AI Medical Compendium Topic:
Protein Binding

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Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network.

Scientific reports
Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA bindin...

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland)
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

BMC bioinformatics
BACKGROUND: Immunotherapy is an emerging approach in cancer treatment that activates the host immune system to destroy cancer cells expressing unique peptide signatures (neoepitopes). Administrations of cancer-specific neoepitopes in the form of synt...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

Journal for immunotherapy of cancer
BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising immunotherapy approach with limited off-target effects. However, most peptides predicted using MHC binding prediction algorithms do not induce a CD8 +...

Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

Molecular omics
The zinc (Zn) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in pro...

MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...

Prediction of binding property of RNA-binding proteins using multi-sized filters and multi-modal deep convolutional neural network.

PloS one
RNA-binding proteins (RBPs) are important in gene expression regulations by post-transcriptional control of RNAs and immune system development and its function. Due to the help of sequencing technology, numerous RNA sequences are newly discovered wit...

Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space.

Structure (London, England : 1993)
Flexibility is often a key determinant of protein function. To elucidate the link between their molecular structure and role in an organism, computational techniques such as molecular dynamics can be leveraged to characterize their conformational spa...

Machine-Learning-Based Predictor of Human-Bacteria Protein-Protein Interactions by Incorporating Comprehensive Host-Network Properties.

Journal of proteome research
The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria remains a crucial step in systematically understanding the underlying molecular mechanisms of bacterial infection. Computational prediction approaches a...

Computational advances in combating colloidal aggregation in drug discovery.

Nature chemistry
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assa...