb 1, 2021
BACKGROUND: Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from paralle...
Chromatin Neural Networks, Computer Protein Binding Computational Biology Transcription Factors Binding Sites
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SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences Jan 30, 2021
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...
Humans Ligands Caenorhabditis elegans Deep Learning Animals Protein Structure, Secondary Datasets as Topic Drug Discovery Protein Binding Binding Sites Protein Domains
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Systematic analysis of binding of transcription factors to noncoding variants.

Nature Jan 27, 2021
Many sequence variants have been linked to complex human traits and diseases, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human trans...
Support Vector Machine Ligands Humans Genome, Human Protein Binding Disease Polymorphism, Single Nucleotide Binding Sites Transcription Factors SELEX Aptamer Technique
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Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

International journal of molecular sciences Jan 19, 2021
Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-AT...
Adenosine Triphosphate Carrier Proteins Proteins Amino Acid Sequence Protein Binding Computational Biology Support Vector Machine Humans Machine Learning Neural Networks, Computer Algorithms
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Classification and prediction of protein-protein interaction interface using machine learning algorithm.

Scientific reports Jan 19, 2021
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therap...
Molecular Docking Simulation Protein Interaction Maps Protein Binding Support Vector Machine Proteins Thermodynamics Protein Interaction Mapping Databases, Protein Models, Molecular Binding Sites
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A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.

Interdisciplinary sciences, computational life sciences Jan 12, 2021
The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public heal...
Reproducibility of Results SARS-CoV-2 ROC Curve Deep Learning Protein Binding Genome, Viral Humans Amino Acids Protein Interaction Mapping COVID-19 Algorithms
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Prediction of kinase inhibitors binding modes with machine learning and reduced descriptor sets.

Scientific reports Jan 12, 2021
Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and b...
Machine Learning Protein Binding Support Vector Machine Humans Protein Conformation Models, Molecular Protein Kinase Inhibitors Protein Kinases
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