AIMC Topic: Lysine

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A deep learning framework for lysine 2-hydroxyisobutyrylation site prediction using evolutionary feature representation.

Scientific reports
Lysine 2-hydroxyisobutyrylation (Khib) has emerged as a crucial Post-Translational Modification (PTM) with significant roles in diverse biological processes ranging from gene expression to metabolic regulation. Despite its importance, computational a...

Deep Learning for Automated Measures of SUV and Molecular Tumor Volume in [Ga]PSMA-11 or [F]DCFPyL, [F]FDG, and [Lu]Lu-PSMA-617 Imaging with Global Threshold Regional Consensus Network.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Metastatic castration-resistant prostate cancer has a high rate of mortality with a limited number of effective treatments after hormone therapy. Radiopharmaceutical therapy with [Lu]Lu-prostate-specific membrane antigen-617 (LuPSMA) is one treatment...

Modeling skin sensitization: hierarchical support vector regression-based prediction of lysine depletion in DPRA.

Chemico-biological interactions
Skin sensitization is a critical endpoint in toxicology, especially in the context of drug discovery and development process for topical and transdermal treatments. Accurate evaluation of skin sensitization potential is essential to ensure the safety...

Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.

Nature communications
The pyrrolysyl-tRNA synthetase (PylRS) is widely used to incorporate noncanonical amino acids (ncAAs) into proteins. However, the yields of most ncAA-containing protein  remain low due to the limited activity of PylRS variants. Here, we apply machine...

EUP: Enhanced cross-species prediction of ubiquitination sites via a conditional variational autoencoder network based on ESM2.

PLoS computational biology
Ubiquitination is critical in biomedical research. Predicting ubiquitination sites based on deep learning model have advanced the study of ubiquitination. However, traditional supervised model limits in the scenarios where labels are scarcity across ...

Joint control and machine learning prediction of co-formation and kinetic profiles of typical hazardous Maillard reaction products by catechin treatment in air-fried potato chips.

Food chemistry
The Maillard reaction generates hazardous processing contaminants, including acrylamide (AA) and Nε-(carboxymethyl)lysine (CML), necessitating effective inhibitors. Here we use machine learning approaches to predict how catechin treatment reduces sim...

SSE-Net: A novel network based on sequence spatial equation for Camellia sinensis lysine acetylation identification.

Computational biology and chemistry
Lysine acetylation (Kace) is one of the most important post-translational modifications. It is key to identify Kace sites for understanding regulation mechanisms in Camellia sinensis. In this study, we defined a mathematical formula, named sequence s...

Lipid discovery enabled by sequence statistics and machine learning.

eLife
Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that MprF synthesizes lysyl-phosphatidylgl...

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...