Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
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Updated
May 15, 2024 - Python
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
MMseqs2 app to run on your workstation or servers
Saprot: Protein Language Model with Structural Alphabet
C++ package that provides tools for correcting structural predictions of proteins (eg. from X-Ray Crystallography or AlphaFold) using X-ray small-angle scattering (SAXS) in solution
In this research project a standardised AlphaFold 2-based molecular replacement strategy is developed and implemented in an existing biomolecule structure solution pipeline at MAX IV Laboratory. It can be run on high performance clusters similar to the LUNARC (https://www.lunarc.lu.se/). A standalone and implemented version of the pipeline exists.
Jupyter notebook examples of Scop3P REST API services
Large scale, in silico interaction analyses of SARS-CoV-2 nucleocapsid protein variants against human cytokines.
GUI for running jobs with a local installation of AlphaFold2. Supports submission to queuing systems.
Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.
Protein 3D structure prediction pipeline
molecular graph representation
Predicting Antibody and ACE2 Affinity for SARS-CoV-2 BA.2.86 with In Silico Protein Modeling and Docking
FFFold: A tool for the quick optimisation of protein structures from AlphaFold DB
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
Optimizing AlphaFold Training and Inference on GPU Clusters
The Python software re-glycosylates protein structures using MD simulation results from the glycoshape database. It takes alphafold protein structures as input and outputs modified structures with glycans added at appropriate sites.
𝛂Charges: A tool for the quick calculation of partial atomic charges for protein structures from AlphaFoldDB
Optimal AlphaMissense threshold based on VKGL variant classifications
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