/tools

The Protein-Language-Models repository provides a systematic review of protein language models, covering their architectures, evaluation metrics, and relevant datasets. It also introduces tools for ongoing research in the field of protein modeling and analysis.

The Triangle Multiplicative module is a PyTorch implementation that efficiently mixes rows or columns of a 2D feature map. It is utilized in AlphaFold2 for predicting protein structures, contributing to advancements in protein design and molecular modeling.

The prtm repository provides a deep learning library for protein models, enabling tasks such as protein folding, inverse folding, and ligand docking. It aims to streamline workflows in protein design and structure prediction, making it a valuable tool in computational biology and drug discovery.

Rosetta-DL is a collection of deep-learning packages aimed at predicting and designing biomolecular structures, including proteins and antibodies. It includes tools for protein folding and sequence design, contributing to advancements in molecular biology and computational chemistry.

GCP-VQVAE is a model that converts protein tertiary structures into discrete tokens using vector-quantized variational autoencoders. It enables the generation and evaluation of protein structures, achieving state-of-the-art performance on various benchmarks.

ANARCII is a generalized language model designed for numbering antigen receptor sequences. It provides a web tool and installation options for users interested in protein design and analysis.

LatentDE is a tool for protein sequence design that utilizes a latent-based directed evolution approach. It employs a variational autoencoder to optimize biological functionalities by exploring high-fitness mutants in the latent space, making it suitable for protein engineering tasks.

The ESM-S repository provides a structure-informed protein language model that enhances the learning of protein representations by integrating structural information without requiring explicit protein structures. It is designed for tasks such as remote homology detection and function prediction in proteins.

The af_backprop repository contains modifications to AlphaFold that enable backpropagation through the model, facilitating advancements in protein design. It is associated with projects that enhance protein design accessibility and improve sequence alignment methods.

PyMOL Remote is a Python package that allows users to send commands and data to and from PyMOL, a molecular visualization tool. It enables remote procedure calls between a client and a PyMOL server, facilitating molecular design and analysis workflows.

ProtFlow is a Python package that facilitates the management of protein design workflows on computing clusters and local machines. It provides tools for configuring and running various protein design tools, making it a valuable resource for researchers in molecular biology.

PeptideDesign is a codebase for full-atom d-peptide co-design utilizing flow matching methods. It includes functionalities for training models to generate peptides based on binding pockets, making it a valuable tool for peptide design in molecular biology.

The 'alphafold_singularity' repository contains a Singularity recipe for running AlphaFold, a software that predicts protein structures. It facilitates the use of AlphaFold in high-performance computing environments, enabling researchers to perform protein folding simulations efficiently.

gcWGAN is a tool developed for de novo protein design using a guided conditional Wasserstein GAN. It leverages deep generative models to explore sequence-structure relationships and generate novel protein sequences based on given folds.

ConFit is a machine learning method that utilizes protein language models to learn the fitness landscape of proteins with limited experimental data. It employs a contrastive learning strategy to enhance predictions of protein-specific fitness, making it useful for applications in protein design and optimization.

Deep H3 Loop Prediction is a tool that utilizes a deep residual network architecture to predict probability distributions of inter-residue distances and angles specifically for CDR H3 loops in antibodies. It is designed to assist in the modeling and prediction of antibody structures based on sequence input.

GPDL is a deep learning framework designed for generating novel protein backbones based on specified motifs and sequences. It utilizes a protein language model to optimize the design process, making it a valuable tool for molecular design in protein engineering.

DeepRank-GNN-esm is a tool that utilizes graph neural networks to score protein-protein complexes, incorporating features from protein language models. It provides functionalities for generating embeddings and predicting interaction scores, making it useful for molecular design and analysis.

Finetune-ESM is a tool for scalable finetuning of protein language models, utilizing advanced training techniques to enhance the prediction of protein functions from sequences. It supports distributed training and reproducibility, making it suitable for bioinformatics applications.

RosettaDesign is a Python script that facilitates the design of proteins using fixed or flexible backbone methods in PyRosetta. It allows users to generate multiple protein structures and evaluate their designs through simulations.

ESM-Ezy is a tool designed for training and inference on protein sequences using a pre-trained model. It facilitates the retrieval and analysis of candidate sequences, making it relevant for applications in protein design and bioinformatics.

PDBench is a dataset and software package that evaluates fixed-backbone sequence design algorithms for proteins. It includes a benchmark set of protein structures and provides metrics for assessing the performance of various design models.

DeepCriticalLearning implements various deep learning methods for protein function prediction and classification. It provides tools for training models that can be used in the context of protein design and analysis.

vcmsa is a Python library designed for vector clustering of Multiple Sequence Alignments (MSA) using protein language models. It allows for the alignment of protein sets that have conserved functions or structures but poorly conserved sequences, making it useful for protein design and analysis.