AIMC Topic: Proteins

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HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods...

Deep neural learning based protein function prediction.

Mathematical biosciences and engineering : MBE
It is vital for the annotation of uncharacterized proteins by protein function prediction. At present, Deep Neural Network based protein function prediction is mainly carried out for dataset of small scale proteins or Gene Ontology, and usually explo...

The reactome pathway knowledgebase 2022.

Nucleic acids research
The Reactome Knowledgebase (https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired dise...

TGSA: protein-protein association-based twin graph neural networks for drug response prediction with similarity augmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances in deep learning algorithms make it possible to predict drug responses accurately based on genetic prof...

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed.

Methods in molecular biology (Clifton, N.J.)
In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular ...

Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction.

Methods in molecular biology (Clifton, N.J.)
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage...

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Methods in molecular biology (Clifton, N.J.)
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. He...

Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.

Methods in molecular biology (Clifton, N.J.)
After nearly two decades of research in the field of computational methods based on machine learning and knowledge-based potentials for ΔG and ΔΔG prediction upon variations, we now realize that all the approaches are poorly performing when tested on...

Machine Learning-driven Protein Library Design: A Path Toward Smarter Libraries.

Methods in molecular biology (Clifton, N.J.)
Proteins are small yet valuable biomolecules that play a versatile role in therapeutics and diagnostics. The intricate sequence-structure-function paradigm in the realm of proteins opens the possibility for directly mapping amino acid sequence to fun...

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Current drug metabolism
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of re...