AI Medical Compendium Topic:
Protein Binding

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RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

IEEE/ACM transactions on computational biology and bioinformatics
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-pr...

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.)
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...

Predicting drug-target interaction network using deep learning model.

Computational biology and chemistry
BACKGROUND: Traditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target...

CiRCus: A Framework to Enable Classification of Complex High-Throughput Experiments.

Journal of proteome research
Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results have not kept pace, requiring significant manual efforts to do so. Here, we present CiRCus, a framework to gen...

Elucidating the druggability of the human proteome with eFindSite.

Journal of computer-aided molecular design
Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and r...

Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells.

Methods (San Diego, Calif.)
We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulatio...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

Journal of molecular graphics & modelling
BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for reproductive health. The role of FSHR in tumor progression as well as osteoporosis advancement has al...

RPITER: A Hierarchical Deep Learning Framework for ncRNA⁻Protein Interaction Prediction.

International journal of molecular sciences
Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-bi...

Predicting protein-ligand binding residues with deep convolutional neural networks.

BMC bioinformatics
BACKGROUND: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categ...