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Binding Sites

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DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

International journal of molecular sciences
It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction ...

A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network.

BMC bioinformatics
BACKGROUND: Studies have shown that RNA secondary structure, a planar structure formed by paired bases, plays diverse vital roles in fundamental life activities and complex diseases. RNA secondary structure profile can record whether each base is pai...

Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...

Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Proteins
Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better predict...

A novel artificial intelligence protocol to investigate potential leads for diabetes mellitus.

Molecular diversity
Dipeptidyl peptidase-4 (DPP4) is highly participated in regulating diabetes mellitus (DM), and inhibitors of DPP4 may act as potential DM drugs. Therefore, we performed a novel artificial intelligence (AI) protocol to screen and validate the potentia...

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures.

Cell research
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/o...

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

PLoS computational biology
N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing ...

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.

Proteins
Deep learning has emerged as a revolutionary technology for protein residue-residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning-based contact predictions have been achie...

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

Molecular diversity
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...