AIMC Topic: Proteins

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Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.

Molecules (Basel, Switzerland)
In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy...

Machine learning-based approaches for ubiquitination site prediction in human proteins.

BMC bioinformatics
Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due ...

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features.

Scientific reports
Protein-peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and...

Label-free identification of protein aggregates using deep learning.

Nature communications
Protein misfolding and aggregation play central roles in the pathogenesis of various neurodegenerative diseases (NDDs), including Huntington's disease, which is caused by a genetic mutation in exon 1 of the Huntingtin protein (Httex1). The fluorescen...

Time-optimized protein NMR assignment with an integrative deep learning approach using AlphaFold and chemical shift prediction.

Science advances
Chemical shift assignment is vital for nuclear magnetic resonance (NMR)-based studies of protein structures, dynamics, and interactions, providing crucial atomic-level insight. However, obtaining chemical shift assignments is labor intensive and requ...

From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.

Journal of chemical information and modeling
Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack o...

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

International journal of molecular sciences
Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by r...

Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.

Journal of molecular modeling
CONTEXT: Machine learning techniques are becoming increasingly important in the selection and optimization of therapeutic molecules, as well as for the selection of formulation components and the prediction of long-term stability. Compared to first-p...

Functional annotation of enzyme-encoding genes using deep learning with transformer layers.

Nature communications
Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the ...

Predicting multiple conformations via sequence clustering and AlphaFold2.

Nature
AlphaFold2 (ref. ) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates, and disease-causing point mutations often ca...