/tools

ELASPIC2 is a tool designed to predict the effects of mutations on protein stability and binding affinity using pretrained neural networks. It provides various interfaces, including a web server, Python API, and command-line interface, making it accessible for users to analyze protein mutations effectively.

SpatialPPIv2 is a tool that enhances the prediction of protein-protein interactions by utilizing graph neural networks and large language models to embed sequence features and capture structural information. It supports inference using both protein structure files and sequence files, making it versatile for molecular biology applications.

EquiPNAS is a tool designed for predicting binding sites between proteins and nucleic acids (DNA/RNA) using equivariant deep graph neural networks. It provides methods for training and testing models specifically tailored for protein-DNA and protein-RNA interactions.

This repository implements a method for aggregating residue-level embeddings from protein language models using optimal transport. It aims to improve the representation of protein sequences for various prediction tasks, such as drug-target interactions and protein-protein interactions.

The 'Viral Protein Function prediction using Protein Language Model (VPF-PLM)' repository provides a method for predicting the functional categories of viral proteins based on their sequences. It utilizes a classifier built on protein language models to enhance the annotation of prokaryotic viral proteins.

PPLM is a protein-protein language model designed to predict interactions, binding affinities, and contact residues between proteins. It utilizes a novel attention mechanism to enhance the accuracy of these predictions, making it a valuable tool in molecular biology and computational chemistry.

EvoMIL is a deep learning method designed to predict virus-host associations at the species level using protein sequences. It leverages a pre-trained protein language model and attention-based multiple instance learning to enhance prediction accuracy for both prokaryotic and eukaryotic hosts.

ProteomeLM is a transformer-based language model that predicts protein-protein interactions and gene essentiality across various taxa. It processes entire proteomes, capturing inter-protein dependencies and enabling fast interaction analysis.

PRODIGY-LIG is a tool designed for predicting the binding affinities of protein-small molecule complexes. It utilizes a structure-based approach to calculate binding energy, making it useful for drug discovery and molecular interactions.

Genos is a collection of foundation models designed for genomic sequence analysis, capable of handling long sequences and providing insights into gene elements and regulatory interactions. It supports various genomic tasks, including mutation prediction and cross-species classification, making it a valuable tool for researchers in molecular biology.

ProtHGT is a model designed for predicting protein functions by integrating diverse biological datasets into a knowledge graph. It utilizes a heterogeneous graph transformer architecture to learn complex relationships and make accurate predictions across various Gene Ontology categories.

STCRpy is a software suite designed for analyzing and processing T-cell receptor (TCR) structures. It provides tools for interaction profiling, geometry calculations, and generating datasets compatible with machine learning frameworks, making it useful for researchers in molecular biology and immunology.

Longbow automates the submission and monitoring of molecular simulations on remote high-performance computing machines. It supports various bio-molecular simulation software and job schedulers, making it easier for researchers to run complex simulations efficiently.

Pose is a Python library that allows users to construct and manipulate protein molecular structures, including building polypeptides from sequences and performing various structural manipulations. It provides functionalities for analyzing molecular properties such as potential energy and radius of gyration, making it a valuable tool for protein design and molecular simulations.

PHIEmbed is a tool designed for predicting phage-host interactions by utilizing protein language models to represent receptor-binding proteins. It improves upon traditional methods by automating feature extraction and enhancing prediction accuracy through the use of embeddings.

Prospr is a toolbox designed for protein structure prediction using the HP-model. It includes various prediction algorithms, a protein data structure for simulating folding, and datasets for research in protein folding and structure analysis.

This repository provides an introductory workshop on protein language models, exploring the theoretical and practical aspects of protein embeddings and their applications in protein-related machine learning tasks.

The Phylogeny-MSA-Transformer repository provides tools for training protein language models on multiple sequence alignments to learn phylogenetic relationships. It includes scripts and requirements for analyzing protein sequences and generating phylogenetic insights.

protPy is a Python package designed to calculate a variety of physicochemical, biochemical, and structural descriptors for proteins. It utilizes sequence-derived features of amino acids to aid in applications such as protein engineering and predicting protein structure and function.

VenusX is a benchmark tool designed for fine-grained functional annotation of proteins, focusing on tasks such as residue-level classification and fragment-level classification. It includes a comprehensive dataset with over 878,000 samples, facilitating the evaluation of protein models and their functional understanding.

WatCon is a Python tool that analyzes conserved water networks in both static structures and dynamic trajectories of proteins. It facilitates the visualization of these networks and their interactions across protein families, contributing to the understanding of molecular interactions.

AlphaCutter is a Python tool that efficiently removes non-globular regions from predicted protein structures. It utilizes parameters to define and filter out specific structural features, aiding in the refinement of protein models.

SCEPTR is a transformer-based model designed for T cell receptor (TCR) representation, enabling alignment-free analysis and prediction of TCR-pMHC interactions. It outperforms traditional methods in TCR specificity prediction, making it a valuable tool in immunoinformatics.

CATHe is a deep learning tool that utilizes protein sequence embeddings to detect remote homologues for CATH superfamilies. It achieves high accuracy in predicting protein classifications, making it a valuable resource for researchers in molecular biology and bioinformatics.