/tools

PRODIGY is a tool designed to predict the binding affinity of protein-protein complexes based on structural data. It allows users to analyze single or multiple structures and provides detailed output on intermolecular contacts and predicted affinities.

AF2Complex is a tool designed to predict and model protein complexes using AlphaFold deep learning models. It enhances the capabilities of AlphaFold to accurately predict protein-protein interactions and supports various features for complex modeling and evaluation.

pVACtools is a computational toolkit that aids in the identification and prioritization of neoantigens from genomic data, facilitating the design of personalized cancer vaccines. It includes various tools for analyzing genetic variants and their implications in immunotherapy.

SaprotHub is a platform that democratizes protein language model training and sharing, enabling biologists to fine-tune and utilize protein models without requiring advanced machine learning expertise. It supports various protein prediction tasks, facilitating collaboration and resource sharing within the protein research community.

PENSA is a Python library designed for the exploratory analysis and comparison of biomolecular conformational ensembles, particularly from molecular dynamics simulations. It offers functionalities such as structural ensemble comparison, clustering, and trajectory processing, making it a valuable tool for researchers in molecular biology and biophysics.

FABind is a software tool designed for fast and accurate prediction of protein-ligand binding interactions. It includes enhancements for molecular docking through improved pocket prediction and pose generation, making it useful for drug discovery and molecular simulations.

SIRIUS is a software framework that facilitates the de-novo identification of metabolites using tandem mass spectrometry. It automates the analysis of mass spectra to deduce molecular formulas and structures, integrating various tools for enhanced metabolite identification.

PANDORA is a research platform designed for high-dimensional data analysis in biomedical research, facilitating predictive modeling and biomarker discovery. It leverages advanced statistical methodologies to provide insights into systems biology and drug discovery.

MSA Pairformer is a tool that extracts evolutionary signals from aligned homologous sequences to predict residue-residue interactions in proteins. It provides functionalities for embedding protein sequences and predicting contacts, making it useful for applications in protein design and analysis.

This repository provides a PyMOL extension that allows users to color protein structures derived from the AlphaFold Protein Structure Database according to their confidence levels (pLDDT). It enhances the visualization of protein structures, aiding in the analysis of structural biology data.

The 'galaxytools' repository contains a collection of Galaxy Tool wrappers that facilitate the integration of cheminformatics and RNA bioinformatics tools into the Galaxy platform, enabling researchers to utilize these tools for molecular analysis and data processing.

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.

Folddisco is a tool for efficiently searching and indexing discontinuous motifs in protein structures across large-scale databases. It allows users to query protein structures for specific motifs, enabling insights into protein function and structure relationships.

Awesome-Molecular-Docking is a curated list of resources aimed at solving molecular docking and related tasks. It includes software for docking, datasets, and references to molecular dynamics simulations, making it a valuable tool for researchers in drug discovery and molecular biology.

APPT is a state-of-the-art model designed to predict protein-protein binding affinity using transformer architectures and the ESM protein language model. It supports drug discovery and protein engineering by providing precise predictions based on protein sequence pairs.

The ProSE repository provides multi-task and masked language model-based protein sequence embedding models. It allows users to train models and embed protein sequences, facilitating research in protein structure and function analysis.

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.

PPIRef is a Python package designed for working with 3D structures of protein-protein interactions. It includes functionalities for extracting, visualizing, and analyzing protein-protein interfaces, as well as providing a dataset for machine learning applications in this domain.

ProtST is a pretraining framework designed for understanding and predicting protein sequences by integrating protein functions with biomedical texts. It supports various downstream tasks such as protein localization prediction and zero-shot protein classification, enhancing the capabilities of protein language models.

ProtFlash is a lightweight protein language model designed to generate embeddings for protein sequences. It utilizes pretrained models to facilitate protein representation learning, which can aid in various bioinformatics applications.

Evolocity is a Python package that utilizes evolutionary velocity to analyze protein evolution through language models. It provides tools for predicting evolutionary dynamics and includes benchmarking scripts for performance evaluation.

SumGNN is a tool designed for predicting multi-typed drug interactions by utilizing knowledge graph summarization techniques. It provides datasets and models for drug-drug interaction prediction, making it a valuable resource in the field of bioinformatics and drug discovery.

TypeDB Bio is an open-source biomedical knowledge graph designed to facilitate research in drug discovery, precision medicine, and drug repurposing. It allows researchers to query interconnected biomedical data, helping to identify potential drug targets and understand biological interactions.

gLM is a genomic language model designed to predict protein co-regulation and function. It provides tools for computing embeddings of protein sequences and allows users to train the model on custom datasets for various protein-related analyses.