AIMC Topic: Proteins

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Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Biotechnology journal
Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the prote...

EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Protein science : a publication of the Protein Society
Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule prod...

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Briefings in bioinformatics
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, ...

[Advances in using artificial intelligence for predicting protein-ligand binding affinity].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
The binding of proteins and ligands is a crucial aspect of life processes. The calculation of the protein-ligand binding affinity (PLBA) offers valuable insights into protein function, drug screening targets protein receptors, and enzyme modification...

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Briefings in functional genomics
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research...

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Nucleic acids research
Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting t...

Deep learning for the PSIPRED Protein Analysis Workbench.

Nucleic acids research
The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions a...

Drug-Target Interaction Prediction via Deep Multimodal Graph and Structural Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...