/tools

AWS RoseTTAFold is a tool that facilitates the prediction of protein structures using the RoseTTAFold algorithm on AWS Batch. It includes Jupyter notebooks for submitting protein sequences for analysis, leveraging high-performance computing resources to optimize the prediction process.

Venus-MAXWELL is a tool that efficiently learns the stability landscapes of protein mutations using advanced protein language models. It allows users to generate predictions for the effects of single mutations on protein stability, making it relevant for applications in protein design and stability analysis.

AMix-1 is a protein foundation model that employs a test-time scalable approach to generate and evaluate protein sequences. It allows for the iterative evolution of protein designs through a systematic evaluation of various metrics, making it a valuable tool for protein engineering.

The 'protein-uq' repository provides tools for benchmarking uncertainty quantification methods in protein engineering. It includes functionalities for training models that predict protein fitness and function, utilizing active learning and Bayesian optimization techniques.

SLIP is a sandbox environment designed for engineering protein sequences through synthetic fitness functions. It allows users to create and manipulate landscapes for protein design, making it a valuable tool in computational biology.

The 'antibody-dl' repository is a collection of platforms, tools, and resources aimed at enhancing antibody engineering. It includes deep learning models for antibody design, structural prediction, and various databases that support antibody research and development.

The PTM Parameterization Prototype allows users to parameterize proteins with post-translational modifications and simulate them using the OpenFF Rosemary force field. It provides a Jupyter Notebook that demonstrates how to handle both canonical and non-canonical amino acids in protein simulations.

OPUS-Fold is an open-source framework designed for protein folding based on torsion-angle sampling. It integrates various methods for protein structure prediction and allows for efficient modeling of protein side chains during the folding process.

The mlx-esm repository provides an implementation of Meta's ESM-1 protein language model using the MLX library. It allows users to generate new protein sequences and predict masked amino acids, contributing to protein design and structure prediction in molecular biology.

PMGen is a comprehensive pipeline designed for predicting peptide-MHC complex structures and optimizing peptide sequences. It utilizes advanced techniques like AlphaFold for structure prediction and includes features for iterative peptide optimization and mutation screening.

iPPIGAN is a tool for de novo molecular design that utilizes deep molecular generative models to create inhibitors targeting protein-protein interactions. It includes functionalities for training models and generating novel compounds, contributing to drug discovery efforts.

MutaPLM is a tool for protein language modeling that aids in understanding and engineering mutations in proteins. It utilizes pre-training and fine-tuning on specific datasets to enhance its capabilities in mutation explanation and engineering tasks.

PoET-2 is a multimodal protein language model designed for generating protein sequences and predicting the effects of protein variants. It utilizes retrieval-augmented techniques to enhance its predictive capabilities, making it a valuable tool in protein design and bioinformatics.

PPI-Miner is a pipeline for searching protein-protein interactions based on specific structural and sequence motifs. It allows users to mine potential PPI partners and optimize protein structures, facilitating protein design and interaction studies.

HoloBench is a benchmarking tool for discrete sequence optimization algorithms, specifically designed for biophysical applications. It allows users to optimize sequences using evolutionary strategies, making it relevant for protein design and optimization tasks.

This repository provides tutorials and pipelines for the computational design of metallohydrolases using the RFdiffusion2 model. It includes both dry lab data and wet lab data, facilitating the design and testing of enzyme sequences.

BC-Design is a framework designed for high-precision inverse protein folding, integrating structural and biochemical features to enhance protein design accuracy. It utilizes a dual-encoder architecture to generate amino acid sequences that correspond to specific 3D protein structures, making it valuable for protein engineering and drug development.

CCMgen and CCMpredPy provide a Python toolkit for generating realistic synthetic protein sequences using second-order Markov Random Field models. The tools allow for the sampling of protein-like sequences while adhering to evolutionary pressures, making them useful for protein design and analysis.

Joint_Model_Stability is a toolkit for protein design that utilizes joint TrROS/TrMRF models for zero-shot stability prediction. It includes functionalities for designing protein sequences and structures, leveraging pre-trained models like ESMFold and ProteinMPNN.

STARLING is a tool for predicting coarse-grained ensembles of intrinsically disordered proteins from their sequences using a generative model. It allows for the generation of multiple conformations and provides functionalities for analyzing and converting ensemble data.

SCISOR is a diffusion model that generates shrunken protein sequences by learning from evolutionary data. It aims to optimize protein design by suggesting deletions that preserve functional motifs, making it a valuable tool in molecular biology.

Bridge-IF is an implementation of a model for learning inverse protein folding using Markov Bridges. It utilizes datasets related to protein structures to train and evaluate the model for generating protein sequences based on their structures.

The pmpnndiff repository provides an implementation of discrete diffusion for inverse protein folding, including pre-trained models and training routines. It aims to facilitate protein folding predictions and experiments, making it a valuable tool in molecular design.

The Interactive Mutation Browser is a tool that utilizes protein language models to facilitate interactive exploration of protein mutations. It aims to enhance the understanding and application of protein structures through innovative interactions with large language models.