Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.
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Updated
Mar 29, 2021 - Jupyter Notebook
Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.
Predicting the effect of mutations on protein stability using a simple orientational potential.
Some notes (cookbook) for pyMol. Protein Crystallography course.
Predicting the effect of mutations on protein stability and protein-protein interaction affinity.
Prediction of protein thermodynamic stability changes upon mutations through a Gaussian Network Model simulating protein unfolding behavior
A workflow to get rid of redundant mutations
Implementation of Abyssal, a deep neural network trained with a new "mega" dataset to predict the impact of an amino acid variant on protein stability.
Reimplementation of RaSP, a deep neural network for rapid protein stability prediction, in PyTorch.
https://biohackathon.biolib.com/event/2021-protein-edition/ - team "house-of-mutants" - task "Predicting multi-mutant protein stability"
Snakemake pipeline for Rosetta 'cartesian-ddg' protocol for protein stability prediction upon mutations.
Large data set of thermal stabilities for mutants of BglB, and associated publication
Identify the thermostable mutations in enzymes
Official repository for the paper "Few-shot Prediction of the experimental functional measurements for proteins with single point mutations".
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