AI Medical Compendium Topic:
Protein Binding

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Imputation for transcription factor binding predictions based on deep learning.

PLoS computational biology
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....

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PloS one
Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such ...

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

PloS one
Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scorin...

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

Proteins
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...

A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

Archives of pharmacal research
Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were develop...

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

BMC systems biology
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...

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

BMC genomics
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...

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

Proteins
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...

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

Artificial intelligence in medicine
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...