SaprotHub: Making Protein Modeling Accessible to All Biologists
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Updated
Jul 9, 2024 - Jupyter Notebook
SaprotHub: Making Protein Modeling Accessible to All Biologists
Tool to design cyclic peptides that mimic proteins and target their binding partners.
Code to accompany the paper: ”ProteinNetworkSight efficiently transforms co-expressed protein lists into interactive networks and offers suggestions for their modifications”
A tool to predict protein functions (Gene Ontology) via protein-protein and ontology networks
Dataset and package for working with protein-protein interactions in 3D
Fast AlphaFold-Multimer based pipeline for Protein-Protein Interaction (PPI) screening
The repository contains all the code for the paper amino acid encoding using deep learning application
A predictor of GPCR couplings with G-proteins/B-arrs using Transformers
Webpage of the HADDOCK group
Webpage of the Bonvinlab @ Utrecht University and HADDOCK software
Scipion framework plugin for the use of tools provided in the Rosetta software suite. Currently it has protocols for the use of the Rosetta DARC docking software
Bioinformatic tool to predict structural protein-protein interfaces
A web interface for ontology-based protein network visualization.
Code for ICML 2024 paper "Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning"
Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
Tools for processing and analysing PPI datasets
This package provides an basic implementation of the contact prediction network used in AlphaFold 1 for beginner, associated model weights and CASP13 dataset as used for CASP13 (2018) and published in Nature
Large scale, in silico interaction analyses of SARS-CoV-2 nucleocapsid protein variants against human cytokines.
Sequence based PPI prediction algorithm is developed using Xgboost. Around 73,000 positive and negative interacting protein pairs were extracted from Pan’s PPI dataset
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