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
Optimizing AlphaFold Training and Inference on GPU Clusters
Trainable PyTorch framework for developing protein, RNA and complex models.
Saprot: Protein Language Model with Structural Alphabet
Predicting direct protein-protein interactions with AlphaFold deep learning neural network models.
Modified version of Alphafold to divide CPU part (MSA and template searching) and GPU part. This can accelerate Alphafold when predicting multiple structures
Exploring Evolution-aware & free protein language models as protein function predictors
PyMOL extension to color AlphaFold structures by confidence (pLDDT).
Run AlphaFold2 (and multimer) step by step
Infrastructure template and Jupyter notebooks for running RoseTTAFold on AWS Batch.
A tool for predicting the effects of missense mutations on protein stability changes upon missense mutation using protein sequence only. PROST uses colab AlhpaFold2 for the prediction of pdb struture from FASTA sequence.
A tool to visualise the results of AlphaFold2 and inspect the quality of structural predictions
This repository contains the AlphaCutter.py for the removal of non-globular regions from predicted protein structures.
LightMHC: A Light Model for pMHC Structure Prediction with Graph Neural Networks
𝛂Charges: A tool for the quick calculation of partial atomic charges for protein structures from AlphaFoldDB
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.
An implementation of Invariant Point Attention from Alphafold 2
GUI for running jobs with a local installation of AlphaFold2. Supports submission to queuing systems.
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