AIMC Topic: Binding Sites

Clear Filters Showing 341 to 350 of 500 articles

Imputation for transcription factor binding predictions based on deep learning.

PLoS computational biology Feb 24, 2017
Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard....
Humans Neural Networks, Computer Machine Learning Reproducibility of Results Sensitivity and Specificity High-Throughput Nucleotide Sequencing K562 Cells Protein Binding Transcription Factors Binding Sites Hep G2 Cells Protein Interaction Mapping Chromatin Immunoprecipitation
View on PubMed DOI

A machine learning approach for ranking clusters of docked protein-protein complexes by pairwise cluster comparison.

Proteins Jan 20, 2017
Reliable identification of near-native poses of docked protein-protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein-protein interactions is challenging for traditional biophysical or knowledge based potentials and th...
Binding Sites Thermodynamics Protein Interaction Mapping Structural Homology, Protein Research Design Molecular Docking Simulation Protein Binding Computational Biology Cluster Analysis Machine Learning Proteins Software Benchmarking Protein Conformation
View on PubMed DOI

Synthesis and antimicrobial studies of novel derivatives of 4-(4-formyl-3-phenyl-1H-pyrazol-1-yl)benzoic acid as potent anti-Acinetobacter baumannii agents.

Bioorganic & medicinal chemistry letters Dec 29, 2016
Microbial resistance to antibiotics is a global concern. The World Health Organization (WHO) has identified antimicrobial resistance as one the three greatest threats for human beings in the 21st century. Without urgent and coordinated action, the wo...
Structure-Activity Relationship Catalytic Domain Acinetobacter baumannii Benzoic Acid Anti-Infective Agents Molecular Docking Simulation Pyrazoles Microbial Sensitivity Tests Gram-Negative Bacteria Drug Resistance, Multiple, Bacterial Gram-Positive Bacteria Binding Sites
View on PubMed DOI

An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA.

BMC genomics Dec 22, 2016
BACKGROUND: The ability to engineer zinc finger proteins binding to a DNA sequence of choice is essential for targeted genome editing to be possible. Experimental techniques and molecular docking have been successful in predicting protein-DNA interac...
Algorithms Protein Binding DNA Models, Molecular DNA-Binding Proteins Molecular Conformation Neural Networks, Computer Binding Sites Zinc Fingers
View on PubMed DOI

Predicting protein conformational changes for unbound and homology docking: learning from intrinsic and induced flexibility.

Proteins Dec 5, 2016
Predicting protein conformational changes from unbound structures or even homology models to bound structures remains a critical challenge for protein docking. Here we present a study directly addressing the challenge by reducing the dimensionality a...
Machine Learning Computational Biology Models, Statistical Proteins Benchmarking Software Research Design Molecular Docking Simulation Protein Binding Protein Multimerization Structural Homology, Protein Protein Conformation Dimensional Measurement Accuracy Binding Sites Thermodynamics
View on PubMed DOI

A high-order representation and classification method for transcription factor binding sites recognition in Escherichia coli.

Artificial intelligence in medicine Dec 1, 2016
BACKGROUND: Identifying transcription factors binding sites (TFBSs) plays an important role in understanding gene regulatory processes. The underlying mechanism of the specific binding for transcription factors (TFs) is still poorly understood. Previ...
Transcription Factors Escherichia coli Humans Binding Sites Algorithms Protein Binding Computational Biology
View on PubMed DOI

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

Nucleic acids research Nov 29, 2016
The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF b...
Humans Chromatin Machine Learning Chromatin Assembly and Disassembly Human Embryonic Stem Cells CD4-Positive T-Lymphocytes Hepatocytes Gene Expression Regulation Organ Specificity K562 Cells Histones Binding Sites Hep G2 Cells Algorithms Primary Cell Culture Cell Line Cell Line, Tumor Principal Component Analysis Protein Binding DNA Transcription Factors
View on PubMed DOI

A Graph Approach to Mining Biological Patterns in the Binding Interfaces.

Journal of computational biology : a journal of computational molecular cell biology Nov 28, 2016
Protein-RNA interactions play important roles in the biological systems. Searching for regular patterns in the Protein-RNA binding interfaces is important for understanding how protein and RNA recognize each other and bind to form a complex. Herein, ...
Amino Acid Motifs Protein Interaction Domains and Motifs RNA-Binding Proteins Support Vector Machine Proteins Data Mining RNA Protein Conformation Binding Sites Computer Graphics Animals Molecular Docking Simulation Protein Binding Computational Biology Humans
View on PubMed DOI

Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

Computational biology and chemistry Nov 9, 2016
Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and s...
Proteins Carbohydrates Binding Sites Support Vector Machine Cluster Analysis
View on PubMed DOI

Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy.

HLA Oct 20, 2016
Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing ...
Binding Sites Protein Interaction Domains and Motifs Structural Homology, Protein Macaca Gorilla gorilla Pan troglodytes Humans Protein Binding Machine Learning Alleles Mice Software Histocompatibility Antigens Class I Gene Expression Animals Ligands Epitopes Oligopeptides
View on PubMed DOI
Popular Topics
Recent Journals