/tools

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.

Boltz-sample is a tool that extends the Boltz-2 framework to enhance conformational sampling of proteins by scaling the latent pair representation. It allows users to explore alternative protein conformations and evaluate predictions against reference structures, making it useful for protein structure prediction and molecular simulations.

The PS4 Dataset is the largest open-source dataset for predicting protein single sequence secondary structure. It includes methods for validation and evaluation of secondary structure prediction models, making it a valuable resource for researchers in protein structure prediction.

AlphaFold Analyser is a command line tool designed for rapid visualization of predictions made by AlphaFold2 and AlphaFold3. It generates publication-quality plots for predicted aligned error (PAE) and pLDDT scores, as well as creating PyMol sessions for structural analysis.

ESMBind is a deep learning and physics-based workflow designed to predict metal-binding proteins and generate their 3D structures with bound metal ions. It integrates evolutionary scale modeling for residue-level predictions and physics-based modeling for detailed structural generation.

The alphascreen tool facilitates the screening of protein interactions by generating input files for AlphaFold predictions and analyzing the results. It automates the process of fetching sequences, fragmenting them, and interpreting the predictions to identify potential interactions between proteins.

The CB513_dataset repository contains datasets for predicting protein secondary structures, specifically designed for use in deep learning models. It includes filtered datasets that facilitate training and evaluation of models aimed at understanding protein structures.

The CASP15 repository benchmarks various state-of-the-art protein structure prediction methods, including AlphaFold2 and RoseTTAFold. It provides generated protein structures and metrics for evaluating their predictive performance, making it a valuable resource for researchers in molecular biology and computational chemistry.

MCTS-RNA is a computational tool that solves the RNA inverse folding problem using Monte Carlo Tree Search. It allows for the design of nested and pseudoknot RNA structures while controlling the GC-content and its deviation precisely.

AIRFold is a protein structure prediction system built on AlphaFold2, providing scalable solutions for predicting protein structures through various advanced models. It integrates modules for co-evolutionary information extraction and offers a user-friendly interface for researchers in the life sciences.

RosettaAbinitio is a Bash script designed to automate the process of running Rosetta Abinitio folding simulations on high-performance computing systems. It facilitates the submission of jobs for generating and clustering protein decoys, making it a useful tool for protein structure prediction.

This repository contains implementations for protein structure prediction using various optimization techniques, including molecular dynamics simulations and genetic algorithms. It aims to explore different approaches to improve the accuracy and efficiency of predicting protein folding.

This repository contains source codes for a balance-evolution artificial bee colony algorithm aimed at optimizing protein structures. It utilizes a three-dimensional off-lattice model to enhance protein structure prediction and optimization processes.

GNN_UNet is a tool for multi-scale protein structure modeling that utilizes geometric graph U-Nets to capture complex protein interactions. It provides a framework for training models that can predict protein structures, making it relevant for computational biology and molecular modeling.

FAPEloss is a Python implementation of the FAPE loss function used in the AlphaFold algorithm for predicting protein structures. It provides a framework for testing and optimizing the loss function, which is crucial for accurate protein design.

proteinviz is an open-source tool that predicts the 3D structure of proteins based on their amino acid sequences. It utilizes a protein folding model to generate PDB files and visualize the protein structures in a user-friendly interface.