Nature Biotechnology: Ultra-fast, sensitive detection of protein remote homologs using deep dense retrieval
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Updated
Jul 8, 2024 - Python
Nature Biotechnology: Ultra-fast, sensitive detection of protein remote homologs using deep dense retrieval
Predict protein folding structures using ColabFold. Gain a deeper understanding of protein folding prediction with AlphaFold2 and MMseqs2. Run the Jupyter notebook on UCloud, learn to interpret results, predict protein structures of interest. Technical requirements provided. Enhance your knowledge of protein folding and AlphaFold2's principles. Fam
AlphaFold Unofficial Wiki
The provided code snippet forms a critical component of an automated script aimed at facilitating the prediction of protein structures using the AlphaFold model within a Google Colab environment. The script is tailored to handle the simultaneous upload of multiple input files, each containing multiple protein sequences for prediction.
Prediction of possible paths of gene mutation using AlphaFold
Investigate the role of mtDNA in the sex determination/development of Potamilus streckersoni, a freshwater mussel with doubly uniparental mitochondrial inheritance. Scripts for DESeq2, WGCNA, GSEA, AlphaFold/AlphaPulldown, and mt-sncRNA validation.
BETA provides datasets of structures and sequences that were not used during AlphaFold training.
Reliable AlphaFold Measures
This repo contains a random forest, a convolutional neural network, and a graph convolutional neural network which predict the binding interaction between olfactory proteins and various chemicals using Alphafold predicted structural data.
Repository with scripts and data generated during my internship at Institut Pasteur of Paris
dna protein prediction using transformer. second version of engima-1.5b
A proof-of-concept for using AlphaFold with new amino acid sequences (this may change to a full project in the future).
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