AIMC Topic: Kelch-Like ECH-Associated Protein 1

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AI cancer driver mutation predictions are valid in real-world data.

Nature communications
Characterizing and validating which mutations influence development of cancer is challenging. Artificial intelligence (AI) has delivered significant advances in protein structure prediction, but its utility for identifying cancer drivers is less expl...

Pred5AOP: an efficient screening of food-derived antioxidant peptides based on deep learning, molecular docking, and experimental validation.

Food chemistry
Antioxidant peptides derived from dietary proteins positively impact human health due to their high activity and safety. In this study, a database of 76,343 peptides was constructed via in silico hydrolysis of 29 dietary proteins. A novel antioxidant...

Cyclic peptide structure prediction and design using AlphaFold2.

Nature communications
Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here...

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions.

Journal of chemical information and modeling
Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts thei...

Use of Deep-Learning Assisted Assessment of Cardiac Parameters in Zebrafish to Discover Cyanidin Chloride as a Novel Keap1 Inhibitor Against Doxorubicin-Induced Cardiotoxicity.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Doxorubicin-induced cardiomyopathy (DIC) brings tough clinical challenges as well as continued demand in developing agents for adjuvant cardioprotective therapies. Here, a zebrafish phenotypic screening with deep-learning assisted multiplex cardiac f...

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.

Chemical communications (Cambridge, England)
We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resulted in a large increase of the hit rate against a protein-protein interaction target. This was demo...

VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.

International journal of molecular sciences
The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and th...

Recapitulating the Binding Affinity of Nrf2 for KEAP1 in a Cyclic Heptapeptide, Guided by NMR, X-ray Crystallography, and Machine Learning.

Journal of the American Chemical Society
Macrocycles, including macrocyclic peptides, have shown promise for targeting challenging protein-protein interactions (PPIs). One PPI of high interest is between Kelch-like ECH-Associated Protein-1 (KEAP1) and Nuclear Factor (Erythroid-derived 2)-li...