AIMC Topic: Protein Binding

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Systematic auditing is essential to debiasing machine learning in biology.

Communications biology Feb 10, 2021
Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify ...
Bias Pharmaceutical Preparations Data Mining Proteome Proteins Databases, Protein Histocompatibility Antigens Machine Learning Reproducibility of Results Animals Protein Interaction Maps Protein Binding Proteomics Humans
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OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction.

Journal of molecular graphics & modelling Feb 9, 2021
Voxel-based 3D convolutional neural networks (CNNs) have been applied to predict protein-ligand binding affinity. However, the memory usage and computation cost of these voxel-based approaches increase cubically with respect to spatial resolution and...
Proteins Ligands Neural Networks, Computer 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...
Chromatin Immunoprecipitation Sequencing Binding Sites Epigenesis, Genetic Deep Learning Histone Code Transcription Factors Protein Binding Computational Biology
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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 Binding Sites Protein Interaction Mapping Protein Interaction Domains and Motifs Proteins 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 Computational Biology Protein Binding 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...
Ligands Datasets as Topic Binding Sites Deep Learning Caenorhabditis elegans Protein Binding Humans Protein Structure, Secondary Protein Domains Animals Drug Discovery
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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 Humans Disease Protein Binding Ligands Binding Sites Genome, Human Polymorphism, Single Nucleotide SELEX Aptamer Technique Transcription Factors
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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...
Humans Amino Acid Sequence Neural Networks, Computer Support Vector Machine Adenosine Triphosphate Carrier Proteins Algorithms Proteins Protein Binding Computational Biology Machine Learning
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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 Binding Sites Proteins Thermodynamics Databases, Protein Protein Interaction Mapping Models, Molecular
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