proteins

Standardized data set for machine learning of protein structure

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

A Python API for the RCSB Protein Data Bank (PDB)

A PyTorch framework for prediction of tertiary protein structure

Bio4j abstract model and general entry point to the project

MoleculeKit: Your favorite molecule manipulation kit

Protein-Level ASSembler (PLASS): sensitive and precise protein assembler

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

P2Rank: Protein-ligand binding site prediction tool based on machine learning. Stand-alone command line program / Java library for predicting ligand binding pockets from protein structure.

Python macromolecular parsing (with .pdb/.cif/.mmtf parsing and production)

Implementation and replication of ProGen, Language Modeling for Protein Generation, in Jax

Application to assign secondary structure to proteins

R client for the OmniPath web service

Cancer-dedicated gene set interpretation

A geometric deep learning pipeline for predicting protein interface contacts.

Fast, state-of-the-art ab initio prediction of protein secondary structure in 3 and 8 classes

PSIPRED Protein Secondary Structure Predictor

Recurrent Geometric Network in Pytorch

The official repo of the new modular BioExcel2 version of HADDOCK

Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large corpus of protein sequences.

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

The pmartR R package provides functionality for quality control, normalization, exploratory data analysis, and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data. This includes data transformation, specification of groups that are to be compared against each other, filtering of feature and/or samples, data normalization, and data summarization (correlation, PCA).

The Enhanced Database of Interacting Protein Structures for Interface Prediction

A Python package to calculate, visualize and analyze correlation maps of proteins.

State-of-the-art ab initio prediction of 1D protein structure annotations

A structure-based, alignment-free embedding approach for proteins. Can be used as input to machine learning algorithms.

Protein structural disorder prediction

A package for making MuE observation models in Edward2.