AIMC Topic: Protein Domains

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PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.

Journal of molecular modeling
The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based ...

UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines.

BMC systems biology
BACKGROUND: The conjugation of ubiquitin to a substrate protein (protein ubiquitylation), which involves a sequential process--E1 activation, E2 conjugation and E3 ligation, is crucial to the regulation of protein function and activity in eukaryotes....

Active Learning-Guided Hit Optimization for the Leucine-Rich Repeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities.

Journal of chemical information and modeling
The leucine-rich repeat kinase 2 (LRRK2) is the most mutated gene in familial Parkinson's disease, and its mutations lead to pathogenic hallmarks of the disease. The LRRK2 WDR domain is an understudied drug target for Parkinson's disease, with no kno...

DPAM-AI: a domain parser for AlphaFold models powered by artificial intelligence.

Bioinformatics (Oxford, England)
MOTIVATION: Due to the breakthrough in protein structure prediction by AlphaFold, the scientific community has access to 200 million predicted protein structures with near-atomic accuracy from the AlphaFold protein structure DataBase (AFDB), covering...

Chainsaw: protein domain segmentation with fully convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structur...

Insights into the inner workings of transformer models for protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent ...

Lactylation prediction models based on protein sequence and structural feature fusion.

Briefings in bioinformatics
Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread ...

Morinda officinalis polysaccharides inhibit the expression and activity of NOD-like receptor thermal protein domain associated protein 3 in inflammatory periodontal ligament cells by upregulating silent information regulator sirtuin 1.

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology
OBJECTIVES: This study aims to investigate the effect of morinda officinalis polysaccharides (MOP) in inflammatory microenvironment on the expression of silent information regulator sirtuin 1 (SIRT1) and NOD-like receptor thermal protein domain assoc...

ProteinMAE: masked autoencoder for protein surface self-supervised learning.

Bioinformatics (Oxford, England)
SUMMARY: The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achie...

CoCoNat: a novel method based on deep learning for coiled-coil prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction me...