/tools

PXDesignBench is a comprehensive evaluation suite for protein design that integrates various state-of-the-art models and standardized pipelines. It supports tasks such as monomer and binder design, allowing for thorough assessment of protein sequences and structures.

trRosetta2 provides deep learning models and scripts for predicting protein structures based on inter-residue geometries. It is designed to assist in the molecular design and optimization of protein structures, making it a valuable tool in computational biology.

Dense Homolog Retriever (DHR) is a tool designed for the ultra-fast detection of protein remote homologs using deep dense retrieval methods. It supports homolog retrieval benchmarks and includes functionalities for embedding and structure prediction of proteins.

RareFold is a tool for predicting the structures of single-chain proteins that incorporate rare noncanonical amino acids. It also enables the design of novel peptide binders through its EvoBindRare framework, allowing for flexible and diverse peptide design without prior knowledge of binding sites.

RGN2 is a reference implementation of a recurrent geometric network designed for predicting the 3D structures of orphan or de novo protein sequences. It utilizes deep learning techniques to enhance protein structure prediction, making it a valuable tool in the field of molecular biology.

AF3Score is a pipeline for evaluating the quality of protein structures using metrics derived from AlphaFold3. It generates various scoring metrics to assess the accuracy of protein models, focusing on both global and local structural features.

The 'arnie' repository provides a Python API for estimating and comparing RNA energetics across various secondary structure algorithms. It facilitates structure prediction and analysis of RNA sequences, making it a useful tool in molecular biology.

OVO is an open-source ecosystem designed for de novo protein design, integrating models, workflows, and data management. It provides a scalable platform for scaffold and binder design, utilizing advanced methods for protein structure generation and validation.

MMseqs2-App is a graphical interface for the MMseqs2 and Foldseek software suites, enabling users to search and annotate large sequence and structure datasets. It supports interactive data exploration and can be deployed on servers or workstations, facilitating molecular biology research.

AlphaFold provides an implementation of a model for predicting protein structures based on their amino acid sequences. It utilizes advanced machine learning techniques to generate accurate structural predictions, which are essential for understanding protein function and interactions.

The 'protein-ebm' repository provides a PyTorch implementation of energy-based models aimed at predicting protein conformations at atomic resolution. It includes training code, datasets, and pre-trained model weights, making it a valuable resource for researchers in molecular biology and computational chemistry.

ABCFold provides scripts to run AlphaFold3 and other related models for predicting protein structures using multiple sequence alignments and custom templates. It facilitates the generation of protein models and outputs useful visualizations for analysis.

MDANCE is a framework that enhances the analysis of molecular dynamics simulations through innovative clustering algorithms, enabling efficient processing of large datasets. It provides tools for identifying biologically relevant conformations and predicting native protein structures from simulation data.

nf-core/proteinfold is a bioinformatics pipeline designed for predicting the 3D structures of proteins using various methods such as AlphaFold2 and ESMFold. It facilitates the analysis of protein sequences and structures, making it a valuable tool for researchers in molecular biology and computational chemistry.

FoldBench is a benchmarking tool for all-atom biomolecular structure prediction, enabling the evaluation of models across various biomolecular interactions and monomeric structures. It includes a comprehensive dataset for proteins, nucleic acids, and ligands, facilitating assessments of prediction accuracy in molecular simulations.

LiteFold is a tool designed to make protein structure prediction more accessible by providing a lightweight server that runs state-of-the-art folding models. It allows researchers to submit prediction jobs and retrieve results easily, bridging the gap between complex computational tools and the scientific community.

This repository provides educational materials for a course on Structural Bioinformatics, including practical exercises on molecular dynamics simulations, protein structure prediction, and molecular docking. It serves as a resource for understanding and applying computational methods in molecular biology and bioinformatics.

DiffPack is a torsional diffusion model that predicts the conformation of protein side-chains based on their backbone structures. It utilizes a diffusion process to improve the accuracy of side-chain packing, making it a valuable tool for protein design.

ProteinDJ is a protein design pipeline that integrates multiple software packages to facilitate the design and optimization of protein structures. It allows users to generate new protein sequences and structures through various design modes, leveraging advanced modeling techniques like RFdiffusion and AlphaFold2.

AlphaRED is a pipeline designed for predicting protein complex structures from sequences, utilizing AlphaFold for structure prediction and ReplicaDock for protein-protein docking. It provides a comprehensive approach to analyze and refine docking results based on predicted binding modes.

tr-rosetta-pytorch is a PyTorch implementation of trRosetta and trDesign, designed for predicting and designing protein structures. It provides a convenient package for users to perform structure predictions using deep learning techniques.

ProteinEBM is an open-source tool that utilizes denoising score matching to approximate the energy landscape of protein structures based on their sequences. It supports applications in structure prediction, stability assessment, and molecular simulations, making it a valuable resource in computational biology.

MiniFold is a fast model designed for single chain protein structure prediction, leveraging a protein language model for improved speed and efficiency. It allows users to predict protein structures from FASTA files and includes training capabilities for model optimization.

ManyFold is a library that facilitates the training and validation of protein folding models, including AlphaFold and pLMFold. It allows users to generate and optimize protein structures using advanced machine learning techniques.